# Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years

Jingfeng Xiao, Frédéric Chevallier, Cécile Gomez, Luis Guanter, Jeffrey A. Hicke, Alfredo R. Huete, Kazuhito Ichii, Wenjian Ni, Yong Pang, Abdullah F. Rahman, et al.

► To cite this version:

Jingfeng Xiao, Frédéric Chevallier, Cécile Gomez, Luis Guanter, Jeffrey A. Hicke, et al. Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years. Remote Sensing of Environment, 2019, 233, 37 p. 10.1016/j.rse.2019.111383. hal-02628929

HAL Id: hal-02628929

https://hal.inrae.fr/hal-02628929v1

Submitted on 11 Aug 2025

HAL is a multi-disciplinary open access
archive for the deposit and dissemination of sci-
entific research documents, whether they are pub-
lished or not. The documents may come from
teaching and research institutions in France or
abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est
destinée au dépôt et à la diffusion de documents
scientifiques de niveau recherche, publiés ou non,
émanant des établissements d'enseignement et de
recherche français ou étrangers, des laboratoires
publics ou privés.

<figure><img src="image_1.png" /></figure>

Distributed under a Creative Commons Attribution 4.0 International License
1 Remote sensing of the terrestrial carbon cycle: A review on 50 years of advances

2 Jingfeng Xiao¹*, Frederic Chevallier², Cecile Gomez³, Luis Guanter⁴, Jeffrey A. Hicke⁵, Alfredo

3 R. Huete⁶, Kazuhito Ichii⁷, Wenjian Ni⁸, Yong Pang⁹, Abdullah F. Rahman¹⁰, Guoqing Sun¹¹,

4 Wenping Yuan¹², Li Zhang¹³, Xiaoyang Zhang¹⁴

5 ¹Earth Systems Research Center, Institute for the Study of Earth, Oceans, and Space, University
6 of New Hampshire, Durham, NH 03824, USA; ²Laboratoire des Sciences du Climat et de
7 l'Environnement, CEA, CNRS, UVSQ/ IPSL, Université Paris-Saclay, France ; ³LISAH, Univ
8 Montpellier, IRD, INRA, SupAgro, F-34060 Montpellier, France; ⁴Centro de Tecnologías
9 Físicas, Universitat Politècnica de València, Camí de Vera s/n, 46022 València, Spain;
10 ⁵Department of Geography, University of Idaho, Moscow, ID 83844, USA; ⁶Climate Change
11 Cluster, University of Technology Sydney, NSW 2007, Australia; ⁷Center for Environmental
12 Remote Sensing, Chiba University, Chiba, Japan; ⁸State Key Laboratory of Remote Sensing
13 Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing
14 100094, China; ⁹Institute of Forest Resource Information Technique, Chinese Academy of
15 Forestry, Beijing 100091, China; ¹⁰Coastal Studies Lab, University of Texas Rio Grande Valley,
16 TX 78597, USA; ¹¹Department of Geographical Sciences, University of Maryland, College Park,
17 MD 20740, USA; ¹²School of Atmospheric Sciences, Sun Yat-Sen University, Guangzhou
18 519082, China; ¹³Key Laboratory of Digital Earth Science, Institute of Remote Sensing and
19 Digital Earth (RADI), Chinese Academy of Sciences, Beijing 100094, China; ¹⁴Geospatial
20 Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA

21

22 *Corresponding author: j.xiao@unh.edu

23
**Abstract** Quantifying ecosystem carbon fluxes and stocks is essential for better understanding the global carbon cycle and improving projections of the carbon-climate feedbacks. Remote sensing has played a vital role in this endeavor during the last five decades by quantifying carbon fluxes and stocks. The availability of satellite observations of the land surface since the 1970s, particularly the early 1980s, has made it feasible to quantify ecosystem carbon fluxes and stocks at regional to global scales. Here we provide a review of the advances in remote sensing of the terrestrial carbon cycle from the early 1970s to present. First, we present an overview of the terrestrial carbon cycle and remote sensing of carbon fluxes and stocks. Remote sensing data acquired in a broad wavelength range (visible, infrared, and microwave) of the electromagnetic spectrum have been used to estimate carbon fluxes and/or stocks. Second, we provide a historical overview of the key milestones in remote sensing of the terrestrial carbon cycle. Third, we review the platforms/sensors, methods, findings, and challenges in remote sensing of carbon fluxes. The remote sensing data and techniques used to quantify carbon fluxes include vegetation indices, light use efficiency models, terrestrial biosphere models, data-driven (or machine learning) approaches, solar-induced chlorophyll fluorescence, land surface temperature, and atmospheric inversions. Fourth, we review the platforms/sensors, methods, findings, and challenges in passive optical, microwave, and lidar remote sensing of biomass carbon stocks as well as remote sensing of carbon stocks in soils. Fifth, we review the progresses in remote sensing of disturbance impacts on the carbon cycle. Sixth, we also discuss the uncertainty and validation of the resulting carbon flux and stock estimates. Finally, we offer a forward-looking perspective and insights for future research and directions in remote sensing of the terrestrial carbon cycle. Remote sensing is anticipated to play an increasingly important role in carbon cycling studies in the future. This comprehensive and insightful review on 50 years of remote
sensing of the terrestrial carbon cycle is timely and valuable and can benefit scientists in various research communities (e.g., carbon cycle, remote sensing, climate change, ecology) and inform ecosystem and carbon management, carbon-climate projections, and climate policymaking.

**Keywords:** Carbon fluxes; carbon stocks; optical remote sensing; microwave remote sensing; lidar; aboveground biomass; soil organic carbon; solar-induced chlorophyll fluorescence; terrestrial ecosystems; Landsat; AVHRR; MODIS; Sentinel; OCO-2; Synthetic Aperture Radar; carbon cycling; vegetation indices; light use efficiency; disturbance; carbon-climate feedbacks

# 1. Introduction

Terrestrial ecosystems “breathe” in carbon dioxide (CO₂) through photosynthesis and release carbon (C) into the atmosphere through respiration and therefore play an important role in the global C cycle and the Earth’s climate (Schimel 1995). The terrestrial C cycle has received tremendous attention from the research community during the last several decades. Remote sensing has been widely used to study the terrestrial C cycle by quantifying ecosystem C fluxes and stocks (e.g., Running et al. 2004; Saatchi et al. 2011), examining the impacts of global change on C dynamics (e.g., Nemani et al. 2003; Smith et al. 2016; Li et al. 2018a), and the feedbacks to the climate (e.g., Ollinger et al. 2008; Xiao 2014). The availability of satellite observations of the land surface since the 1970s, particularly from the early 1980s, has made it feasible to assess the magnitude, spatial patterns, interannual variability, and long-term trends of ecosystem C dynamics at landscape, regional, and global scales.

Ecosystem C fluxes have been widely measured or quantified using remotely sensed data. For example, satellite-derived vegetation indices (VIs) (e.g., normalized difference vegetation index or NDVI, enhanced vegetation index or EVI) are traditionally used to approximate or
estimate gross primary production (GPP) directly (e.g., Zhou et al. 2001; Xiao and Moody 2004; Rahman et al. 2005), and are also used in light use efficiency (LUE) models for quantifying GPP and net primary production (NPP) (Potter et al. 1993; Running et al. 2004). The recent advent of solar-induced chlorophyll fluorescence (SIF) has offered an innovative and independent approach for estimating GPP (Frankenberg et al. 2011; Joiner et al. 2011; Li et al. 2018b). In addition, satellite-derived land surface temperature (LST) has also been used to quantify ecosystem respiration (ER) (e.g., Rahman et al. 2005). Moreover, the measurement of atmospheric column CO₂ concentrations from the Greenhouse gases Observing SATellite (GOSAT) and the Orbiting Carbon Observatory-2 (OCO-2) allows the estimation of net biome production (NBP) (Kondo et al. 2015). Besides directly measuring C fluxes, remote sensing has also been used to estimate leaf area index (LAI) that is in turn used to prescribe LAI in diagnostic ecosystem models (Liu et al. 2018b) or to be assimilated into prognostic models (Hazarika et al. 2005) for simulating ecosystem C fluxes.

Both optical and microwave remote sensing data have been widely used to quantify and monitor C stocks during the last few decades. For example, surface reflectance and VIs derived from optical sensors have been used to quantify biomass using empirical models (Myneni et al. 2001), allometric equations (Zhang and Kondragunta 2006), or machine learning approaches (John et al. 2018). Active microwave remote sensing has also been traditionally used to estimate biomass (Le Toan et al. 1992; Ni et al. 2016). Lidar (light detection and ranging) has emerged as an effective technique for quantifying aboveground biomass (AGB) for forests and other ecosystems and has been increasingly used over the last two decades (Lefsky et al. 1999; Lefsky et al. 2005). Besides biomass C stocks, soil organic C (SOC) has also been estimated using
92 remotely sensed data using empirical or machine learning approaches (Gomez et al. 2008;
93 Mishra et al. 2010; Chen, Chang, Xiao et al. 2019).

94 During the last five decades, a number of airborne and satellite sensors have observed the
95 Earth's land surface, and a wide variety of techniques have been developed to quantify C fluxes
96 and stocks using remotely sensed data. Several previous studies have reviewed the remote
97 sensing of C fluxes or stocks (e.g., Roughgarden et al. 1991; Hilker et al. 2008; Ladoni et al.
98 2010; Goetz and Dubayah 2011; Schimel et al. 2015; Ryu et al. 2019; Mohammed et al. 2019).
99 For example, Hilker et al. (2008) reviewed the status and future directions of simulating GPP via
100 the use of remote sensing in LUE models. Turner et al. (2004) reviewed the integration of
101 remotely sensed data (e.g., vegetation type, stand age, biomass, phenology, LAI, and tree height)
102 with ecosystem process models for regional assessment of C fluxes. Ladoni et al. (2010)
103 reviewed the potential and limitations of remotely sensed data for mapping SOC. Despite these
104 review and overview efforts, to our knowledge, no study has systematically reviewed the remote
105 sensing of the terrestrial C cycle over the last 50 years. Moreover, the last decade has witnessed
106 more rapid advances in remote sensing and its extensive use for quantifying C fluxes and stocks.
107 A comprehensive review on the history and advances in remote sensing of the terrestrial C cycle
108 will be timely and useful.

109 Here we provide a review on 50 years of advances in remote sensing of the terrestrial C cycle.
110 We conducted a comprehensive review of the achievements, challenges, and opportunities of
111 remote sensing science and technology in terrestrial C cycle studies from the 1970s to present.
112 Section 2 presents an overview of the terrestrial C cycle and remote sensing of C fluxes and
113 stocks based on a broad wavelength region (visible, infrared, and microwave) on the
114 electromagnetic spectrum. In Section 3, we provide a historical overview of the main milestones
in remote sensing of the terrestrial C cycle. In Section 4, we review the platforms/sensors, methods, achievements, and challenges for quantifying ecosystem C fluxes. The remotely sensed data and techniques used for quantifying C fluxes include vegetation indices, LUE models, terrestrial biosphere models, data-driven (or machine learning) approaches, SIF, LST, and atmospheric inversions. In Section 5, we review the platforms/sensors, methods, achievements, and challenges in passive optical, microwave, and lidar remote sensing of biomass C stocks and also in remote sensing of C stocks in soils. In Section 6, we review the methods and challenges for assessing the impacts of disturbances on the terrestrial C cycle. In Section 7, we discuss the uncertainty and validation of the resulting C fluxes and stock estimates. Finally, we offer a forward looking perspective and insights for future research and directions in the terrestrial C cycle area of environmental remote sensing in Section 8.

126

127

## 2. Overview of the terrestrial carbon cycle and remote sensing

The terrestrial C cycle encompasses the exchange of C among the terrestrial biosphere, pedosphere, geosphere, and atmosphere of the Earth. Plants absorb C from the atmosphere through photosynthesis and store the C in biomass (leaves, branches, trunks, and roots). GPP, the amount of C fixed by terrestrial ecosystems through photosynthesis, constitutes the largest C flux between the terrestrial biosphere and the atmosphere. GPP is the basis for the production of food, wood, and fiber, and therefore has important implications for human welfare. A part of the C absorbed is returned to the atmosphere through plant respiration (i.e., autotrophic respiration), and the difference between GPP and autotrophic respiration is termed as NPP. Litterfall, plant materials that fall to the ground (leaves, branches, flowers, and fruits), contributes to the buildup of the soil C pool. The C input from litterfall and root mortality/exudation and the C release from

128

129

130

131

132

133

134

135

136

137
decomposition (i.e., heterotrophic respiration) determine the size of the SOC pool (Liu et al. 2011). GPP along with ER, the sum of autotrophic and heterotrophic respiration, determine the net ecosystem production (NEP). Other processes such as deforestation, harvest, and fire can also lead to the loss of C, and the net ecosystem C balance is NBP. Disturbances are critical ecosystem processes that influence C cycle dynamics. Direct emissions from one disturbance type, wildfire, transfer C from ecosystems to the atmosphere immediately (while burning). Major impacts to GPP and respiration can occur following fires as well as other disturbance types, which include natural disturbances (insect and disease outbreaks, drought, and severe storms) and harvesting, and these impacts can last for decades as ecosystems recover (Odum 1969). The C fluxes and stocks that are quantified using remote sensing data and methods are illustrated in **Figure 1**. Better understanding the terrestrial C cycle has important implications for climate projections. Remote sensing has played a critical role in quantifying C fluxes (e.g., GPP, NPP, ER, NEP, NBP) (Running et al. 2004; Xiao et al. 2011a) and stocks (biomass and SOC) (Saatchi et al. 2011) at various spatial and temporal scales during the last five decades.
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Carbon (C) fluxes and stocks that are quantified using remote sensing data and methods. The C fluxes that are estimated by remote sensing include gross primary production (GPP), ecosystem respiration (ER), net primary production (NPP), net ecosystem production (NEP), and net biome production (NBP). The C stocks that are quantified by remote sensing consist of aboveground biomass (AGB) and soil organic C (SOC). Fluxes contributing to NPP are GPP and autotrophic respiration (AR) (NPP=GPP-AR). Fluxes contributing to NEP are GPP, AR, and heterotrophic respiration (HR) (NEP = NPP-HR). The net ecosystem C balance, NBP, is mainly determined by NEP minus the loss of C by processes such as fire and harvest.</figcaption></figure>

Ecosystem C fluxes and stocks are quantified with remotely sensed data acquired in the broad wavelength range of the electromagnetic spectrum from visible light to microwave (Figure 2). Ecosystem C flux components (e.g., GPP, NPP, ER, NEP, and NBP) are typically estimated using optical remote sensing data acquired in the visible, near-infrared (NIR), and
shortwave infrared (SWIR) wavelength. ER can also be estimated using LST observations from
the thermal infrared (TIR) wavelength (Rahman et al. 2005; Kimball et al. 2009). Satellite-
derived VIs (Rahman et al. 2005), LUE models (Running et al. 2004), and SIF (red and far red
wavelength) (Li et al. 2018c) have been used to estimate GPP. Remotely sensed data have also
been integrated with machine learning approaches (Xiao et al. 2008; Jung et al. 2009; Xiao et al.
2014a) and process-based models (Hazarika et al. 2005; Liu et al. 2018b) for quantifying C
fluxes (e.g., GPP, NPP, ER, NEP, and NBP). NBP can also be estimated from total column CO2
concentrations retrieved mainly in the SWIR wavelength (Rayner and O'Brien 2001). Both
optical (Zhang and Kondragunta 2006; Blackard et al. 2008) and microwave (Dobson et al. 1992;
Le Toan et al. 1992) remote sensing have been used to estimate biomass with allometric
equations and/or empirical approaches. Optical remote sensing data have also been used to
quantify SOC with empirical and machine learning approaches (Ben-Dor et al. 2002; Gomez et
al. 2008). Figure 2 illustrates the electromagnetic spectrum and the remote sensing of the
terrestrial C cycle.
<figure><img src="image_3.png" /><figcaption>**Figure 2.** The electromagnetic spectrum and the remote sensing of the terrestrial carbon (C) cycle. The C flux components typically consist of gross primary productivity (GPP), net primary productivity (NPP), ecosystem respiration (ER), net ecosystem productivity (NEP), and net biome productivity (NBP). The C stocks consist of biomass and soil organic C (SOC). NIR, SWIR, and TIR stand for near-infrared, shortwave infrared, and thermal infrared wavelength, respectively, while MLA stands for machine learning approaches.</figcaption></figure>

**3. Milestones in remote sensing of the terrestrial carbon cycle**

The development of the terrestrial C cycle area of environmental remote sensing during the last five decades is characterized by a series of milestones (Figure 3). The main milestones are briefly described below.
<figure><img src="image_4.png" /><figcaption>**Figure 3.** Milestones in remote sensing of the terrestrial C cycle.</figcaption></figure>

**3.1. First Earth observing satellite and longest record of Earth observations – the Landsat archive**

Remote sensing of the terrestrial C cycle mainly started from the launch of the first Landsat satellite (Landsat 1) on July 23, 1972. The launch of Landsat 1 made the global land monitoring possible with optical measurements for the first time. The Landsat series of Earth-observing satellites (Landsat 1-8), co-managed by U.S. Geological Survey (USGS) and National
Aeronautics and Space Administration (NASA), provides the longest continuous observations of the Earth's surface from space (Wulder et al. 2016). Imagery from the Landsat series of satellites (Landsat 1-8) has been used to quantify C fluxes and stocks at regional to global scales (Foody et al. 2003; Masek and Collatz 2006). The Landsat archive now includes almost 50 years of observations globally, providing opportunities for investigating regional or global ecosystem C dynamics at multidecadal scales.

## 3.2. Start of global continuous vegetation observations and longest NDVI record – the AVHRR record

The advanced very high resolution radiometer (AVHRR), an instrument on board the National Oceanic and Atmospheric Administration's (NOAA) polar-orbiting meteorological satellites (NOAA 7, 9, 11, 14, 16-19), provides spatially and temporally continuous observations of global vegetation from 1981 to present (Pinzon and Tucker 2014). The AVHRR provides the longest, continuous surface reflectance and NDVI records globally at the spatial resolution of 8 km. The availability of the AVHRR observations since 1981 has made it feasible to monitor photosynthetic activity and GPP seamlessly over the globe. The most widely used AVHRR-derived NDVI records are perhaps the Global Inventory Monitoring and Modeling Studies (GIMMS) NDVI dataset and the GIMMS3g product (Pinzon and Tucker 2014). Other widely used AVHRR NDVI records include the Pathfinder NDVI dataset (James and Kalluri 1994) and AVHRR long term data record (LTDR) (Pedelty et al. 2007). The 30+ year AVHRR record allows the research community to examine C fluxes and/or stocks globally at annual and decadal times.

## 3.3. Mapping forest biomass using synthetic aperture radar (SAR) imagery
The availability of SIR-C/X-SAR and ERS-1/2 SAR imagery since the early 1990s greatly facilitated the monitoring of forest biomass using SAR data (Dobson et al. 1992; Le Toan et al. 1992). They advanced studies of SAR interferometry on the estimation of forest AGB. The JERS-1, PALSAR, and PALSAR2 provide multi-year global coverage of L-band SAR data which enabled more studies on forest biomass estimation from SAR data. In addition, PolInSAR and TomoSAR provide a new technique for estimating AGB by detecting the 3D structure of forests. Microwave signals can penetrate clouds and therefore SAR can collect data continuously in all weather conditions, allowing the estimation of biomass in large regions including areas often covered by clouds.

## 3.4. Start of the MODIS era and the first operational global GPP/NPP product

The MODerate resolution Imaging Spectroradiometer (MODIS) on board two key satellites of NASA's Earth Observing System (EOS) - Terra and Aqua provides observations of the Earth's surface and atmosphere with daily coverage in 36 spectral bands and a spatial resolution from 250 m to 1 km. These two major EOS platforms have been providing global observations of the atmosphere, land, and oceans since February 2000 and June 2002, respectively. Compared with AVHRR, MODIS provides observations of the Earth's surface with better radiometric quality and higher spatial resolution. In particular, the MODIS GPP/NPP product (Running et al. 2004) provides spatially continuous GPP and NPP estimates globally. The MODIS data products have also been used to develop continental scale forest biomass maps (Zhang and Kondragunta 2006; Blackard et al. 2008), and also to enable the estimation of GPP, ER, and NEP with 1 km spatial resolution at regional and continental scales (Xiao et al. 2014a). The availability of MODIS ASCII subsets for a variety of MODIS data products (e.g., surface reflectance, NDVI/EVI, LAI, the fraction of photosynthetically active radiation or FAPAR, GPP/NPP, albedo) (Cook et al.
246 2004) allows scientists to extract MODIS data for the grid cell or local area (e.g., 3 km × 3 km, 7 km × 7 km) surrounding specific research sites. This has greatly facilitated the calibration and evaluation of LUE models (Yuan et al. 2007; Mahadevan et al. 2008) and machine learning approaches (Xiao et al. 2008; Jung et al. 2009) and the data assimilation of terrestrial biosphere models (Stockli et al. 2008) for estimating C fluxes.
247
248
249
250

251 **3.5. Emergence of lidar technology**

252 Lidar can provide accurate information on the vertical structure of forests and therefore can be
253 used to quantify forest AGB. Lidar data were first used to estimate AGB of temperate deciduous
254 forests (Lefsky et al. 1999). Following this pioneering work, many studies have quantified forest
255 AGB at landscape or regional scales using airborne lidar data (Drake et al. 2002; Naesset and
256 Gobakken 2008). The availability of spaceborne lidar data in the 2000s made it feasible to map
257 forest AGB over large regions or even over the globe using lidar data (Lefsky et al. 2005). Lidar
258 data have also been used to estimate biomass for other ecosystems such as shrublands and
259 grasslands (Wu et al. 2009; Li et al. 2017). The integration of spaceborne lidar data with other
260 satellite data (e.g., MODIS, Landsat, SAR) can be used to map forest biomass seamlessly at
261 regional to global scales (Saatchi et al. 2012).
262

262 **3.6. Integrating remotely sensed data with AmeriFlux and FLUXNET measurements**

263 The eddy covariance (EC) technique provides continuous measurements of C, water, and energy 264 exchange at the ecosystem level at various timescales (e.g., diurnal, synoptic, seasonal, and 265 interannual) (Nie et al. 1992; Wofsy et al. 1993; Baldocchi et al. 2001). The partitioning of the 266 NEE measurements can lead to continuous GPP and ER estimates. The EC technique was 267 pioneered in the late 1980s in field campaigns (e.g., FIFE grassland study) (Nie et al. 1992). A 268 growing number of EC flux sites have been established across the globe since the early 1990s. 269
269 Regional flux networks (e.g., AmeriFlux) and FLUXNET coordinate analysis of EC flux
270 measurements at regional and global scales, respectively, and have made standardized flux
271 datasets available to the research community. The recently released FLUXNET2015 dataset
272 (http://fluxnet.fluxdata.org/data/fluxnet2015-dataset/) provides standardized and high-quality
273 flux and meteorological data for a total of 212 sites globally and has up to 15 years of overlap
274 between flux data and MODIS data products. These flux datasets have been used to evaluate or
275 calibrate C fluxes derived from remote sensing approaches. For example, AmeriFlux data have
276 been used to evaluate the MODIS GPP product (Heinsch et al. 2006; Xiao et al. 2014a) and to
277 estimate parameters in LUE models (Xiao et al. 2011b, 2014b). The release of the AmeriFlux
278 and FLUXNET datasets also enabled the upscaling of flux observations and the generation of
279 gridded GPP, ER, and NEP estimates at regional to global scales using machine learning
280 approaches (Xiao et al. 2008; Jung et al. 2011). The National Ecological Observatory Network
281 (NEON) has begun operations, and this continental-scale observation system will measure C,
282 water, and energy fluxes through 47 terrestrial sites across the U.S. The integration of remote
283 sensing with the measurements from AmeriFlux, FLUXNET, NEON, and other flux networks
284 will greatly benefit carbon cycle studies.

3.7. Quantifying NBP from satellite observations of column CO₂ concentrations

286 The availability of column CO₂ concentrations retrievals from satellites in the 2000s and 2010s
287 has made it feasible to quantify NEP from satellite observations. Column CO₂ concentrations
288 data have been obtained from the SCanning Imaging Absorption spectroMeter for Atmospheric
289 CartograpHY (SCIAMACHY) on board ESA's ENVIronment SATellite (EnviSat) (2002-2012)
290 (Barkley et al. 2006), the Thermal and Near Infrared Sensor for Carbon Observation-Fourier
291 Transform Spectrometer (TANSO-FTS) on board GOSAT (launched in 2009) (Wunch et al.
292 2011), NASA's OCO-2 (2014-present), and the Atmospheric Carbon Dioxide Grating 293 Spectrometer (ACGS) on board the Chinese Carbon Dioxide Observation Satellite (TanSat) 294 (2016-present). For example, Basu et al. (2013) estimated the distribution of CO₂ fluxes globally 295 using column CO₂ measurements from GOSAT. Inversions of satellite CO₂ observations provide 296 useful constraints on terrestrial C sinks/sources, although significant biases exist for fluxes even 297 aggregated over continental scales (Basu et al. 2013; Reuter et al. 2014).

298 **3.8. Free access to the Landsat record**

299 Prior to 2008, the cost and access to Landsat imagery had always limited our ability to monitor 300 the Earth's land surface (Woodcock et al. 2008). On April 21, 2008, the USGS announced plans 301 to make all archived Landsat scenes available to all users at no charge. The research community 302 has had "Web-enabled" access to the entire Landsat record since then. This new data policy has 303 revolutionized the use of the Landsat archive in creating new science, methods, and data 304 products (Wulder et al. 2012). The Landsat archive is also readily available on the Google Earth 305 Engine (Gorelick et al. 2017) for scientific analysis. The free-access policy of the Landsat 306 archive and its availability on Google Earth Engine are anticipated to promote the study of the 307 terrestrial C cycle.

308 **3.9. Advent of solar-induced chlorophyll fluorescence measurement from space**

309 The recent availability of satellite-based SIF measurements has provided great potential for 310 estimating terrestrial GPP regionally or globally (Frankenberg et al. 2011; Joiner et al. 2011). 311 Previous studies have demonstrated strong correlations between SIF and GPP by examining the 312 relationship of satellite-derived SIF with coarse-resolution, gridded GPP data products (Guanter 313 et al. 2012; Parazoo et al. 2014) or flux tower GPP more recently (Sun et al. 2017; Verma et al. 314 2017; Wood et al. 2017; Li et al. 2018b, 2018c). Previous studies showed that SIF predicted GPP
better than satellite-derived VIs and LUE models (Frankenberg et al. 2011; Li et al. 2018b, 2018c). SIF is also likely to be more sensitive to changes in plant photosynthetic status caused by environmental stresses such as heat and water stresses than most VIs (Daumard et al. 2010; Middleton et al. 2018; Zarco-Tejada et al. 2013; Joiner et al. 2014; Rascher et al. 2015) and has been increasingly used to examine the dynamics of terrestrial photosynthesis and its responses to drought (Sun et al. 2015; Yoshida et al. 2015; Li et al. 2018a). Satellite-based SIF observations will likely bring revolutionary changes to the estimation of terrestrial photosynthesis from space.

## 4. Remote sensing of carbon fluxes

Carbon fluxes can be quantified using various remote sensing data and methods. This section is divided into the following sub-sections: vegetation indices, light use efficiency models, terrestrial biosphere models, data-driven (or machine learning) approaches, solar-induced chlorophyll fluorescence, land surface temperature, and atmospheric inversions.

### 4.1. Satellite-derived vegetation indices as proxies of GPP and NPP

VIs derived from remote sensing spectral measurements are perhaps the most basic spectral measures of plant biologic activity from space. They combine the chlorophyll-sensitive red absorbing band with the leaf- and canopy structure-sensitive, NIR band, to represent a community property of canopy “greenness” encompassing canopy structure, chlorophyll content, plant phenology and leaf ontogeny. VIs aim to quantify the presence and extent of green vegetation and enable intercomparisons of vegetation “greenness” across space and time. The earliest VIs, such as the NDVI (Rouse et al. 1974), the perpendicular vegetation index (PVI) (Richardson and Wiegand 1977), and the tasselled cap green vegetation index (TC-GVI) (Kauth and Thomas 1976), have been widely used for estimating plant productivity. More recently developed greenness and chlorophyll indices include the EVI (Huete et al. 2002), the wide
dynamic range vegetation index (WDRVI) (Gitelson 2004), and the MERIS total chlorophyll index (MTCI) (Dash and Curran 2004). The robustness of VIs and available long term time series have generated a vast number of carbon-climate studies over northern latitudes, Africa, and the Amazon (Tucker et al. 1986; Myneni et al. 1997a; Tucker et al. 2001; Zhou et al. 2001; Xiao and Moody 2005; Saleska et al. 2016).

The phenological life cycle of plant species has large effects on photosynthesis rates and annual productivity (Tucker et al. 1986), and VIs are able to provide seasonal and annual growing season metrics of plant productivity. Previous studies found strong relationships between aboveground NPP (ANPP) and growing season integrated NDVI for North American biomes (Goward et al. 1985) and later between ANPP and EVI for U.S. arid grasslands to forests (Ponce-Campos et al. 2013) and arid and mesic grasslands (Moran et al. 2014). In general, VIs provide measures of the capacity of plants to absorb light energy and to reflect recent environmental stresses with reductions in NDVI indicative of less chlorophyll and/or less foliage (Running et al. 2004). Primary production is essentially an integrator of resource availability, and according to the resource optimization theory (Field et al. 1995), plants can maximize photosynthesis and growth with the adjustment plant characteristics by ecological processes to match the environmental capacity at weekly to monthly timescales. Often in-situ ANPP-VI uncertainties may arise due to discrepancies in timing of harvesting sampling periods. For example, when EVI was only partially integrated from the beginning to the peak of the growing season period (rather than the full season), significant improvements in productivity-EVI relationships were found across grassland sites (Moran et al. 2014). The NDVI integral over the early growing season was strongly related to in-situ measurements of tree diameter and ring
360 width in the central Great Plains (Wang et al. 2004). Continuous VI growing season productivity 361 profiles allow one to better synchronize VI temporal values with actual in-situ sampling periods.

362 VIs are used to estimate the rates of ecosystem processes (e.g., photosynthesis) that depend 363 on absorbed light (Monteith and Unsworth 1990). With the widespread use of VIs in large-scale 364 productivity assessments, there is great interest in learning how to best couple VIs with in-situ 365 productivity measures from flux towers. Further, the flux tower observations are top-of canopy 366 measurements that do not need details of canopy architecture or LAI, facilitating their 367 comparisons with VI measures that similarly involve community properties resulting from 368 integrative, top-of-canopy radiation interactions. VIs in combination with meteorological data 369 (e.g., temperature, vapor pressure deficit or VPD, solar radiation) have been used to estimate 370 GPP at various spatial scales using LUE models as described below in 3.2. There have also been 371 many attempts to estimate GPP based solely on remote sensing inputs, thereby minimizing or 372 eliminating the need for meteorological and LUE information. Spectral VIs were found to 373 accurately estimate GPP across a wide range of North American ecosystems (Wylie et al. 2003; 374 Rahman et al. 2005), African tropical savannas (Sjostrom et al. 2011), Australian mesic and xeric 375 tropical savannas (Ma et al. 2013), various terrestrial ecosystems in China (Xiao, Zhou, & Zhang 376 2015), and dry to humid tropical forests in Southeast Asia and the Amazon (Huete et al. 2006; 377 Huete et al. 2008). A more recent study examined the relationship between satellite-derived VIs 378 and flux tower GPP for 121 FLUXNET sites encompassing a wide range of biomes across the 379 globe and assessed how the VI-GPP relationship varies with indices, biomes, timescales, and the 380 bidirectional reflectance distribution function (BRDF) effect (Huang et al. 2019). These studies 381 indicated that VIs are able to estimate GPP with relatively high accuracy, thus potentially
382
383

simplifying C balance models and offering opportunities for region-wide upscaling of C fluxes
(Glenn et al. 2008).

384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404

The above studies, however, reported weaker relationships between VIs with tower GPP over aseasonal evergreen forests compared to seasonally contrasting deciduous forests. This may simply be due to the low seasonal signal range of VI values from which to statistically fit good relationships, although significant differences in VI relationships with tower GPP were found between biologically driven and meteorologically driven ecosystems (Restrepo-Coupe et al. 2016). No significant relationships between GPP and VIs were observed for primarily meteorological-driven and relatively aseasonal ecosystems (e.g., tropical forests). On the other hand, for phenology-driven ecosystems, GPP is driven by changes in the vegetation status and can be well represented by VIs. The highest VI-GPP correlations were found in ecosystems with synchronous meteorology and phenology, while low correlations were observed in locations with asynchronous meteorology and phenology (e.g., Mediterranean ecosystems). When successful, satellite-derived VIs constitute measurements of ecosystem structure and function, and are more related to measures of photosynthetic capacity (the maximum photosynthesis rate under ideal conditions of light, moisture, temperature, and nutrient availability) rather than GPP. Besides VIs, biophysical variables (e.g., canopy nitrogen content, chlorophyll content) derived from remote sensing data have also been used to estimate C fluxes. For example, canopy nitrogen content retrieved from hyperspectral airborne (e.g., the Airborne Visible/Infrared Imaging Spectrometer or AVIRIS) and broad-band satellite (e.g., MODIS) imagery has been used to estimate photosynthetic capacity (Ollinger et al. 2008) and NPP (Smith et al. 2002). These studies are mainly limited to landscapes and small regions due to the limited availability of hyperspectral data.
405

## 4.2. Estimating GPP and NPP with light use efficiency models

406

407

408

409

410

411

412

413

414

The LUE models (i.e., production efficiency models) are widely used to quantify GPP and NPP from remotely sensed data. LUE models are based on the original radiation use efficiency logic of Monteith (1972) that under well-watered and fertilized conditions the productivity of a cropland exhibits a linear relationship with the absorbed photosynthetically active radiation (APAR) by the canopies. Under actual environmental conditions, potential optical energy utilization rate is affected by water, temperature and other environmental factors. Therefore, GPP (or NPP) can be simulated in the LUE logic as APAR multiplied by maximum LUE and environmental stresses. The LUE approach has been one of the most important methods to map GPP and NPP regionally or globally (Potter et al. 1993; Running et al. 2004).

415

416

417

418

419

420

421

422

423

424

425

426

427

Remote sensing data play a significant role in the LUE approach by providing information on vegetation type, growth status and environment conditions. FAPAR is derived through spectral VI relationships (Asrar et al. 1984; Sellers 1985; Goward and Huemmrich 1992) or based on FAPAR products (e.g., MODIS FAPAR) (Zhao et al. 2005). Asrar et al. (1984) demonstrated the NDVI was linearly related with vegetation absorption of light energy (APAR) or FAPAR, and thereby related to productivity through the potential capacity of vegetation to absorb light for photosynthesis. The linear relationship between NDVI and FAPAR has been documented through field measurements (Ruimy et al. 1995; Fensholt et al. 2004) and theoretical analyses (Sellers 1985; Goward and Huemmrich 1992; Myneni and Williams 1994), although their relationship differs with canopy type, structure, soil, and sun-view orientation and saturates at high values (e.g., NDVI>0.7). AVHRR-FAPAR (Zhu et al. 2013) and GLASS-FAPAR products (Xiao, Liang, Sun et al. 2015) extend the temporal coverage of FAPAR back to 1982 and benefit to generate long-term GPP products. A previous study suggested that the fraction of PAR
428 absorbed by chlorophyll throughout the canopy (fAPARchl) could lead to more accurate  
429 cropland GPP estimates than the MODIS FAPAR (Zhang et al. 2014b). Remote sensing also  
430 provides measures of two other inputs of LUE models: water stress (Jones et al. 2017) and  
431 incident radiation (Zhang et al. 2014c). The photochemical reflectance index (PRI) (Gamon et al.  
432 1997) derived from tower-based spectral measurements (Middleton et al. 2009) and MODIS data  
433 (Middleton et al. 2016) has been shown to be a good proxy for LUE. In addition, remote sensing  
434 also provides spatially explicit information on land cover type (Loveland et al. 2000; Friedl et al.  
435 2010) that determines maximum LUE and other model parameters.

436 A number of LUE models have been developed and widely used for quantifying GPP (or 437 NPP) (Potter et al. 1993; Running et al. 2004; Xiao et al. 2004; Yuan et al. 2007). In particular, a 438 LUE approach along with NDVI (or EVI) was implemented in the Carnegie Ames Stanford 439 Approach (CASA) Biosphere model for simulating NPP (Potter et al. 1993). CASA has been 440 widely used to simulate C dynamics at regional to global scales although it could lead to large 441 biases in simulated C fluxes (Randerson et al. 2009). Moreover, a LUE approach along with 442 MODIS data were used to develop the standard MODIS GPP/NPP product, the first operational 443 and most widely used GPP/NPP product (Running et al. 2004) (Figure 4). All LUE models 444 integrate the physiological regulations of temperature and water demand (soil moisture or 445 atmospheric VPD). Some models also incorporate the enhancement effects of atmospheric CO2 446 fertilization (Veroustraete et al. 2002). In addition, some models (e.g., CFlux, TL-Model) 447 explicitly simulate the differential effects of diffuse and direct radiation on photosynthesis (Gu et 448 al. 2002) using an empirical equation or a two-leaf model (King et al. 2011; Zhou et al. 2016). 449 LUE models with the addition of a linear or nonlinear function of air temperature have also been 450 used to estimate NEE (Mahadevan et al. 2008; Xiao et al. 2011b). Different LUE models differed
in algorithms describing the regulations of environmental stresses particularly water stress on GPP (Yuan et al. 2014). Different model structures and parameterization schemes can result in marked discrepancies in GPP estimates (Xiao et al. 2011b; Cai et al. 2014; Yuan et al. 2014). Having proper model structure that accurately represents the regulation of all driving factors on GPP is essential for improving the GPP estimates (Zheng et al. 2018). Uncertainty in model parameters could lead to substantial uncertainty in flux estimates at regional scales (Xiao et al. 2014b).

<figure><img src="image_5.png" /><figcaption>**Figure 4.** Global mean annual NPP (gC m⁻² year⁻¹) over the period 2000-2015 based on the MODIS GPP/NPP product. (Courtesy of Steven W. Running)</figcaption></figure>

Reducing uncertainty in the GPP (or NPP) estimates is one of the challenges of LUE models. A model-data comparison study showed most LUE models underestimated GPP at cloudy or overcast days because the effects of diffuse radiation from cloud cover on LUE were ignored (Yuan et al. 2014). A recent study revealed that soil moisture stress alone reduced GPP at semi-humid, semi-arid, or arid sites by up to 40%, but most LUE models only integrate the effects of atmospheric water demand that cannot account for the limitation of soil moisture on GPP
(Stocker et al. 2018). A second challenge revolves around the fact that the uncertainty of remote sensing data would further lead to uncertainty in vegetation productivity estimates. For instance, the saturation problem of satellite-derived VIs generally results in underestimation of GPP or NPP in areas with dense vegetation. In addition, the global GPP products based on LUE models are at medium spatial resolution (e.g., 1 km), limiting their use in some applications (e.g., estimating regional crop yield, quantifying GPP in urban areas) that require GPP products with finer spatial resolution. The fusion of temporally dense satellite data (e.g., MODIS) and spatially finer observations (e.g., Landsat) can allow the estimation of GPP at finer spatial resolution (e.g., 30 m) (He et al. 2018), overcoming the limitations of current GPP products due to coarse resolution.

### 4.3. Quantifying carbon fluxes using terrestrial biosphere models

Process-based terrestrial ecosystem models describe terrestrial C, water, and energy fluxes in a mechanistic way to quantify these terrestrial cycles. The process-based representation of ecosystem processes allows us to understand terrestrial ecosystem status and changes in mechanistic ways. These models can be divided into two categories: diagnostic models and prognostic models. Diagnostic models use remotely sensed data (e.g., LAI, FAPAR) as temporally-variant input data to capture spatial and temporal variations in terrestrial vegetation in a more realistic way, and therefore their simulations are limited to the period when remotely sensed data are available. Prognostic models, in general, use climate data as temporally-variant input data and can simulate past-present-future changes in terrestrial ecosystem status. Many terrestrial model intercomparison studies revealed large uncertainties in simulated CO₂ fluxes at both site (Ichii et al. 2013) and regional (Huntzinger et al. 2012) scales. Therefore, refinements
490
491

of these models are strongly required. Both diagnostic models and prognostic models can make
use of remotely sensed data to improve their performance in quantifying C fluxes.

492
493
494
495
496
497
498
499
500
501
502
503
504

Diagnostic models generally adopted process-based photosynthesis sub-models (Farquhar et al. 1980; Collatz et al. 1991). With temporally-variant inputs of satellite-based LAI and/or FAPAR, photosynthesis is simulated and GPP is then calculated. These models are designed to estimate GPP in a more realistic way by considering mechanistic behaviors of photosynthesis responding to environmental conditions (e.g., climate and atmospheric CO2 concentration) and incorporating actual spatial and temporal variations in LAI and/or FAPAR. Most diagnostic models, such as SiB2 (Sellers et al. 1996), BEPS (Liu et al. 1997), InTEC (Chen et al. 2000), and BEAMS (Sasai et al. 2005), contain biomass and soil C pools. Therefore, these models can simulate not only GPP but also other C fluxes (e.g., NPP, ER, NEP, and NBP). In general, these process-based models use a similar approach to calculate autotrophic respiration and heterotrophic respiration based on the first-order decay with the responses to the environmental factors. NEP is calculated as the difference of GPP and ER. Some diagnostic models like the Breathing Earth System Simulator (Ryu et al. 2011) can only explicitly simulate GPP.

505
506
507
508
509
510
511
512

Remotely sensed data are also integrated with prognostic models for quantifying ecosystem C fluxes. Prognostic models internally simulate LAI based on climate and soil data, C allocation, and specific leaf area. The data assimilation is an effective approach to constrain and improve prognostic models and to reproduce more realistic ecosystem status using remotely sensed data (e.g., LAI). For example, satellite-based LAI data were used to nudge the Sim-CYCLE model (Hazarika et al. 2005). If the simulated LAI did not converge with the MODIS-LAI, the simulated LAI was replaced with the MODIS-LAI and then C fluxes were re-simulated. The assimilation of the MODIS LAI improved the simulation of NPP. Similarly, the assimilation of
513 MODIS LAI retrievals into a Dynamic Global Vegetation Model (DGVM) reduced the growing
514 season length and improved the estimates of GPP and NPP globally (Demarty et al. 2007).
515 Kleidon (2004) and Ichii et al. (2009) improved NPP and GPP simulations by inversely
516 estimating rooting depths using simple diagnostic ecosystem models and satellite-derived APAR
517 and evapotranspiration. Satellite data have also been used to optimize parameters of terrestrial
518 biosphere models. For example, satellite-derived FAPAR was used to reduce the uncertainty in
519 parameters in a terrestrial ecosystem model – BETHY, and the parameter optimization improved
520 the performance of the model for simulating C fluxes (Kato et al. 2013). A more recent study
521 used satellite NDVI observations together with EC data and atmospheric measurements to
522 optimize ~180 parameters of the ORCHIDEE model (Peylin et al. 2016).

523 Satellite-derived data products can have significant uncertainty that in turn can propagate
524 through model simulations and result in uncertainty in simulated fluxes. For example, a recent
525 modeling study showed that the significant uncertainty in satellite-derived LAI products led to
526 substantial uncertainty in GPP simulated by BEPS, a prognostic process-based model (Liu et al.
527 2018b). The uncertainty in satellite data products (e.g., LAI) will also lead to uncertainty in C
528 fluxes simulated by prognostic models assimilating these products. Moreover, additional
529 remotely sensed data that process-based models require may not be readily available. For
530 example, historical disturbance information (e.g., land cover change, forest age, fire) are key
531 inputs to process-based models for simulating the terrestrial C cycle. Forest age maps have been
532 generated at regional to continental scales using inventory and remote sensing information (Pan
533 et al. 2011; Zhang et al. 2014a), although it still remains a challenge to develop forest age maps
534 at the global scale. Diagnostic process-based models typically contain a number of processes and
535 parameters and are much more complex than LUE models. Unlike LUE models, diagnostic
models also require model initialization (model spinup), and therefore powerful computation
resources are often required to run these models at regional or global scales. In addition, the
required spinup process to create initial state of terrestrial biosphere pools sometimes makes the
model simulation difficult in the case of diagnostic process based models. In general, time-series
inputs of remotely sensed products (e.g. LAI, FAPAR, NDVI) are limited to the satellite
observation periods, and therefore it is sometimes difficult to create reasonable initial spinup
outputs.

## 4.4. Quantifying GPP, ER, and NEP using data-driven approaches

Remote sensing has been widely used to scale flux observations from EC flux towers to broad regions during the last decade or so. The growing network of flux towers provides continuous observations of the exchange of C, water, and energy between ecosystems and the atmosphere. Despite the large number of flux towers across the globe, they are not uniformly distributed and the tower measurements only represent the fluxes at the small tower footprint. The quantification of C fluxes at regional to global scales require the upscaling of the flux observations to these broad regions (Xiao et al. 2008; Jung et al. 2011).

<figure><img src="image_6.png" /></figure>

551
**Figure 5.** Mean annual carbon fluxes over North America for the period 2001-2012: (a) GPP; (b) ER; and (c) NEP derived from a data-driven approach. The units for carbon fluxes are g C m⁻² yr⁻¹. (Adapted from Xiao et al., 2014b)

Data-driven (or machine learning) approaches have been used to upscale C fluxes (GPP, ER, NEE) from sites to regional or global scales (Zhang et al. 2007; Xiao et al. 2008; Jung et al. 2011; Ichii et al. 2017). A special issue published in Journal of Geophysical Research: Biogeosciences (Xiao et al. 2012) reflected the progress in the upscaling of carbon and water fluxes from towers to broad regions prior to 2012, and significant advances have been made since then. The machine learning approaches used include artificial neural network (Papale and Valentini 2003), support vector machine (Yang et al. 2007), piecewise regression models (Zhang et al. 2007; Xiao et al. 2008), model tree ensemble (Jung et al. 2009), and random forest (Bodesheim et al. 2018). For example, a data-driven approach based on piecewise regression models were used in GPP, ER, and NEE for North America from MODIS and meteorological data (Xiao et al. 2014a) **(Figure 5)**. A recent study showed that four different data-driven techniques, including the adaptive neuro-fuzzy inference system, artificial neural network, extreme learning machine, and support vector machine, had almost identical performance in estimating forest C fluxes (Dou and Yang 2018). Although machine learning approaches differ from mechanistic models and do not explicitly incorporate biogeochemical processes, the machine learning methods can effectively estimate C dynamics through time and space and also reveal plant responses to environmental controls.

A variety of remotely sensed datasets such as VIs (e.g., NDVI, EVI), LAI, FAPAR, and land cover type have been used in data-driven approaches for the upscaling of C fluxes. Both daytime and nighttime LST data from MODIS have been incorporated to provide temperature estimates
with finer spatial resolution than meteorological reanalysis data (Xiao et al. 2008; Xiao et al. 2010; Xiao et al. 2014a). Satellite-derived measures of water availability such as the normalized difference water index (NDWI) (Gao 1996) have also been incorporated as explanatory variables in upscaling efforts (Xiao et al. 2008; Xiao et al. 2010; Xiao et al. 2014a). Some studies used additional remotely sensed variables. For example, the incorporation of stand age and AGB improved the estimation of C fluxes for forests, and the addition of the proxy for canopy nitrogen content (i.e., the NIR reflectance integrated over the peak portion of the growing season) reduced the uncertainty in C fluxes for both forests and non-forests (Xiao et al. 2014a). A more recent study integrated satellite-derived SIF into the artificial neural network model for global GPP estimation, and indicated that SIF could improve the estimation of GPP in regions where flux variability is not mainly driven by phenology and incident radiation (Alemohammad et al. 2017).

Machine learning approaches have been used to estimate C fluxes with reasonable accuracy (Zhang et al. 2007; Xiao et al. 2008; Jung et al. 2009; Xiao et al. 2010). The accuracy of the resulting GPP estimates is generally comparable to or slightly better than that of other approaches (e.g., LUE models) (Zhang et al. 2007; Ichii et al. 2017). The accuracy of ER was generally lower than that of GPP (Xiao et al. 2014a; Boyte et al. 2018) partly because the spatially explicit information on C pools and soil conditions is not readily available (Xiao et al. 2014a) and the regulation of soil respiration by temperature and precipitation is not well understood (Boyte et al. 2018). In general, the NEP estimation accuracy was lower than that of GPP and ER (Jung et al. 2011; Tramontana et al. 2016; Ichii et al. 2017; Bodesheim et al. 2018). Tramontana et al. (2016) compared 11 regression algorithms for estimating global ecosystem C fluxes, indicating that the accuracy of ER was slightly lower than that of GPP but higher than that of NEP. The lower accuracy in NEP estimates can be attributed to the lack of information on
disturbance history, stand age, biomass, SOC, and management practices (Xiao et al. 2014a; Papale et al. 2015). Based on the inter-comparison of models, a previous study showed that the Support Vector Machine was comparable to eight process-based models (Ichii et al. 2010). Another multi-model comparison study showed that the upscaling approaches based on machine learning methods were comparable to, or slightly better than, most ecosystem models (Raczka et al. 2013).

Despite their simplicity and effectiveness, the machine learning approaches face challenges and the resulting flux estimates have various sources of uncertainty. First, the number of EC flux sites and their distribution can significantly affect the accuracy and interannual variability of the regional flux estimates based on machine learning methods (Papale et al. 2015). A recent study based on multiple machine learning algorithms found that fluxes were better predicted in temperate regions than under-represented regions (e.g., the tropics) (Tramontana et al. 2016). In addition, the errors or uncertainty of satellite data and other explanatory variables (e.g., meteorological data) could propagate and lead to biases in the gridded flux estimates. The NEE estimates could have substantial uncertainty, particularly in productive regions, which makes it challenging to quantify the sizes of C sinks and sources. Explicitly incorporating information on management and disturbance into the machine learning approaches is anticipated to improve the accuracy of the resulting flux estimates.

## 4.5. Estimating GPP with solar-induced chlorophyll fluorescence

SIF is an electromagnetic signal emitted in the red and far-red portions of the spectrum by green leaves after excitation by solar radiation. SIF originates at the core of the photosynthetic apparatus and holds a mechanistic link to photosynthesis and is thus a better proxy of GPP than other biophysical parameters or VIs (Porcar-Castell et al. 2014). The relationship between SIF
and GPP has been examined by a growing number of field studies (Flexas et al. 2000; Meroni et al. 2008; Rascher et al. 2009; Daumard et al. 2010; Damm et al. 2015; Rossini et al. 2015; Yang et al. 2015b; Migliavacca et al. 2017; Yang et al. 2017; Miao et al. 2018; Yang et al. 2018; Gu et al. 2019). These studies have shown that SIF has the potential to indicate actual (as opposed to potential) plant photosynthetic activity, but at the same time have revealed the complexity of the link between top-of-canopy SIF measurements and GPP for the whole canopy.

At regional to global scales, an important breakthrough was made in 2011 with the development of global SIF maps (Frankenberg et al. 2011; Joiner et al. 2011) (Figure 6) based on measurements from the Japanese GOSAT mission. The SIF was retrieved by evaluating the in-filling of the solar Fraunhofer lines by SIF present in the 755-775 nm window. Following these two pioneering studies, several global SIF datasets have been produced from other space-borne spectrometers, including GOME-2 (the Global Monitoring Ozone Experiment 2) and SCIAMACHY (the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY) (Joiner et al. 2012; Joiner et al. 2013; Kohler et al. 2015; Wolanin et al. 2015) and more recently from OCO-2 (Sun et al. 2018) and TROPOMI (the TROPOspheric Monitoring Instrument) (Kohler et al. 2018). The GOME-2 SIF dataset has been the most widely used SIF product because of its continuous spatial sampling, global coverage, and long time series despite its coarse spatial resolution (40 km × 40 km in the best case). OCO-2, in turn, samples only a small portion of the Earth's land surface, but its relatively high spatial resolution (1.3 km × 2.25 km at nadir) makes it to be the first mission allowing direct comparisons of SIF retrievals and flux tower-based GPP estimates at the ecosystem scale (Verma et al. 2017; Wood et al. 2017; Li et al. 2018b, 2018c), which promises to deliver important information for the understanding of SIF-GPP relationships. The discrete OCO-2 soundings have also been used to generate spatially and
temporally continuous SIF estimates globally. For example, Li and Xiao (2019) developed the global, OCO-2 based SIF product (GOSIF) with 0.05-degree resolution and 8-day time step for the period from 2000 to present using OCO-2 SIF, MODIS, and meteorological reanalysis data. In addition, the first SIF retrievals from TROPOMI have become available very recently (Kohler et al. 2018). TROPOMI shares the spatially-continuous sampling of GOME-2 and its potential for SIF retrieval at both red and far-red wavelengths, but offers a much better monitoring potential because of the finer spatial resolution (i.e., 3 km ×7 km at nadir), a number of clear-sky SIF observations, and daily global coverage. The bidirectional effects associated with the wide swath of TROPOMI might introduce difficulty to the retrieval of far-red fluorescence.

<figure><img src="image_7.png" /><figcaption>**Figure 6.** First global solar-induced chlorophyll fluorescence (SIF) map based on monthly averages of instantaneous SIF for July 2009 from GOSAT P polarization. (Reproduced from Joiner et al. 2011)</figcaption></figure>

A number of studies based on global SIF products have demonstrated that a close relationship between SIF and GPP holds at ecosystem to global scales. A strong linear correlation was observed between SIF based on GOSAT and gridded GPP data derived from data-driven models for global and annual data composites (Frankenberg et al. 2011), and the
same applied to monthly averages over single biomes (Guanter et al. 2012). Joiner et al. (2014) found a good correspondence between the temporal trajectories of SIF and GPP. Li et al. (2018c) conducted a global-scale analysis based on OCO-2 soundings and flux tower data, showing that there was a strong relationship between SIF and GPP at the ecosystem level, and the relationship was also nearly universal across a wide variety of biomes. Other studies have focused on the analysis of the magnitude and dynamics of GPP using satellite-based SIF data over particular biomes, such as large crop belts (Guanter et al. 2014; Zhang et al. 2014d; Song et al. 2018), the Amazon forest (Lee et al. 2013; Parazoo et al. 2013; Guan et al. 2015), high latitude forests (Walther et al. 2016; Jeong et al. 2017), temperate forests (Li et al. 2018c), tundra ecosystems (Luus et al. 2017), and dryland ecosystems in Southwest U.S. (Smith et al. 2018). There is therefore a large body of literature showing that space-based SIF retrievals are able to represent GPP better than the reflectance-based vegetation parameters widely used in remote sensing.

To relate SIF to GPP some considerations should be taken into account. First, SIF is known to be a good indicator of green APAR, as SIF responds to PAR and is directly emitted by chlorophyll-a molecules. This close relationship between SIF and green APAR is most likely driving the high linear SIF-GPP correlations at coarse spatial and temporal scales (Yang et al., 2018) for those ecosystems (e.g., croplands, grasslands) in which productivity is driven by total chlorophyll content (Guanter et al. 2014). However, there is much less remote sensing-based evidence for the ability of SIF to track short-term changes in photosynthetic efficiency (Middleton et al. 2018). The SIF yield might be less sensitive than the photosynthesis yield under stress conditions (Wohlfahrt et al. 2018). In addition, although photosynthesis and fluorescence yields tend to be positively correlated under natural conditions, they have different temporal trajectories within the day and season for certain stress conditions (Porcar-Castell et al. 2014).
Second, the relationship between photosynthesis and top-of-canopy SIF measurements is complicated by leaf and plant structure effects: a fraction of the SIF photons emitted by one leaf can be trapped by other leaves, and is therefore not counted in the satellite measurement. This effect strongly depends on canopy structure and leaf morphology and orientation (Fournier et al. 2012) as well as wavelength. Some studies dealing with this topic and proposing different solutions for canopy-to-leaf downscaling have been published recently (Liu et al. 2018a; Romero et al. 2018; Yang and van der Tol 2018), and further research is warranted in the next years. Third, the clear-sky bias in space-based SIF retrievals complicates their use to quantify daily GPP estimates comprising both clear- and cloudy-sky conditions. This clear-sky bias especially affects SIF-based GPP estimates for those ecosystems in which clear-sky GPP differs substantially from cloudy-sky GPP (Zhang et al. 2018b).

The systematic estimation of GPP from space-based SIF retrievals requires therefore an explicit or implicit consideration of those confounding factors. Process-based modeling appears as a potentially effective approach to dealing with this problem. Mechanistic models with explicit representations of SIF and photosynthesis are being developed. For example, the SCOPE (Soil Canopy Observation, Photochemistry and Energy fluxes) photosynthesis and fluorescence model (van der Tol et al. 2009) couples SIF and GPP with a series of inputs describing vegetation status (pigment content and canopy structure) and meteorological conditions, and it is being widely used to analyze the relationship between SIF and GPP (Verrelst et al. 2015; Zhang et al. 2016). In parallel, the photosynthesis modules of some DGVMs are being extended in order to incorporate a consistent modeling of SIF (Lee et al. 2015b; Thum et al. 2017). An accurate representation of photosynthesis and SIF in such global C models is a pre-condition to the use of space-based SIF retrievals to benchmark terrestrial biosphere models and to improve their global
708
709

GPP predictions, which could be based on data assimilation schemes (Scholze et al. 2017; MacBean et al. 2018).

710

**4.6. Estimating ER from land surface temperature**

711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729

ER is a large C flux from the land to the atmosphere, and is thus an important contributor to the increasing atmospheric CO2 and, consequently, global climate change (Le Quere et al. 2009). The underlying reason of correlating air (or soil) temperature to ER is that temperature is a main environmental controlling factor of respiration rates (Raich and Schlesinger 1992; Lloyd and Taylor 1994). A remote sensing approach of using LST has the potential of providing spatially distributed estimates of ER on a per-pixel basis. Rahman et al (2005) first found that the MODIS-derived LST (MOD11) was strongly correlated with the EC-derived ER, especially for densely forested sites. Many studies then tested the ER-LST relationship and its variations in other regions and for different biomes. A strong correlation between MODIS LST and tower ER was found for two Swedish peat lands (R² = 0.93 to 0.98) (Schubert et al. 2010). Moore et al. (2013) utilized a modified version of the ‘TG’ model (Sims et al. 2008), in which LST was combined with the EVI of a high-elevation insect-infested forest to estimate GPP and then to derive ER (Moore et al. 2013). The MODIS-derived LST was combined with a MODIS-derived water index to model ER of a mixed temperate forest with improved seasonal cycles in NEE (Tang et al. 2011). Although most of the LST-ER correlation studies used MODIS LST data, Kimball et al. (2009) used the LST derived from the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) sensor and found it is an effective surrogate to soil respiration (HR and root respiration) across a broad range of boreal forest, grassland and tundra sites in the boreal and arctic biomes.
There are a few concerns on the use of LST for estimating ER. LST is not an exact measure of either soil temperature or vegetation temperature, but a complex thermal representation of the three-dimensional land surface. Satellite pixel size and the time of data acquisition also affect the LST values. Averaging LST values over a temporal period (such as 8 days for MODIS) and over a larger area (such as 3×3 MODIS pixels) acts as a smoothing filter, which can provide a better correlation with time averaged ER. Another limitation of the semi-empirical LST-ER correlation is that it does not include other constraints to ER, such as plant productivity, soil moisture, and nutrient limitations (Jagermeyr et al. 2014). In addition, although the biochemical processes involved in the two main components of ER (i.e., autotrophic and heterotrophic respiration) have been studied quite extensively (Atkin and Tjoelker 2003; Sitch et al. 2003; Wehr et al. 2016), measuring these component fluxes separately from an ecosystem is challenging, mainly due to the difficulty in their direct measurement. The EC technique does not provide estimates of the ER components, and therefore the satellite-derived LST can only be used to directly estimate ER.

A few recent studies have extended the LST-ER relationships to include a wider range of ecosystem functionalities and parameters for improving ER estimates using remotely sensed data. Wu et al (2014) found that the nighttime LST, when combined with NDVI, was strongly correlated with ER ($R^2$ = 0.86) of aspen, black spruce and jack pine stands in Saskatchewan, Canada and the NDVI was an important driver of ER, confirming the connection between photosynthetic activities and both AR and HR (Wu et al. 2014). Similarly, Gao et al (2015) developed an ER model by assuming that one part of ER is derived from the current photosynthetic activity with the respiratory rate coupling closely with GPP, and the other part of ER is derived from reserved ecosystem organic matter, with the respiratory rate responding strongly to temperature change. Their model was driven by MODIS-derived EVI, NDWI, and LST and
explained 93% of the variance in EC-derived ER for temperate mixed forest, temperate steppe, alpine shrubland, alpine marsh and alpine meadow-steppe in Northern China and the Tibetan Plateau (Gao et al. 2015). In another study, ER was modeled as an exponential function of soil temperature and moisture and the model output explained up to 82% of the variance in the EC-derived ER of a temperate deciduous broadleaf forest in Ozark, Missouri (Huang et al. 2014). More recently, six different process models were used to examine the LST-ER relationships of global terrestrial vegetation, showing that the LST-ER response curves largely displayed exponential trend of respiration rates with increasing temperature, although the exact forms of the relationships varied among the models (Ai et al. 2018). Based on this finding, Ai et al (2018) combined the EVI of global vegetation types in their models to incorporate the effects of vegetation on respiration and developed models of reference respiration, which was capable of capturing the spatial and temporal patterns of ER at the global scale.

Since the first publication showing the promise of estimating ER from satellite-derived LST (Rahman et al. 2005), most studies on this topic have used semi-empirical methods to correlate ER with LST. As mentioned above, a few recent studies have attempted to utilize the LST in process-based models in order to estimate ER of larger areas and diverse ecosystems. Further improvement on incorporating satellite-derived LST in the process-based ER models would allow the use of LST in operational models for routinely deriving ER values for global ecosystems. The finer-resolution LST data derived from Landsat have not been used to estimate ER yet. Recent studies have addressed the generation of consistent LST data from Landsat imagery and estimating their uncertainties (Fu and Weng 2016; Laraby and Schott 2018). Studies are needed to explore whether a smaller pixel of LST (such as 30 m of Landsat vs. 1 km of MODIS) data would lead to better ER estimates, particularly in heterogeneous regions. It should
be noted that the overpass time (around 10am) is not perfect to studying LST and thereby ER.

The AVHRR provides routine LST data of global coverage (1.1 km, two passes each day), and
Terra and Aqua MODIS together offers global daytime and nighttime LST measurements for
four different times of the day. ASTER provides 90 m resolution LST data every 16 days, but
these data are available free of charge. Landsat 9, which has been fast-tracked for a December
2020 launch, will have a Thermal Infrared Sensor 2 (TIRS-2) with two TIR bands onboard which
will enable consistent retrieval of LST. Use of thermal sensors onboard unmanned aerial vehicles
(UAV, or drones) will be able to provide high resolution, near surface LST measurements for
studying ER at detailed spatial scales and on-demand temporal intervals. The NOAA's
Geostationary Operational Environmental Satellites (GOES) provide routine LST data (4 km,
once every 15 min) for North America and South America. These GOES LST data can
potentially be used to examine the diurnal cycles of ER. The ECOsystem Spaceborne Thermal
Radiometer Experiment on Space Station (ECOSTRESS) launched on June 29, 2018 has the
diurnal sampling capability, and the resulting LST imagery (70 m × 70 m spatial resolution) can
also potentially be used to study the diurnal variations of ER.

## 4.7. Quantifying NBP using atmospheric inversions

NBP, the net carbon exchange between ecosystems and the atmosphere, can be quantified using
remotely sensed column CO₂ concentrations. CO₂ is a passive tracer in the atmosphere: it has no
chemical sink there and is only absorbed at the Earth's surface, by oceans and the terrestrial
biosphere. This characteristic, for instance, prevents defining a lifetime for CO₂ (Tans 1997).
Marginal sources in the atmosphere from the oxidation of reduced C (Folberth et al. 2005) and
from aircraft contribute to the increase of its concentrations, while respiration from the
biosphere, outgassing from the oceans and the volcanoes, fires and anthropogenic sources have a
much larger impact altogether (Ciais et al. 2013). Air motions therefore directly relate these various sources and sinks, termed “fluxes” hereafter, to the CO₂ concentrations that are observed in the atmosphere. This link is not a bijection and inverting it is meaningful only from a statistical point of view. Inversion is usually performed by assimilating the observed CO₂ concentrations into a numerical transport model under the constraint of some prior estimation of the fluxes and within a Bayesian framework (Peylin et al. 2013). Such an approach has been developed in the 1990s for the interpretation of measurements from the surface air sample networks (Enting et al. 1995), but sparse measurement coverage, particularly in low or high latitudes and over the oceans, limited the flux information content and motivated the extension of the approach to satellite observations of the CO₂ column (Rayner and O'Brien 2001).

Initial satellite studies focused on partial columns above the planetary boundary layer retrieved from radiances measured in the TIR wavelength by orbiting sensors that were designed for other purposes like meteorology (Chevallier et al. 2009; Nassar et al. 2011; Chevallier et al. 2014): High resolution Infrared Radiation Sounder-2 (HIRS-2) (Chedin et al. 2003), Atmospheric Infrared Sounder (AIRS) (Engelen et al. 2004) and Tropospheric Emission Spectrometer (TES) (Kulawik et al. 2010). Only Nassar et al. (2011) found the impact of the assimilation of such data (TES in this case) positive, and only by assimilating it jointly with measurements from the surface network. In general, these early attempts were hampered by systematic errors in the retrievals and in the transport models (particularly for vertical motion) of various amplitudes. The variability of the CO₂ column over the globe and over a year is of the order of 10‰ of the CO₂ dry air model fractions and most of it is already known: the useful signal is of the order of 1‰ of the retrieval value (Chevallier et al. 2005) and ideally should not be obscured by similar errors.
These issues have been reduced by optimizing the sensors for atmospheric inversion.
Appropriate choice of the wavelengths in the SWIR rather than the TIR range extended the column retrievals to the planetary boundary layer, close to the surface fluxes, in sunlit and cloud-free areas, and therefore relaxed the constraint on the accuracy of the transport model a bit. It also allowed better identification of the CO₂ signal in the measured spectrum with respect to the signals from other atmospheric constituents or from the surface, therefore reducing the retrieval systematic errors. This evolution was started by SCIAMACHY on-board EnviSat operated from 2002 until 2012, and has been continued by TANSO-FTS on-board GOSAT in 2009 (still being operated, and is followed by GOSAT-2 launched in October 2018), and by NASA's OCO-2 in 2014 (still being operated). Similar data from three recent Chinese satellites (e.g., TanSat) were not as widely available at the time of writing, but more instruments are being planned in the USA, Asia and Europe. Residual systematic errors in the SWIR retrievals are further damped by empirical bias-corrections against reference retrievals from ground-based spectrometers (Wunch et al. 2011), even though these reference data themselves have non-negligible absolute uncertainties (Wunch et al. 2010). Transport models have progressed on their side with increased horizontal and vertical resolutions, better meteorological analyses and improved parameterization of subgrid-scale processes (Locatelli et al. 2015; Krol et al. 2018).

A few inverse modeling studies attempted to document the natural surface fluxes at regional to global scales using these SWIR retrievals (Basu et al. 2013; Reuter et al. 2014), specifically for extreme events such as the 2010 summer heat wave in Eurasia (Guerlet et al. 2013), the 2011 La Niña episode in Australia (Detmers et al. 2015), and the 2015-2016 El Niño episode in the Tropics (Liu et al. 2017). Yet these studies have been paralleled by intercomparison exercises between inversions, or with aircraft data or C inventories that questioned the reliability of
existing satellite-based CO₂ inversions (Chevallier et al. 2014; Reuter et al. 2017). Kondo et al. (2015) examined global NBP (Figure 7) derived from the inversion of atmospheric CO₂ concentrations retrieved by GOSAT SWIR spectra, and found large differences in the NBP with flux estimate derived from a bottom-up approach.

<figure><img src="image_8.png" /><figcaption>**Figure 7.** Annual NBP estimates based on GOSAT L4A, a flux product derived from GOSAT atmospheric CO₂ concentrations with Bayesian inverse modeling: (a) terrestrial regions (i.e., tiles) used for the development of the GOSAT L4A product; (b) mean annual NBP over the period 2000-2014.</figcaption></figure>
853

854

The much increased scientific visibility of CO₂ remote sensing (Eldering et al. 2017), manifests the fact that the accuracy of the CO₂ retrievals and of the transport models is approaching the levels needed to constrain the fluxes from the natural biosphere (Buchwitz et al. 2017) despite all residual uncertainties in the end-to-end processing chains. The remote sensing of CO₂ has defined unprecedented quality standards in the monitoring of the Earth's atmosphere.

855

856

857

858

859

860

861

862

863

864

865

866

867

868

## 5. Remote sensing of carbon stocks

869

870

871

872

873

Remote sensing has been widely used to quantify biomass carbon stocks at regional to global scales since the early 1990s. Biomass C stock has been estimated using passive optical remote sensing data, microwave remote sensing data, and lidar remote sensing data. Carbon stocks in soils have also been quantified using remote sensing, mainly for croplands and at relatively small spatial scales.

874

### 5.1. Passive optical remote sensing of biomass

875

876

AGB has been frequently estimated from passive optical remote sensing that is particularly sensitive to vegetation canopy properties. Passive optical satellite observations with a wide range
of spatial resolutions have been applied for estimating AGB. The coarse-resolution data (250m - 8000m) (e.g., AVHRR, SPOT, and MODIS) are often used to estimate biomass at regional or global scales for various ecosystems such as forests (Dong et al. 2003; Zhang and Kondragunta 2006; Chopping et al. 2011), grasslands (John et al. 2018), and Arctic tundra (Epstein et al. 2012). The medium spatial resolution data (~30m), mainly from Landsat TM/ETM+/OLI, Sentinel-2 Multispectral Imager (MSI), and Terra/Aqua ASTER, are perhaps the widely-used optical remote sensing data for biomass estimation at local and regional scales for different ecosystems such as forests (Fazakas et al. 1999; Turner et al. 2004; Sibanda et al. 2015), grasslands (Friedl et al. 1994), and shrublands (Shoshany and Karnibad 2011). The high spatial resolution data (< 5m), including high-resolution satellite imagery on QuickBird (2.44m in multispectral and 0.61m in panchromatic), IKONOS (3.2m in multispectral and 0.82m in panchromatic), and WorldView-2/3 (1.85m in multispectral and 46cm in panchromatic), are commonly used to calculate local tree biomass (Leboeuf et al. 2007; Palace et al. 2008; Fuchs et al. 2009) and grass biomass (Sibanda et al. 2017).

The variables derived from passive optical remote sensing for biomass estimation mainly consist of spectral reflectance, VIs, spatial texture, and vegetation canopy attributes. Spectral reflectances are the simplest variables, which vary from visible to SWIR wavelength. VIs are more frequently used in biomass estimation because they can enhance green vegetation signals and minimize the impacts from soil background, sun-canopy-sensor geometry, and atmosphere (Huete et al. 1985). The commonly used VIs are NDVI, EVI, Simple Ratio (SR), middle infrared (MIR) index, chlorophyll based difference index, and tasseled cap (TC) transformation or principal component analysis (PCA) of spectral bands. Spatial texture of spectral or panchromatic bands, which describes the spatial characteristics of images and identifies objects
or regions in images, has also been used for biomass estimation (Sarker and Nichol 2011).
Canopy spatial texture is scale dependent, which can be calculated using a 3 × 3 window size or
Fourier Transform Textural Ordination (FOTO) method from very high spatial resolution
imagery (Eckert 2012; Dube and Mutanga 2015b; Ploton et al. 2017).

Moreover, vegetation canopy attributes derived from optical satellite data can be used as
effective predictors of AGB. The frequently used attributes include LAI, canopy structure, and
tree shadow fraction. LAI, an important biophysical factor regulating photosynthesis,
evapotranspiration, and C fluxes, has been estimated from spectral VIs based on either empirical,
regression models (Heiskanen 2006) or radiative transfer (RT) algorithms (Myneni et al. 1997b).
Canopy structure is generally quantified using biophysical parameters (e.g., tree crown size/area,
height, and density), and both foliage biomass and total standing biomass are related to these
parameters (Franklin and Hiernaux 1991; Wu and Strahler 1994). Canopy reflectance models
have been used to estimate the crown size and density within each grid cell. The Li-Strahler
geometric-optical (GO) model is a widely used canopy reflectance model (Li and Strahler 1985).
Shadow fraction can also be quantified from medium-resolution satellite imagery (e.g., Landsat
TM/ETM+) using spectral mixture analysis (SMA) (Hall et al. 1995) and from high-resolution
satellite imagery using a digital value threshold approach (Leboeuf et al. 2007). Although it is
not a biophysical forest attribute, shadow fraction has proven useful for the estimation of forest
biomass (Greenberg et al. 2005).

AGB is generally estimated by empirically or functionally relating to satellite-derived
variables based on empirical models (regression or machine learning approaches) and physically-
based allometric models. Simple regression models are established by associating a single VI or
spectral reflectance to field biomass measurements (Roy and Ravan 1996; Calvao and Palmeirim
2004; Heiskanen 2006). The simple linear model format could also be transformed from logarithmic or exponential functions. The model coefficients can be estimated with the ordinary least-squares approach by assuming that independent variables have high accuracy or the reduced major axis regression method by assuming that both dependent and independent variables have significant uncertainties (Larsson 1993; Powell et al. 2010). Although the simple model is widely applied to estimate AGB in a local region, the model accuracy and optimal independent variables vary with spectral variable and local environment (Roy and Ravan 1996; Foody et al. 2003; Lu et al. 2004). Further, the simple regression models could also correlate biomass to shadow fraction from high resolution imagery (Jasinski and Crago 1999; Leboeuf et al. 2007) and to the LAI in the logarithmic relationships (Saatchi et al. 2007; Madugundu et al. 2008).

A multiple regression model is able to improve biomass estimates by combining surface reflectance, VIs, and biophysical variables (Zheng et al. 2004; Hall et al. 2006). Multiple regression analyses can be performed in three different ways. First, the multiple regression from the ordinary least-squares approach typically includes all explanatory variables regardless of the strength of the relationship between each variable and biomass. Second, stepwise regression analysis can be used to identify the most important explanatory variables for the final model based on their relative contributions. Third, the canonical correlation analysis can maximize the correlation among variables and align spectral bands with the variation in biomass (Heiskanen 2006) and therefore allows multiple regression analysis in a simple and linear way (Cohen et al. 2003). A variety of multiple regression models have been used to estimate biomass (Lu 2005; Saatchi et al. 2007; Powell et al. 2010; Eckert 2012; Dube and Mutanga 2015b), while the
variables used vary among studies and it is still challenging to identify the optimal variables for a given study.

The machine learning algorithm or non-parametric approach has been widely used in recent biomass estimation efforts. Unlike regression methods, this approach can easily handle a large number of explanatory variables derived from remotely-sensed and ancillary data that are linearly or nonlinearly related to biomass. The commonly used machine learning algorithms include neural networks (Foody et al. 2001) and regression trees (John et al. 2018). The machine learning approaches have been established for estimating AGB using time series of MODIS reflectance with ancillary data (including climate and topographic variables) in large regional areas for forests in California (Baccini et al. 2004), Africa (Baccini et al. 2008), and Russia (Houghton et al. 2007), and across the conterminous U.S. (Blackard et al. 2008) and for grasslands in Mongolia and Inner Mongolia (John et al. 2018). Similarly, machine learning approaches, particularly random forest (Breiman 2001), are also established using other optical satellite observations (such as Landsat TM/OLI, Sentinal-2 MSI, and Woldview-2) and spectral variables to calculate biomass at regional scales (Powell et al. 2010; Dube and Mutanga 2015a; Karlson et al. 2015; Pandit et al. 2018). In general, the regression tree approach has advantages over other models in producing consistent estimates with smaller errors (Powell et al. 2010).

Allometric model is a physically-based approach and has been widely used for the estimation of forest AGB. It converts the measurements of tree variables (e.g., canopy crown size, crown depth, diameter at breast height or DBH, and tree height) to AGB based on field observations (TerMikaelian and Korzukhin 1997). The allometric models for biomass estimation are in various forms (Pastor et al. 1984; TerMikaelian and Korzukhin 1997), and AGB is most commonly estimated as a power function of DBH. Although the specific forms of allometric
models vary with species and site, generalized allometric models have also been used for biomass estimation across large regions dominated by mixed species (Jenkins et al. 2003; Wirth et al. 2004).

Most vegetation attributes, particularly tree height and stem diameter, are hard to be retrieved from passive optical remote sensing data, but canopy properties can be easily detected by optical remote sensing data. A foliage-based allometric model was developed to associate AGB to foliage biomass that is a function of satellite-derived LAI (Zhang and Kondragunta 2006). As a result, the maximum LAI derived from 1 km MODIS LAI product (Myneni et al. 2002) was used to estimate AGB across the conterminous United States after the allometric models were established for needleleaf forests, broadleaf forests, and mixed forests, respectively (Zhang and Kondragunta 2006). Moreover, a biomass allometric model based on satellite data was also established with tree crown area. Allometric models are typically developed for coniferous and deciduous trees separately using field measurements and linear least squares regression. Tree crown area derived from the GO model (Soenen et al. 2010; Chopping et al. 2011), object-based classification (Rasmussen et al. 2011), or high-resolution satellite data (Hirata et al. 2014) is used to estimate forest biomass at regional scales based on general allometric models. This method was used to estimate biomass in Canada from SPOT multispectral data and was shown to be better than empirical approaches (NDVI or shadow fraction) (Soenen et al. 2010). Canopy structure parameters were also retrieved from both MODIS and MISR data for the estimation of biomass (Chopping et al. 2011).

Passive optical remote sensing provides perhaps the best tool for biomass estimation at regional to global scales because of its global coverage, multiple spatial resolutions, repeat visits, and cost-effectiveness. However, the satellite variables used in the developed empirical models
991 (regression or machine learning approaches) for AGB estimation vary greatly across various
992 regional environments and the models cannot be directly transferred across biomes and different
993 vegetation phenological stages of satellite observations. Further, it is difficult to evaluate the
994 relative performance of different models because of the complexity in AGB estimation. Satellite-
995 based allometric model is an alternative approach that estimates AGB based on vegetation
996 structural variables. It is challenging to accurately retrieve vegetation canopy attributes from
997 passive optical remote sensing data. The spectral signal tends to saturate when biomass is high,
998 leading to underestimation of high biomass density and overestimation of low biomass density.

## 5.2. Microwave remote sensing of biomass

1000 Microwave remote sensing sensors include radiometers and scatterometers. The scatterometer is
1001 also referred to as an active sensor because it actively sends microwave pulses to the ground and
1002 measures the backscattered power, while the radiometer is referred to as a passive sensor because
1003 it passively observes the microwave radiance from the Earth and its atmosphere (Liu 2015). Real
1004 aperture radar (RAR) is a scatterometer that measures the range from the sensor to the target.
1005 The resolution of RAR depends on the pulse width in the range direction and the distance from
1006 the antenna to the target in the azimuth direction, while the beam width of the radar depends on
1007 both wavelength and the antenna size. In 1951, Carl A. Wiley found that a long antenna could be
1008 synthesized through the frequency analysis of Doppler shift of the signal reflected to the radar by
1009 the objects (McCandless and Jackson 2004). The synthetic aperture radar (SAR) was invented
1010 based on RAR using Wiley's findings. The first civilian spaceborne SAR was the one onboard
1011 the SEASAT which was launched in June 1978. Although the SAR onboard SEASAT only
1012 operated for a short period of time (e.g., ~100 days), this satellite opened a new era of remote
1013 sensing (Shi et al. 2012). Since then, several civilian SAR satellites have been placed in orbit by
National Aeronautics and Space Administration (NASA), European Space Agency (ESA), Japanese space Agency (JAXA), and Canadian Space Agency (CSA) for the observation of earth surface including both the sea and the land.

The wavelength or frequency of SAR is an important parameter for forest monitoring. The microwave with longer wavelength can penetrate deeper into forest canopies. The classic bands (frequency or wavelength) used by SAR in ascending order of wavelength include X-band (9.6G, 3.0cm), C-band (5.6G, 5.7cm), S-band (3.0G, 10cm), L-band (1.27G, 23.5cm), and P-band (0.435G, 70.0 cm). The well-known SAR satellites include SIR-A (USA, L-band, 1981), SIR-B (USA, L-band, 1984), ERS-1 (Europe, C-band 1991), ALMAZ (USSR, S-band, 1991), JERS-1 (Japan, L-band, 1992), SIR-C/X-SAR (USA, C-band, L-band; Germany, X-band, 1994), ERS-2 (Europe, C-band, 1995), RADARSAT-1 (Canada, C-band, 1995), SRTM (USA, C-band; Germany, X-band, 2000), ENVISAT (Europe, ASAR, C-band, 2002), ALOS/PALSAR (Japan, L-band, 2006), RADARSAT-2 (Canada, C-band 2007), TerraSAR-X (Germany, X-band, 2007), TanDEM-X (Germany, X-band, 2010), HJ-1C (China, S-band, 2012), ALOS-2 (Japan, L-band, 2014), and Gaofen-3 (China, C-band, 2016). Several new missions under preparation include BIOMASS (Europe, P-band, scheduled 2022), NISAR (USA, L-band; India, S-band, scheduled 2020), and TanDEM-L (Germany, L-band, scheduled 2022).

Microwave radar data is coherent signal with both amplitude and phase information and can be operated at various polarization, such as H (horizontal) and V (vertical) polarizations. Polarimetric SAR includes signals at quad-polarization (HH, HV, VH, and VV). The radar backscatter intensity (i.e., the power of the backscatter signal) at HV polarization was the first used for forest biomass estimation. In addition to the use of SAR backscattering intensity, the other use of SAR data includes SAR interferometry (InSAR) (Rosen et al. 2000),
1037 Radargrammetry (Schanda 1985), Polarimetric SAR interferometry (PolInSAR) (Cloude and Papathanassiou 1998), and tomographic SAR (TomoSAR) (Frey et al. 2008). In the period of 1038 SIR-A and SIR-B, the information of earth surface was mainly extracted from the SAR image 1039 intensity. Radargrammetry is the application of photogrammetry (optical image) on SAR images. 1040 Radargrammetry can detect the vertical distribution of ground objects by measuring 1041 displacements of one object on two SAR images acquired with different view angles. Scientists 1042 insisted on the studies of radargrammetry from 1960s till recent years (G 1963; Vastaranta et al. 1043 2014). In addition to terrain elevations, the improvement of spatial resolution of SAR images 1044 provided radargrammetry a new opportunity to capture the 3D structures of forest (Vastaranta et al. 2012). The InSAR data from SIR-C/X-SAR mission and tandem ERS-1 and ERS-2 was a 1045 milestone in the monitoring of forest using SAR data. They advanced studies of SAR 1046 interferometry, especially interferometric coherence, on the estimation of forest AGB. PolInSAR 1047 and TomoSAR can directly detect the 3D structure of forest and provide a new technique for 1048 monitoring forest AGB. The new missions, including BIOMASS (Le Toan et al. 2011), NISAR 1049 (Rosen et al. 2016) and Tandem-L, have the work mode to acquire SAR data for PolInSAR and 1050 TomoSAR. 1051
1052

1053 Spaceborne SAR can work at all time under all-weather conditions. Therefore, it has great 1054 potential for the estimation of forest AGB even over areas that are often covered by clouds or fog 1055 throughout the year. The estimation of forest AGB was initiated by two papers published in the 1056 early 1990s. Le Toan et al (1992) observed the strong correlation of L- and P-band SAR 1057 backscattering coefficients with red pine biomass. Dobson et al (1992) reported that the 1058 backscatter of polarimetric airborne SAR at P-, L- and C-bands was dependent on AGB of 1059 conifer forests. Following these two pioneering studies, many scientists estimated forest biomass
1060 using multi-band and multi-polarization SAR data (Beaudoin et al. 1994; Rignot et al. 1994; Harrell et al. 1995; Kasischke et al. 1995; Pulliainen et al. 1996; Ranson et al. 1997; Ranson and Sun 2000; Sun et al. 2002; Ni et al. 2016). In addition to radar backscatter coefficients, interferometric coherence was also used to estimate forest AGB in the early 2000s (Luckman et al. 2000; Gaveau 2002; Santoro et al. 2002; Santoro et al. 2007). These studies showed that the SAR backscatter increased almost linearly with increasing biomass and then gradually saturates at high biomass level. The biomass saturation level depends on radar frequency with ~200 tons/ha at P-band and ~100 tons/ha at L-band, while the backscatter at the C-band is much less sensitive to biomass (Shi et al. 2012). For example, Yu and Saatchi (2016) examined the relationship between ALOS/PALSAR HV backscatter and AGB for tropical forests and temperate conifer forests, and found different relationships and different levels of sensitivity to AGB among the biomes (Figure 8). Neither the SAR backscattering coefficients nor the interferometric coherence is the direct measurement of forest AGB. Their strong correlation with AGB could be easily destroyed by environmental factors such as precipitation and soil moistures (Kasischke et al. 2011).
<figure><img src="image_9.png" /><figcaption>**Figure 8.** Relationships between bin-averaged means of ALOS/PALSAR HV backscatter coefficient (sigma-0) and AGB values of the middle of the bin for tropical moist forests (Africa, Southeast Asia, and Latin America) and temperature conifer forests. Solid lines stand for the regression lines between HV sigma-0 and AGB. For fitting purposes, outliers marked with x were excluded and bins above 155 Mg ha⁻¹ were cut off for tropical forests. (Reproduced from Yu and Saatchi 2016)</figcaption></figure>

Forest AGB is directly determined by four parameters: tree number, tree height, tree DBH, and tree species. Forest spatial structures are characterized by the first three parameters. The direct measurement of forest spatial structure using PolInSAR and TomoSAR data will be the final solution to overcome the saturation for accurate estimation of forest AGB. The PolInSAR and TomoSAR provide this kind of SAR techniques. PolInSAR makes compound use of the dependence of penetration capability of microwave on both frequency and polarizations, while
1088 TomoSAR combines the penetration capability of microwave on frequency and view angles.
1089 Many studies have measured forest height using SAR imagery (Garestier et al. 2008; Minh et al.
1090 2016; Khati et al. 2017). Currently, the dataset used in studies on PolInSAR and TomoSAR are
1091 acquired by airborne sensors. The advantages and disadvantages of PolInSAR and TomoSAR
1092 will be fully explored when the BIOMASS, NISAR and Tandem-L data are available in near
1093 future.

1094 **5.3. Lidar remote sensing of biomass**
1095

1095 Lidar is an active remote sensing technology which determines the distance between the sensor
1096 and the target using laser energy. Lidar is able to provide accurate information on the vertical
1097 structure of forests through recorded discrete returns or waveforms (Lefsky et al. 2007; Pang et
1098 al. 2008; Simard et al. 2011). Discrete return lidar systems record several returns from each laser
1099 pulse. Waveform lidar systems record a continuous waveform with return signal as a function of
1100 time from sensor to target. By using different platforms, lidar data can be acquired from
1101 terrestrial, aircraft, and spacecraft lidar. These data can characterize the vertical information of
1102 forests at different scales varying from leaf, branch, individual tree, to large forest stands. Figure
1103 9 illustrates a typical workflow of deriving vegetation vertical structure and biomass from raw
1103 lidar data (Zhao et al. 2018b). Lidar data have also been used to estimate the biomass of other
1104 ecosystems such as shrublands (Li et al. 2017) and grasslands (Wu et al. 2009).
1105
<figure><img src="image_10.png" /><figcaption>**Figure 9.** A workflow of deriving vegetation vertical structure and biomass from raw lidar data. Airborne discrete return lidar data are used as an example here, and a similar workflow applies to other types of lidar data. (Adapted from Zhao et al. 2018b)</figcaption></figure>

The terrestrial laser scanning (TLS) sensor provides very dense point cloud data with millimeter intervals (Liang et al. 2016). After classification, TLS data can be separated into points from trunk, branch, and leaves for forests. Branch and foliage volume can then be estimated using shape information fitted from point cloud data (Hauglin et al. 2013; Stovall et al. 2017). This provides a non-destructive way to estimate AGB, even for allometric equation development (Kankare et al. 2013; Stovall et al. 2017). A more recent study showed how to produce quantitative 3D models of branch and trunk using quantitative structural models (Disney et al. 2018). These models improved the estimates of AGB especially for irregular tree stems. A recent study demonstrated the potential of this direct biomass estimation method for tropical forests (Rahman et al. 2017). Besides biomass estimation at individual tree level, forest biomass
or volume at stand level has also been successfully estimated using single-scan TLS data (Astrup et al. 2014; Liang et al. 2016).

Airborne laser scanners (ALS) has been increasingly used for biomass estimation. Strong relationships between laser data and AGB have been shown in many forests because of the capability of ALS to capture canopy height and density accurately. Through segmentation of canopy height model or point cloud data with high density, the ALS data can be used to quantify height, crown width, and crown volume that in turn can be used to estimate biomass at individual tree level (Popescu 2007; Tao et al. 2014). With the increasing availability of multitemporal ALS data, changes in tree level biomass can also be estimated, although correction of negative height biases might be needed (Zhao et al. 2018b). Recently, lidar data acquired from UAVs have also been used to estimate AGB of forests (Messinger et al. 2016) and grasslands (Wang et al. 2017) at landscape scales.

More operational applications at the stand level or large area make use of low point density ALS data. The lidar-derived height and density metrics are often used to build biomass estimation model with biomass data from field plots (Naesset and Gobakken 2008; Zhao et al. 2009; Pang and Li 2012). For example, Naesset and Gobakken (2008) investigated the relationship between forest biomass and canopy coverage using two groups of variables derived from lidar data which include quartile heights and crown densities. Zhao et al. (2009) estimated biomass using two scale-invariant models based on lidar-derived canopy height distributions and quintile functions, respectively. Pang et al. (2012) showed the benefits from species group stratification for component biomass estimation of stem, branch, and leaves. The airborne large footprint waveform data also showed good biomass estimates for a variety of forest types (Lefsky et al. 1999; Drake et al. 2002). Lefsky et al. (1999) estimated biomass of a temperate
1144 deciduous forest using height indices from the Scanning LiDAR Imager of Canopies by Echo
1145 Recovery (SLICER) data. Drake et al. (1999) used the height of median energy from the Laser
1146 Vegetation Imaging Sensor (LVIS) data as a good index for biomass estimation in tropical forests.
1147 Recently, some studies also estimated biomass at the provincial or even national scale using ALS
1148 data (GOFC-GOLD 2016; Price et al. 2017). Price et al. (2017) proved that ALS data were
1149 helpful to estimate biomass of trees outside forest. The GOFC-GOLD recommended the use of
1150 ALS data for biomass estimation in local efforts in reducing emissions from deforestation and
1151 forest degradation (REDD+) (GOFC-GOLD, 2016). Lidar-based biomass surveys at regional
1152 scales may require less intensive field sampling than other remote sensing approaches.

1153 Over the past decade, several spaceborne lidar sensors have been on-orbit. The main
1154 advantages of spaceborne lidar is the capability to collect data routinely for large regions or even
1155 globally. The Geoscience Laser Altimeter System (GLAS) aboard the ICESat satellite operated
1156 from 2003 to 2008. Lefsky et al. (2005) showed that it was feasible to estimate forest AGB using
1157 GLAS waveform data firstly. The GLAS data were then used to estimate biomass for pan-
1158 tropical forests (Baccini et al. 2008; Saatchi et al. 2011). The main limitations of spaceborne
1159 lidar systems are the spatial discontinuity and short operation period. Therefore, these discrete
1160 footprint biomass estimates are usually fused with estimates from other imagery remotely sensed
1161 data (e.g., MODIS, Landsat, and PALSAR) (Baccini et al. 2008; Saatchi et al. 2011; Sun et al.
1162 2011).

1163 With the development of lidar technology, more lidar data are becoming available from new
1164 generation systems like Advanced Topographic Laser Altimeter System (ATLAS) and Global
1165 Ecosystem Dynamics Investigation (GEDI). The ATLAS onboard ICESat-2 launched in
1166 September 2018 is a photon counting system using a micro-pulse, multi-beam photon counting
lidar that works at 532 nm (Markus et al. 2017). Compared to waveform data, the photon counting approach introduces abundant noise appearing in the atmosphere and even below the ground, making it difficult to extract the correct canopy and ground surface in vegetated areas. A few studies have been done to detect the noise and separate the signal, such as a recent novel methodological framework was developed to retrieve ground and canopy height using data from the Multiple Altimeter Beam Experimental Lidar (MABEL), an airborne simulator of ATLAS (Popescu et al. 2018). An approach that was tested with MABEL data by using local outlier factor algorithm modified with an ellipse searching area showed good performance not only in lower noise rate with relatively flat terrain surface but also for a quite high noise rate environment in relatively rough terrain (Chen, Pang, Li et al. 2019). These studies showed good performance for MABEL and simulated data, but further development of noise filtering is still necessary to explore vegetation applications for the new ATLAS data. A study to compare photon counting height metrics with both MABEL and discrete return lidar (DRL) has been done in savanna ecosystem (Gwenzi et al. 2016), showing moderate correlation between MABEL-derived height metrics and DRL-derived height metrics and weaker correlation between simulated ATLAS data with DRL indices. The GEDI system contains 3-laser system flying on the International Space Station (ISS), and the full waveform lidar has a circular footprint of ~25 m. Compared with GLAS system with 40 Hz frequency, GEDI will work with 242 Hz (Stavros et al. 2017). GEDI, launched on December 5, 2018, is the first spaceborne lidar mission that was specifically designed to study forests. GEDI will cover the geographical region between ~51.6°N and S latitudes from the ISS. The availability of the lidar data from these spaceborne platforms will greatly facilitate the estimation of AGB at large scales.

## 5.4. Remote sensing of soil organic carbon
Laboratory spectral characteristics of soils in the Visible, NIR, and SWIR (VNIR-SWIR, 400-2500 nm) domain have been largely studied and described for decades (Ben-dor and Banin 1995; Ben-Dor et al. 1999; Rossel et al. 2006). The base line height and spectral shape are linked to soil physical features and absorption bands are linked to soil chemical features. For example, clay minerals produce an absorption band centred at 2200 nm due to the combination of vibrations associated with the OH and OH-Al-OH bonds (Hunt et al. 1971) and organic matter induces changes in spectral shape and absorption bands due to vibrations of O-H and C-H bonds linked to lignin and cellulose (Ben-Dor et al. 1997). Previous studies have then demonstrated that VNIR-SWIR spectral measurements acquired in laboratory conditions over soil samples can be used to accurately estimate several soil properties, such as soil organic matter (SOM) or SOC, Clay, Calcium Carbonate and Iron content (Chang and Laird 2002; Shepherd and Walsh 2002). For example, Shi et al. (2014) classified 1581 soil samples to clusters with different SOM content based on VNIR-SWIR spectra using a fuzzy clustering method, and found that the spectral reflectance of the soil clusters generally decreased with increasing SOM content (Figure 10).

<figure><img src="image_11.png" /></figure>

1205
**Figure 10.** Reflectance spectra of soil samples in different clusters with different soil organic matter (SOM) content. (Adapted from Shi et al. 2014)

The mapping of soil properties from VNIR-SWIR imaging data has followed encouraging results of laboratory VNIR-SWIR spectroscopy and is achieved by developing either SpectroTransfer Functions (STFs) models based on multivariate regression models (Gomez and Lagacherie 2016) or SCORPAN (soils and/or soil properties, climate and/or climate properties, organisms like flora and fauna and human activities, relief settings, parent material, age, and spatial coordinate n) models based on machine learning techniques (McBratney et al. 2003). The STFs models link the soil properties measured by conventional laboratory analysis (dependent variables) and the VNIR-SWIR spectra (predictor variables). The SCORPAN models link the soil properties measured by conventional laboratory analysis (dependent variables) and several environmental variables with the potential to explain the dependent variables (e.g., climatic data, topographic data, VNIR-SWIR spectra and spectral index). The STFs models are mainly based on multivariate regression methods such as the partial least squares regression and support vector machine (Gomez et al. 2008; Stevens et al. 2010). The SCORPAN models are mainly based on random forest and boosted regression trees (Wang et al. 2018). SFTs functions are developed and applied only for bare soils as soil surface components (such as the dry and green vegetation) affect imaging VNIR-SWIR measurements (Bartholomeus et al. 2011). SFTs functions are therefore developed for croplands during ploughing periods (Ben-Dor et al. 2002) and arid ecosystems (Jarmer et al. 2010). By contrast, SCORPAN models which use environmental variables indirectly linked to the SOC content and stock, may be applied for croplands, rangelands, shrublands and grasslands. The SCORPAN models therefore have higher mapping coverage potential than SFTs models.
The SOC content is a key soil property associated with soil physical, chemical and biological
fertility. It is also related to soil structure and porosity maintenance. The first SOC content
mapping effort based on remotely sensed data and SFTs models was conducted over croplands in
Israel using reflectance data from an airborne hyperspectral sensor, the Digital Airborne Imaging
Spectrometer (DAIS) (Ben-Dor et al. 2002). Then, several studies have successfully mapped
SOC content at landscape and regional scales using VNIR-SWIR imaging data acquired by
multispectral satellite sensors (Wilcox et al. 1994; Jarmer et al. 2010), hyperspectral Hyperion
sensor aboard the Earth Observing-1 (EO-1) satellite (Gomez et al. 2008; Lu et al. 2013; Minu et
al. 2017), and hyperspectral airborne sensors (Selige et al. 2006; Hbirkou et al. 2012). These
studies had moderate to high performances in SOC content estimation, with Ratio of
Performance to Deviation (RPD) from 1.4 to 2 and coefficient of determination (R²) values from
0.5 to 0.8.

The SOC stock, the mass of C per unit area for a given depth (expressed in g C ha⁻¹), is a
function of SOC content (expressed in g C kg⁻¹ or %), bulk density (expressed in g cm⁻³) and
depth (expressed in cm) (Mishra et al. 2010). Some recent studies estimated SOC stock using
SFTs models with laboratory (Rossel and Hicks 2015; Guo et al. 2019), field (Cambou et al.
2016), and imaging (Guo et al. 2019) VNIR-SWIR data. Two approaches were proposed to
estimate SOC stocks with VNIR-SWIR spectroscopy. In a first approach, the SOC stock is
estimated directly using a STF which links a set of SOC stocks measured by conventional
laboratory analysis and a set of VNIR-SWIR spectra. In a second approach, each variable of
SOC stocks (SOM, soil bulk density) is estimated by SFTs and then the SOC stocks are
estimated using an equation which links the SOC stock to these variables (Mishra et al. 2010).
The estimation of SOC stock using laboratory VNIR-SWIR spectroscopy had reasonable
performance ($R^2$ = 0.65, RMSE = 3.27 Mg C ha⁻¹). They also obtained moderate performances of SOC stock estimation using VNIR-SWIR imaging data ($R^2$ = 0.42, RMSE = 3.6 Mg C ha⁻¹).

Recent studies have mapped SOC content and stock using SCORPAN models and VNIR-SWIR satellite imagery data. For example, Wang et al. (2018) showed that the use of seasonal fractional cover data derived from VNIR-SWIR imagery data in association with classical environmental predictors (climate, lithology, relief and weathering covariates) improved the performance of SOC stock prediction. The VNIR-SWIR imagery data used for the estimation of SOC stock are mainly provided by MODIS (Mishra et al. 2010; Gray et al. 2015) and Landsat satellite sensors (Were et al. 2015; Yang et al. 2015a; Grinand et al. 2017; Schillaci et al. 2017; Wang et al. 2018). The VNIR-SWIR imagery from MODIS was also used for SOC content mapping (Ratnayakea et al. 2016; Somarathna et al. 2016). VNIR-SWIR imagery data can be used as spectral bands (Were et al. 2015; Ratnayakea et al. 2016; Grinand et al. 2017) or to generate biophysical variables or VIs such as percent vegetation cover (Gray et al. 2015) and NDVI (Mishra et al. 2010; Ratnayakea et al. 2016; Somarathna et al. 2016; Schillaci et al. 2017). These studies exhibited large variability in the performance of SOC stock estimation, with $R^2$ = 0.4 and RMSE = 3 Mg C ha⁻¹ in semi-arid rangelands of eastern Australia (Wang et al., 2018) and $R^2$ = 0.72 and RMSE = 14.4 Mg C ha⁻¹ in southeastern Madagascar dominated by forest, crop, and pasture (Grinand et al. 2017). Long-term satellite data (e.g., MODIS) can also be used to examine the trends in SOC (or SOM) at regional scales (Chen, Chang, Xiao et al. 2019).

## 6. Remote sensing of disturbance impacts on the terrestrial carbon cycle

Ecosystems can exhibit a range of responses to disturbances depending on disturbance type, severity (damage per area), timing, and ecosystem type, and these responses govern the type and
success of remote sensing methods employed. Key milestones for using remote sensing to study
disturbance effects on C cycling began with the early use of satellite imagery to document
disturbed areas, including deforestation (Stone et al. 1983) and wildfire (Turner et al. 1994). The
development of global time series of VIs with AVHRR (Los et al. 1994) allowed the
combination of satellite data with C cycle models (e.g., CASA) (Field et al. 1995) and longer
studies of disturbance effects over large areas (Hicke et al. 2003). The availability of free
Landsat imagery facilitated the assembly of large-scale, fine-resolution disturbance datasets
(Masek et al. 2008; Hansen et al. 2013) as well as disturbance datasets key for C studies such as
burned areas from the Monitoring Trends in Burn Severity (MTBS) project (Eidenshink et al.
2007). The development and release of MODIS products (e.g., LAI, GPP/NPP) have provided
useful data globally in studying C cycling. More recent significant advances include the use of
lidar data to estimate C stocks with high certainty and the use of remote sensing to scale net C
fluxes from EC flux towers. Remotely sensed data offer substantial opportunities to incorporate
disturbance information into C cycle studies (Frolking et al. 2009; McDowell et al. 2015).

Multiple attributes of disturbance (e.g., severity, timing) and C cycling (fluxes and stocks) can be studied with remote sensing. The area of disturbance is an important attribute (Senf et al. 2017) and can include damage to ecosystems (e.g., stress or canopy damage) or plant mortality. Widespread plant mortality is the largest disturbance effect on the C cycle and therefore one of the easiest to study with remote sensing. The loss of canopy (fires, storms, insects, disease, harvest) or change in needle or leaf color (fires, insects, disease) allows quantification of disturbance extent and severity. Changes in vegetation or soil color following burning also facilitates detection. Substantial (wildfires) or more subtle (insects, drought) changes in canopy temperature during and after disturbances may aid with detection. Disturbances across large
regions that include a mix of disturbance types have been mapped (Masek et al. 2008; Cohen et al. 2016). More muted ecosystem responses and therefore decreased accuracy can result from lower mortality severity (e.g., background tree mortality). Finer resolution data (e.g., Landsat and finer) are better for detecting smaller disturbances than coarser resolution imagery (e.g., AVHRR, MODIS), but data processing and storage requirements are substantially greater, precluding many analyses over broad regions. Remote sensing of disturbances that do not cause vegetation mortality (e.g., low or moderate drought or defoliation) is challenging. To address these challenges, methods that detect disturbances using imagery time series have been employed (Jepsen et al. 2009; Meigs et al. 2011a).

Quantifying the magnitude of vegetation response or area of damage within a pixel provides additional information beyond a binary classification of disturbed/undisturbed. Often, information on disturbance type and therefore the response of vegetation is available from expert knowledge or ancillary data (e.g., aerial surveys documenting insect and disease damage or burned area datasets) and can aid the estimation of the fraction that a pixel is disturbed. Drought typically has a relatively large spatial footprint, and most vegetation within a satellite pixel is likely to experience similar stress. Some insect outbreaks (e.g., bark beetle outbreaks) kill trees, and therefore anomalous signals (from predisturbance conditions) can be translated into an estimate of the number of trees killed (Meddens and Hicke 2014). The MTBS dataset classifies burn severity into low, moderate, and high using Landsat imagery (Eidenshink et al. 2007). Other disturbance types, such as insect defoliation, prove more challenging to determine damage severity because a given reflectance signal may result from widespread low damage or substantial damage over a smaller area. The amount of damage within a pixel also determines the detectability of a disturbance. Meddens et al. (2013) found that accuracy of classifying trees
killed by bark beetles increases with fraction of pixel occupied by killed trees. Tradeoffs exist between spatial resolution and extent: very fine spatial resolution imagery (e.g., 1-2 m) can detect individual killed trees but have limited extent (e.g., Hicke and Logan 2009), whereas coarse-resolution imagery requires larger disturbances within a pixel but has global extent.

The timing and evolution of a disturbance are other important attributes. Repeated remotely sensed observations of a disturbance are often available either on a regular basis or for select times. Temporal resolutions can range from daily to yearly depending on the revisit time of the sensor and objective of the study. For example, active fire and burned area maps are produced globally as both daily (MOD14A1 and MYD14A1) and monthly (MCD64A1) MODIS products, respectively (Justice et al. 2002), drought monitoring is available at approximately two-week intervals (Hargrove et al. 2009), and insect outbreaks have been mapped at annual resolution (Meddens and Hicke 2014). Continental (Zhao et al. 2018a) and global (Hansen et al. 2013) time series maps of changes in forest cover have been produced from Landsat imagery. Spatiotemporal dynamics of individual events, or parts of events, can also be studied with remotely sensed data. Landsat time series are particularly valuable for slow-moving disturbances such as insect or disease outbreaks (Meigs et al. 2011a; Meddens and Hicke 2014). Remote sensing data are useful for estimating not only the immediate effects of disturbance but also the recovery rates of stocks and fluxes. For example, recovery of GPP following wildfires (Hicke et al. 2003) and bark beetle outbreaks (Bright et al. 2013) were studied using LUE models along with annual time series of AVHRR and MODIS imagery, respectively.

The impacts of disturbances on C fluxes can be estimated by combining satellite observations with models. LUE models have been used to compare predisturbance GPP and NPP estimated with satellite reflectances to postdisturbance values, including monitoring recovery
rates (Hicke et al. 2003). A bark beetle outbreak caused a 5-26% decrease in MODIS-derived
GPP, depending on mortality severity (Bright et al. 2013). Global fire emissions (C fluxes from
combustion) were estimated to be 2.2 Pg C year⁻¹ during 1997-2016 by combining satellite-
driven model estimates of C stocks and satellite-derived maps of burned area (van der Werf et al.
2017) **(Figure 11a)**. Net ecosystem fluxes following disturbances can also be estimated by
upscaling EC flux tower data using satellite data (Xiao et al. 2011a; Schwalm et al. 2012). For
example, hurricane Katrina led to a net carbon release into the atmosphere in the Gulf Coast
region impacted by the disturbance (Xiao et al. 2011a) **(Figure 11b)**. The early 2000s drought in
western North America reduced the regional C sink by 30-298 Tg C yr⁻¹ (Schwalm et al. 2012),
and the 2006 drought had a greater effect across the conterminous US (Xiao et al. 2011a)
**(Figure 11c)**. Mechanistic ecosystem models can quantify modifications to C cycling following
disturbance by using satellite-derived information on the extent and timing of disturbances
(Meigs et al. 2011b).

Estimating the amount of C stocks within disturbance areas has been accomplished with
remote sensing in multiple ways (Frolking et al. 2009). Bright et al. (2012b) overlaid airborne
optical imagery (for mapping disturbance area) on airborne lidar data (for estimating C stocks) to
quantify the amount of C in lodgepole pines killed by mountain pine beetles **(Figure 11d)**.
Satellite imagery is needed for larger disturbance areas. Chambers et al. (2007) estimated the
number of trees and associated C stocks affected by hurricane Katrina using spectral mixture
analysis and Landsat and MODIS imagery. By combining Landsat imagery and field
observations, Huang et al. (2010) estimated that drought-induced mortality of piñon-juniper
woodlands resulted in a loss of 4.6 Tg C in an area in the southwestern US in the early 2000s.
Hicke et al. (2013) used Landsat-based MTBS burn severity data and insect data from aerial
surveys in the western US together with a biomass map produced using MODIS imagery (Blackard et al. 2008) to estimate the amount of C in trees killed by wildfires and bark beetle outbreaks.

<figure><img src="image_12.png" /><figcaption>(b)</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>(c)</figcaption></figure>

<figure><img src="image_14.png" /><figcaption>(d)</figcaption></figure>

<figure><img src="image_15.png" /></figure>

**Figure 11.** Illustration of remote sensing applications to assess carbon cycle impacts of different disturbance types with different methods across a range of spatial scales. (a) Use of satellite imagery and carbon cycle modeling to estimate mean annual combustion emissions from wildfires (g C m⁻² yr⁻¹),
1375 1997-2016 (van der Werf et al. 2017). (b) Anomalies of annual NEE in 2006 relative to the 6-year period
1376 2001–2006, indicating that hurricane Katrina led to a net C release into the atmosphere (Xiao et al. 2011a).
1377 The white lines indicate the isotachs, including tropical storm (TS), hurricane category 1 (C1), and
1378 hurricane category 2 (C2). (c) Drought effects (right column; precipitation anomalies from 1970-1999
1379 means, mm) on annual NEE (left column; g C m⁻² yr⁻¹) in 2002 (top row) and 2006 (bottom row)
1380 estimated by upscaling eddy flux covariance measurements with satellite remote sensing (Xiao et al.
1381 2011a). (d) Integration of lidar data and aerial imagery to estimate the percentage of carbon stocks in trees
1382 within an area of a mountain pine beetle outbreak (Bright et al. 2012b).

1383

1384 Different sensors provide different capacities for studying disturbance effects on the C cycle.
1385 Sensors in the visible through shortwave infrared portions of the electromagnetic spectrum (e.g.,
1386 Sentinel, Landsat TM/ETM+/OLI, AVHRR, and MODIS), are useful for quantifying the extent,
1387 severity, and timing of disturbances. Such sensors can detect changes in greenness as represented
1388 by NDVI, decreases in water content following plant stress or death, or changes in canopy (leaf
1389 area or color). Some of these satellites/sensors (e.g., Landsat, AVHRR, MODIS) have long time
1390 records, facilitating the study of a longer disturbance or ecosystem recovery. For instance,
1391 Meddens and Hicke (2014) used 16 years of Landsat imagery to document the spatiotemporal
1392 characteristics of a mountain pine beetle outbreak in Colorado, and Hicke et al. (2003) quantified
1393 NPP responses following using 17 years of AVHRR data. Emitted longwave radiation can also
1394 be used to identify disturbances. Plant stress and mortality results in a reduction or loss of
1395 cooling associated with transpiration, thereby raising surface temperatures (Heller 1968;
1396 Hesslerová et al. 2018). The MODIS LST product was combined with EVI to produce a
1397 Disturbance Index that takes advantage of this change in surface temperature (Mildrexler et al.
1398 2007). Because of nonlinear relationships between canopy closure and biomass (or C) as well as
confounding influences of green understory, challenges exist for using shortwave reflectances to estimate biomass following disturbances. Lidar data can facilitate biomass estimates (Frolking et al. 2009). Returns from ground-based, airborne, and space-based lidar systems (in the visible and NIR portions of the electromagnetic spectrum) has been used to estimate canopy height and volume (Dolan et al. 2011), and the resulting biomass estimates along with disturbance area can then be used to quantify the loss of C stocks (Bright et al. 2012a). For example, C stocks and recovery were assessed by mapping fires with Landsat imagery and estimating biomass with airborne lidar (Bolton et al. 2015).

Despite the significant contribution of remote sensing to advances in the understanding of disturbance impacts on the C cycle, important challenges remain. Maintaining the continuity of existing long-term satellite records, especially Landsat and MODIS, is critical for providing additional understanding of this topic. Studies of future disturbances, trends in disturbances and their changes through time, and recovery following disturbances will benefit from continuing these records. Refinement and development of global, wall-to-wall mapping of disturbances at 30-m spatial resolution and finer will increase the capability to separate disturbance types, leading to better estimates of immediate and legacy impacts on the C cycle. Imagery at fine spatial resolution and broad spatial extent is needed to map diffuse, low-severity disturbances that are nevertheless widespread across the landscape, such as background tree mortality (van Mantgem et al. 2009) and disease outbreaks (Sturrock et al. 2011). Continued development of translating disturbance area into C cycle variables (stocks and fluxes) is needed.

# 7. Uncertainty and validation of carbon flux and stock estimates

## 7.1. Uncertainty
Estimates of C fluxes and stocks based on remote sensing can have significant uncertainty. One important source of uncertainty in C flux/stock estimates is the uncertainty of the remote sensing data products. For example, the satellite-derived surface reflectance, vegetation indices, SIF, and LAI have several sources of uncertainty such as atmospheric effects (e.g., Rayleigh scattering, ozone, aerosol, and water vapor content), retrieval errors, cloud contamination, and sensor degradation (van Leeuwen et al. 2006; Fang et al. 2012). Besides uncertainty associated with remote sensing data, C fluxes and/or stocks estimated from modeling approaches have two other main sources of uncertainty: model structure (e.g., incomplete or flawed underlying processes and assumptions) and model parameters (e.g., imperfectly or poorly defined parameters due to lack of information). For example, the uncertainty in meteorological data (Zhao et al. 2006), satellite-derived LAI data (Liu et al. 2018b), and land cover maps (Quaife et al. 2008; Xiao et al. 2011b; Zheng et al. 2018) can lead to significant uncertainty in regional C fluxes from LUE or diagnostic models. The uncertainty of model parameters can also lead to significant uncertainty in C fluxes in LUE and diagnostic models (Hilton et al. 2014; Xiao et al. 2014b). The biomass estimates have several sources of uncertainty: biases in field sampling, errors in plot locations, measurement errors in DBH and height, misrepresentation of allometric relationships or processes in models, errors in remotely sensed data, and imperfect retrieval methods (Lu et al. 2012). Friedl et al. (2001) attributed the uncertainty of remotely sensed data to three main sources: (1) errors introduced during the acquisition process of the observations, (2) errors generated by the processing of the remotely sensed data; (3) errors resulting from the scale mismatch between the grid cells of remotely sensed data and the scale of the ecological variables or processes of interest on the ground.
The uncertainty in remote sensing data products and the resulting C flux estimates can obscure the analysis or comparison of the magnitude, interannual variability, and long-term trends in vegetation productivity and C dynamics. As mentioned earlier, satellite-derived VIs are widely used as proxies of GPP, while LUE models, machine learning approaches, and diagnostic process-based models are more routinely used to quantify GPP. We used the GIMMS3g NDVI dataset based on AVHRR data (Pinzon and Tucker 2014), the EVI dataset based on MODIS data (Huete et al. 2002), the MODIS GPP product based on a LUE model (Running et al. 2004), GPP data from a diagnostic process-based model – BEPS (Liu et al. 2018b), and GPP data from a machine learning approach (EC-MOD) (Xiao et al. 2014a) to examine the trends in vegetation productivity in China from 2000 to 2014 (Figure 12). All these data products exhibited increasing trends, indicating that vegetation productivity had been increasing due to various factors (e.g., climatic warming, CO₂ fertilization, afforestation, and improved agricultural management practices). Despite the general agreement in the trends, the rate of increase was different between the two NDVI products (GIMMS3g NDVI and MODIS EVI) and among different GPP products (MODIS-GPP, BEPS, and EC-MOD), and there were also large discrepancies in total annual GPP among the three GPP products. Reducing the uncertainty in various remote sensing data products is essential for better understanding the dynamics of ecosystem C fluxes.
<figure><img src="image_16.png" /></figure>

1462

1463 **Figure 12.** Trends of satellite-derived vegetation indices (VIs) and estimated GPP for China over the period 2000-2014. The VIs consist of GIMMS3g NDVI and MODIS EVI. Mean values averaged throughout the year are used for both NDVI and EVI. The EVI is multiplied by 2 for displaying purposes.

1464
1465
1466 The total annual GPP estimates are from a data-driven (or machine learning approach) – EC-MOD, a
1467 LUE model (MODIS-GPP), and a diagnostic process-based model – BEPS. The curves with symbols
1468 stand for annual mean NDVI, EVI, or GPP. The lines stand for the fitted trend lines: NDVI (y=0.00057x-
1469
1470 R²=0.62, p<0.001), EC-MOD (y=0.058x-110.13, R²=0.59, p<0.01), and BEPS (y=0.017x-28.93, R²=0.31,
1471 p<0.05).
1472

1472
1473
1474 Similarly, the C stock maps based on different remote sensing data and approaches can exhibit significant discrepancies. For example, several forest AGB maps have been developed for the conterminous U.S. (Figure 13). Some maps are solely based on optical remote sensing data.
1475
1476 For example, Zhang and Kondragunta (2006) developed an AGB map for North America using MODIS data streams (e.g., LAI, land cover, vegetation continuous fields) and foliage-based
1477 generalized allometric models. Blackard et al. (2008) developed an AGB map with 250 m
resolution for the U.S. from MODIS data (surface reflectance, NDVI, and percent tree cover),
topographic variables, climate, and plot-level biomass data from the U.S. Forest Service's Forest
Inventory Data (FIA) Program using a machine learning approach. Some other maps are based
on optical and microwave remote sensing data. Wilson et al. (2013) developed a forest AGB map
using FIA field data, environmental factors (e.g., temperature, precipitation), MODIS-derived
vegetation phenology, and tree cover data based on Landsat. Some other studies, however,
combined SAR and/or lidar data with optical remote sensing data. For example, Kellndorfer et
al. (2013) developed a forest AGB map using biomass data from the USDA Forest Service FIA
Program, high-resolution InSAR data from the Shuttle Radar Topography Mission (SRTM), and
optical remote sensing data from Landsat ETM+ using an empirical modeling approach. The
NASA-CMS map (Saatchi et al. 2012) is based on a multi-scale approach along with a variety of
geospatial layers (e.g., ICESat GLAS, MODIS, PALSAR, climate, topography, Landsat
disturbance, and Landsat LAI). The ICESat GLAS waveforms were linked to FIA-measured
biomass via the Lorey's Height metric, and the biomass converted from waveforms were then
used as training data for statistical models of biomass (Saatchi et al. 2012). Four maps (**Figure 13a-d**) had the highest AGB in the Pacific Northwest and intermediate values in the Appalachian
Mountains and the Rocky Mountains regions, while the Zhang and Kondragunta map had similar
AGB values in the Pacific Northwest and the Appalachian Mountains. The CMS map had higher
AGB in the eastern U.S. and the Rocky Mountains than the other four products. The total AGB C
stock ranged from 17.2 to 28.3 Pg C (**Figure 13f**). The Zhang and Kondragunta product had the
highest total C stock, and the CMS product also had much higher C stock than the other
products. Quantifying and reducing the uncertainty in remote sensing C stock products are
essential for assessing regional or global C storage and informing management and climate
policymaking.

<figure><img src="image_17.png" /><figcaption>**Figure 13.** Forest aboveground biomass (AGB) carbon stock of the conterminous U.S. based on different datasets: (a) the Blackard et al. map; (b) the Carbon Monitoring System (CMS) map; (c) the Kellndorfer et al. map; (d) the Wilson et al. map; (e) the Zhang et al. map; (f) the total forest AGB carbon stock of the five products.</figcaption></figure>
Quantifying and reducing uncertainty in C flux and stock estimates remain a grand challenge. It is important to quantify the full uncertainty of these estimates by considering various sources of uncertainty. Future efforts are needed to pursue the quantification of the full uncertainty in C flux and stock estimates. Meanwhile, it is equally important to reduce the uncertainty in the estimates of C fluxes and/or stocks by using remotely sensed data with higher quality and finer resolution and better approaches.

## 7.2. Validation

The remote sensing of the terrestrial C cycle typically requires calibration and validation. Both in situ measurements and satellite/airborne data have been used to assess the accuracy of the C flux/stock estimates. C flux estimates have been typically validated using in situ measurements such as flux data from eddy covariance flux towers (Heinsch et al. 2006; Liu et al. 2018b) and NPP measurements. EC flux measurements from flux networks (e.g., FLUXNET, AmeriFlux, Fluxnet-Canada, EUROFLUX, USCCC) (Wofsy et al. 1993; Baldocchi et al. 2001; Xiao et al. 2013) are perhaps the most accurate and most widely used data for validating carbon flux estimates based on remote sensing methods. For example, flux tower data from AmeriFlux and Fluxnet-Canada were used to develop and validate the data-driven approach based on MODIS data streams for the generation of the gridded, 1-km carbon flux product (GPP, ER, and NEP) over North America (Xiao et al. 2014a) (Figure 14). Besides the flux networks (e.g., AmeriFlux and FLUXNET), measurements have from other networks have also been to validate carbon fluxes. The Spectral Network (SpecNet) integrates remote sensing with carbon flux measurements (Gamon et al. 2010). The ES1309 (Innovative optical Tools for proximal sensing of ecophysical processes or OPTIMISE) Cost Action network aims at exploring optical observations from drones and airborne sensors with high spectral, spatial, and temporal resolutions to explore
the links among light use, plant physiology and ecosystem functioning and to validate satellite missions such as Sentinel and the proposed FLuorescence EXplorer (FLEX) missions. The C stock (AGB or SOC) estimates are typically validated using plot-based C stock data (Lefsky et al. 1999; Blackard et al. 2008; Sun et al. 2011).

<figure><img src="image_18.png" /><figcaption>**Figure 14.** Cross-validation of a data-driven upscaling approach based on MODIS data streams for the development of gridded, 1-km carbon fluxes over North America with flux tower data from AmeriFlux and Fluxnet-Canada (Xiao et al. 2014a). Top panels: GPP for forests and non-forests; middle panels: ER</figcaption></figure>
for forests and non-forests; bottom panels: NEP for forests and non-forests (p < 0.001 for all cases). The units of GPP and RMSE are g C m⁻² d⁻¹.

However, the observations from satellite instruments often have medium to coarse resolution ranging from a few hundred meters to tens of square kilometers. Therefore there could be a moderate to large scale mismatch between the footprint of in situ measurements and the grid cell of satellite data, which makes it challenging to directly evaluate remotely sensed data using in situ measurements. For example, the ground area that a MODIS grid cell stands for is much larger than a field plot for in-situ LAI measurements, and to evaluate the MODIS-based LAI data products, gridded LAI estimates derived from finer-resolution satellite imagery (e.g., Landsat) have been used to bridge the gap between in situ measurements and MODIS-based estimates. A field plot is close to the grid cell of finer-resolution satellite data (e.g., Landsat) or airborne data, and the in situ measurements are used to calibrate/validate the satellite (or airborne) estimates that will then in turn be used to evaluate the estimates from medium- or coarse-resolution satellite data. This kind of calibration and validation framework was proposed to evaluate multiple MODIS data products (e.g., GPP/NPP, land cover, LAI) using field measurements in the Bigfoot Project (Cohen et al. 2006) (Figure 15). For example, the MODIS GPP and NPP products were validated using 30-m resolution GPP and NPP estimates driven by the Biome-BGC model and Landsat-derived LAI data (Turner et al. 2006). High-resolution satellite data and fine-resolution airborne and/or UAV data can bridge the gap between field plots or flux towers and satellite grid cells and help evaluate and validate satellite-derived C flux/stock estimates.
<figure><img src="image_19.png" /><figcaption>**Figure 15.** A framework for calibrating and validating carbon flux and stock estimates derived from satellite remote sensing using in situ measurements. Finer-resolution satellite or airborne data can bridge the gap between in situ measurements and medium/coarse resolution satellite data. (Reproduced from Cohen et al. 2006)</figcaption></figure>

Field campaigns are an important component of NASA's Earth Observing System (EOS) and the evaluation and validation of remote sensing of the terrestrial C cycle. NASA has initiated a series of field campaigns over the last three to four decades. For example, the First ISLSCP (International Satellite Land Surface Climatology Project) Field Experiment (FIFE) (Sellers et al.
1988) was conducted at and around the Konza Prairie in Kansas in 1987 and 1989. The objectives of FIFE were to understand the processes regulating the exchange of energy, water, and carbon between the land surface and the atmosphere, develop and test remote sensing methods, and understand how to scale information and processes from the pixel or ecosystem level to regional scales. The Boreal Ecosystem-Atmosphere Study (BOREAS), a joint U.S.-Canadian effort, had field deployments in 1994 and 1996, aimed at quantifying the exchanges of carbon, water, and energy between the boreal forest and the atmosphere (Sellers et al. 1995). BOREAS collected and compiled remote sensing and ground-based (e.g., fluxes) observations. The Large Scale Biosphere-Atmosphere Experiment in Amazonia (LBA), one of the largest coordinated scientific endeavors in the tropics, started in 1998 (Nobre et al. 2001). One of the themes of LBA is carbon storage and exchange, and one of the strategies is to scale processes from small scales to the entire basin scale using models and remote sensing. The latest NASA field campaign is perhaps the Arctic-Boreal Vulnerability Experiment (ABOVE) in Alaska and western Canada that started in 2015 and is planned to last for 8 to 10 years (Kasischke et al. 2010). ABOVE seeks a better understanding of the vulnerability and resilience of arctic and boreal ecosystems to the changing environment. These field campaigns can help develop and refine methods for spatial and temporal scaling of field observation and evaluating remotely sensed data products.

# 8. Prospects for remote sensing of the terrestrial carbon cycle

## 8.1. Remote sensing of carbon fluxes

Key challenges and opportunities with the use of satellite-derived VIs in approximating or estimating ecosystem C fluxes involve the extension of the long time series data record, amidst
ever increasing space and time fidelity of the satellite data, including reduced uncertainties in geolocation, radiometry, and snow and cloud detection (Tucker et al. 2005). Continuity and extension of the satellite VI data record and other land products has recently been provided by the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard NOAA-20, the first Joint Polar Satellite System (JPSS-1) launched in November, 2017 (Justice et al. 2013). The NASA/NOAA JPSS series will further extend our capabilities to examine long-term trends in ecosystem C fluxes and/or stocks. Geostationary data at 1 km pixel resolutions such as Himawari AHI (Advanced Himawari Imager) and GOES-R ABI (Advanced Baseline Imager) are now becoming widely available at sub-hourly time intervals and enabling improved precision of phenological time factors (e.g., budburst, full leaf expansion, dormancy) into ecosystem production models. Better understanding how flux tower data and VIs converge (e.g., Huang et al. 2019) will further reveal the underlying mechanisms of the C cycle (e.g., phenology and climate drivers) and help interpret satellite-derived greenness measures (Restrepo-Coupe et al. 2016).

Satellite phenology products, based on normalized bidirectional adjusted reflectances and a 2-band EVI (EVI2) have been developed for MODIS (MCD12Q2) and extended to VIIRS (Zhang et al. 2018a). There are also phenology metrics being derived from Landsat at 30 m spatial resolution (Melaas et al. 2016) enabling improved spatial-temporal scaling within heterogeneous landscapes, and ready upscaling of surface C measurements to larger landscape units and regional to global studies, which can constrain C cycle models. Ground-based phenocam networks can provide independent sources of land surface phenology information for comparisons and potential validation with satellite phenology products (Richardson et al. 2018).

Additionally, VIs can also play an important role in validating ecosystem responses to climate change. For example, Ichii et al. (2007) used an ecosystem model, BIOME-BGC, to
1615 determine which rooting depth could best estimate GPP based on the consistency between 1616 simulated GPP and satellite-based EVI seasonality and thereby to better simulate C, water, and 1617 energy cycles of tropical forests. Thus, although remote sensing can only directly monitor some 1618 topsoil properties, it can help parameterize models, and with extrapolated climatic data, make it 1619 possible to estimate whole ecosystem C exchange under conditions not yet encountered. 1620

1620 The relationships between SIF and VIs and their relationships with photosynthesis (e.g., Li et 1621 al. 2018b, 2018c) should be examined at various timescales to exploit the complementary use of 1622 VIs with SIF and to better understand the interplay between direct photosynthesis process 1623 measures (SIF) and photosynthetic capacity measures (VIs). Plants respond to the dynamics of 1624 environmental variables through stomatal closure and other diurnal adjustments that cannot be 1625 easily sensed by VIs, and are more likely captured by SIF measurements. The Orbiting Carbon 1626 Observatory-3 (OCO-3) recently launched on May 4, 2019 has the diurnal but temporally 1627 fragmented sampling capability and its measurements will help understand the diurnal cycles of 1628 SIF and photosynthesis. At longer time scales, plants may increase leaf area and their 1629 photosynthetic capacity under favorable environmental conditions, and reduce LAI under stress 1630 when leaves are expensive to produce and maintain. Thus, at longer time scales, there would be a 1631 convergence of satellite greenness signals with biologic and structural canopy properties. Future 1632 research is needed to reveal whether SIF is able to detect plant stress almost immediately and can 1633 be effectively used for temporal remote sensing of vegetation photosynthesis. 1634

1634 The discrete SIF soundings from OCO-2 and OCO-3 can be used to generate global, gridded- 1635 SIF estimates (e.g., GOSIF) (Li and Xiao 2019) with finer spatial and temporal resolutions using 1636 spatially and temporally continuous MODIS and meteorological data along with data-driven 1637 approaches. Moreover, newly operating and upcoming missions such as TROPOMI and
the FLuorescence EXplorer (FLEX), respectively, will provide gridded SIF observations. The TROPOMI instrument aboard Sentinel-5P that was launched in October 2017 provides continuous SIF retrievals at 0.2° spatial resolution globally (Guanter et al. 2015a; Kohler et al. 2018). The TROPOMI SIF product promises to lead a new breakthrough in the application of SIF for the monitoring of vegetation C fluxes. The FLEX that is anticipated to be launched in 2022 will map SIF globally at the finest spatial resolution (300 m) of all existing and upcoming spaceborne instruments (Drusch et al. 2017). In addition, a series of geostationary satellites are scheduled for launch in the 2019–2021 timeframe, and will provide unprecedented SIF measurements in sub-daily time intervals over some regions in Europe (Sentinel-4) and the Americas (TEMPO and GeoCARB). The variety of SIF datasets have the potential to open up a new era in mapping terrestrial photosynthesis. However, further work is needed in order to develop methods to estimate GPP from SIF observations. These models will have to deal with, at least, the effect of canopy structure effects on SIF data, the physiological relationships between SIF, photosynthesis and non-photochemical quenching, and the upscaling from instantaneous and clear-sky SIF data to the diurnal and all-sky conditions in which GPP estimates are typically reported.

The growing number of EC flux sites globally, especially more sites in under-represented ecosystems and regions, is anticipated to benefit the upscaling of C fluxes to regional to global scales based on machine learning approaches. A tremendous amount of new remotely sensed data can be used to upscale ecosystem C fluxes. For example, new satellite data products such as gridded SIF products derived from OCO-2 (e.g., GOSIF) (Li and Xiao 2019), gridded SIF observations from TROPOMI (Kohler et al. 2018) and FLEX, and gridded soil moisture products from the Soil Moisture Active Passive (SMAP) mission (Brown et al. 2013) will potentially
1661 improve the upscaling of flux observations. The integration of the Landsat archive and Sentinel
1662 data will greatly improve the frequency of cloud-free observations, which can make it possible to
1663 upscale C fluxes from towers to regional scales with finer spatial resolution (e.g., 30 m). The
1664 fusion between Landsat and Sentinel data with higher spatial resolution and MODIS data with
1665 more frequent coverage could lead to reflectance and VIs with higher temporal and spatial
1666 resolution that will help capture fast responses of vegetation to stress conditions (e.g., droughts)
1667 and thus the model capacity to capture the detailed seasonal variations and abrupt changes. Other
1668 potential directions in the machine learning approaches include how to better incorporate
1669 management and disturbance information and how to quantify and reduce the uncertainties in
1670 upscaled fluxes.

1671 The column CO₂ concentrations observed by satellite missions such as GOSAT, OCO-2, and
1672 GOSAT-2 are expected to be more frequently used to quantify NBP at regional to global scales,
1673 while reducing the uncertainty in the resulting NBP estimates is critical for assessing C budgets
1674 and informing climate policy. The launch of high-resolution imaging CO₂ sensors in a few years
1675 (Polonsky et al. 2014; Pinty et al. 2017) will allow monitoring the plumes of CO₂-rich or CO₂-
1676 depleted air. Anthropogenic emissions are a primary target but they will provide novel
1677 information about natural fluxes as well. However, they will further challenge the quality of the
1678 transport models that will have to properly simulate these plumes within the inverse systems.
1679 Merging atmospheric inversion with Numerical Weather Prediction data assimilation could be a
1680 natural way forward.

1681 Besides the satellite missions mentioned above, hyperspectral imagery from recently
1682 launched and forthcoming satellites is expected to benefit the estimation of C fluxes. The
1683 recently launched and forthcoming sensors, such as PRISMA (Prototype Research Instruments
1684 and Space Mission technology Advancement) (launched in March 2019) (Stefano et al. 2013),
1685 SBG (Surface Biology and Geology) (previously Hyperspectral InfraRed Imager, HyspIRI) (Lee
1686 et al. 2015a), and EnMap (Environmental Mapping and Analysis Program) (Guanter et al.
1687 2015b), will also produce an increasing amount of hyperspectral data with spatial resolution of
1688 20-30 m, 30 m, and 60 m, respectively. In addition, new CubeSat-type constellations of dozens
1689 or hundreds of small satellites in orbit will potentially provide land observations with high spatial
1690 and temporal coverage that will be useful to C cycle studies, although the processing and inter-
1691 calibration of these observations can be a challenge.

1692 Integrating satellite-based data products and process-based models will continue to be an
1693 effective way to improve our understanding of terrestrial C cycle processes. First, satellite-based
1694 data products need to be improved by reducing their uncertainties. Second, some key variables
1695 such as disturbance history, which we currently face difficulties to estimate globally, will benefit
1696 process-based modeling. Third, methods of model and data integration, including model
1697 parameter optimization and data assimilation, need to be further explored. These model and data
1698 integration studies mostly rely on a single satellite-based product. Use of multiple satellite-based
1699 products can likely refine terrestrial process-based models. Currently, many terrestrial process-
1700 based prognostic models are used to evaluate global terrestrial C budget (Le Quere et al. 2017;
1701 Sitch et al. 2015) and show large model-by-model differences in estimated terrestrial C fluxes.
1702 With the aid of improved and new satellite-based terrestrial products and integration techniques
1703 with process models, these model-by-model differences can be potentially reduced.

## 8.2. Remote sensing of carbon stocks

1704 Passive optical remote sensing, microwave remote sensing, and lidar remote sensing will
1705 continue to be used for AGB estimation. Passive optical remote sensing will continue to be one
1707 of the most important data sources for AGB estimation because of its high spatial and temporal resolution and spatially and temporally continuous coverage. Recently, the NASA Harmonized Landsat and Sentinel-2 (HLS) product has been operationally produced in near real time by combining Lansat-8 and Sentinel-2 observations (Claverie et al. 2018), which allows us to monitor vegetation dynamics at a 30-m field scale with an interval of fewer than 5 days across the globe. Thus, the HLS time series could be used to extract an optimal time series for estimating AGB, which will enhance the capability of consistent AGB estimation from local to global scales. However, the establishment of empirical models or deep machine learning algorithms based on passive optical remote sensing requires a large set of reliable training data representing a broad range of biomass values, while such training data are usually insufficient at regional or global scales. There is a need for the generation of large AGB samples from in situ measurements, very high-resolution passive remote sensing data (e.g., QuickBird, IKONOS, and UAVs), and lidar measurements. Combining HLS data, satisfactory training samples, and deep machine learning algorithms will significantly improve the performance of AGB estimation. Further, saturation issue in biomass estimation using passive optimal remote sensing is expected to be at least partly solved by the fusion of passive optical remote sensing with lidar and microwave remote sensing data.

1724 It has been nearly three decades since the SAR data were initially used for the estimation of forest AGB. The accurate mapping of forest biomass with SAR data at regional scales faces three challenges: saturation problems, terrain effects, and scale issue between the size of field sampling plot and the basic unit of regional mapping of forest AGB. The saturation problem in biomass estimation using radar backscatter intensity may be improved or solved by incorporating forest spatial structure information derived from InSAR data (Sun et al. 2011) or other data such
as optical stereo images (Zhang et al. 2017). Using the three-dimensional information derived
from InSAR, Radargrammetry, and optical stereoscopy to estimate forest height or to improve
the biomass estimation from SAR data will remain an active study area. For example, the digital
surface model (DSM) from TanDEM-X data can be used to monitor forest biomass changes
(Karila et al. 2019). If the digital terrain model (DTM) exists, the InSAR DSM can be used to
generate canopy height model (CHM) or biomass maps. NASA's NISAR, to be launched in 2021,
will provide global L-band InSAR data for biomass mapping and forest disturbance monitoring.
As mentioned earlier, the PolInSAR or TomoSAR data, especially at L and P bands, can be used
to retrieve the 3D structure of forest canopy directly without DTM. With the launch of TanDEM-
L and BIOMASS (Quegan et al. 2019), both scheduled in 2022, the PolInSAR and TomoSAR
data will be widely available to users, and therefore the effects of complex terrains on PolInSAR
or TomoSAR data can be further explored. The terrain effect is a barrier for accurate biomass
estimation based on SAR data. The development of theoretical models and simulations will
likely help identify a proper solution to terrain effects (Ni et al. 2018). One solution to the scale
issue could be the synergy of SAR data with other types of data such as lidar (Qi and Dubayah
2016) and stereo imagery (Zhang et al. 2017).

Lidar sensors and systems have evolved dramatically. TLS is becoming more portable and is
being integrated with mobile platforms (e.g., mobile laser scanning or MLS, backpack laser
scanning or BLS). ALS data have the tendency to be integrated with data from other sensors like
hyperspectral sensors (Asner et al. 2007; Kampe et al. 2010; Cook et al. 2013; Pang et al. 2016).
With the decrease of the data acquisition cost, ALS is being applied for biomass estimation
(Price et al. 2017) or sampling at large scales (Matasci et al. 2018). With more spaceborne lidar
systems on-orbit, the expendability and generalizability of the AGB estimation models based on
lidar data will need more consideration for operational regional or global biomass mapping efforts. The recently launched GEDI will provide estimates of forest biomass over broad regions.

The GEDI will produce high resolution laser ranging observations of the 3D structure of the Earth using a geodetic-class lidar laser system with a 25 m footprint. The integration of spaceborne lidar, SAR, and passive optical remote sensing data is anticipated to provide seamless, more accurate AGB estimates at the global scale.

Passive optical remote sensing will continue to be used for the mapping of SOC at landscape to regional scales. The recent launch of the Sentinel-2A and -2B satellite sensors by the European Space Agency (Baillarin et al. 2012) will produce an increasing amount of multispectral VNIR-SWIR data over the world with high revisit frequency (5 days) and spatial resolution from 10 to 20m, which could be used in SCORPAN models for SOC content and stock mapping. This high revisit frequency would allow the acquisition of Sentinel-2 data during both periods either with adequate vegetation coverage and periods with high coverage of bare soils. Hyperspectral imagery from recently launched and forthcoming satellites is expected to benefit the estimation of SOC at regional scales. The launch of forthcoming sensors, such as PRISMA (Stefano et al. 2013), SBG (Lee et al. 2015a), and EnMap (Stufferl et al. 2007) satellite sensors will also produce an increasing amount of hyperspectral VNIR-SWIR data with spatial resolution of 20-30 m, 30 m, and 60 m, respectively, which could be used in SFTs models for SOC content and stock mapping.

## 8.3. Quantifying and reducing uncertainty in carbon flux and stock estimates

Uncertainty information of carbon fluxes is essential for carbon cycle studies. First, modeled fluxes with uncertainty bounds can facilitate the direct comparison between modeled and observed fluxes/stocks. Second, the quantification of uncertainty will also facilitate the
1776 intercomparison of different approaches (e.g., modeling, inventory, atmospheric inversions).
1777 Third, the availability of uncertainty information will help interpret the dynamics in carbon
1778 fluxes/stocks. Fourth, a better understanding of the sources of uncertainty can provide insight on
1779 future improvement of ecosystem approaches and input data streams. Finally, better
1780 quantification of the uncertainty in C flux/stock estimates is essential for sound climate and
1781 decision-making. Estimates of C fluxes and stocks based on remote sensing could have
1782 significant uncertainties, while quantifying and reducing these uncertainties still remain a grand
1783 challenge. Future efforts are needed to account for various sources of uncertainty and thereby to
1784 quantify the full uncertainty in C flux and stock estimates. Meanwhile, it is equally important to
1785 reduce the uncertainty in the estimates of C fluxes and/or stocks by using remotely sensed data
1786 with higher quality and finer resolution and better approaches.

1787 **8.4. Long-term trends in carbon fluxes and stocks**

1788 Recent decades have witnessed dramatic changes in the Earth's climate, including a dramatic rise
1789 in air temperature, elevated atmospheric CO2, enhanced nitrogen (N) deposition, and increasing
1790 drought frequency and severity (IPCC 2013). Rapid changes in land cover/land use, including
1791 deforestation, afforestation, and urbanization, have also occurred. These changes have
1792 substantially altered ecosystem function and processes. For example, there is compelling
1793 evidence that many parts of the northern middle and high latitudes have been exhibiting
1794 "greening" trends since the early 1980s (Zhou et al. 2001; Xiao and Moody 2005; Saleska et al.
1795 2016). Evidence of the greening is mainly from the NDVI record derived from AVHRR on board
1796 NOAA's polar-orbiting satellites. The satellite-derived greening signal has been widely
1797 interpreted as evidence that ecosystem productivity and net C uptake have been increasing in
1798 many parts of the Earth's land surface. Despite these efforts, a more recent study based on long-
term satellite and FLUXNET records found that there were no trends in spring and autumn
phenology in the northern hemisphere during the warming hiatus (Wang et al. 2019). More
studies are needed to reveal the long-term trends in carbon fluxes. Meanwhile, a mechanistic
understanding of the observed trends is also needed. Satellite observations have also been used to
assess the long-term trends in terrestrial biomass (Liu et al. 2015). Inter-calibration among
different sensors and bias correction for orbital drift and sensor degradation are critical for long-
term analyses in remote sensing data. Inter-calibration among sensors and bias correction are
essential for ensuring a high-quality, long-term GIMMS3g NDVI record (Pinzon and Tucker
2014). The radiometric degradation of MetOp-A GOME-2 over its lifetime has led to declining
SIF (Joiner et al. 2016), which has hindered long-term analyses of SIF. The lengthening of the
satellite records and the addition of new satellite observations will further extend our capabilities
to examine long-term trends in ecosystem C fluxes and/or stocks and their responses and
feedbacks to the climate.

## 8.5. Geospatial processing platforms facilitating remote sensing of the terrestrial carbon cycle

Geospatial processing platforms that provide high performance computing and massive storage capacity are anticipated to play an important role in remote sensing of the terrestrial C cycle in the future. Google Earth Engine is perhaps the most advanced cloud-based geospatial processing platform in the world that is currently available to the remote sensing research community and the public (Gorelick et al. 2017). This platform allows the users to perform highly-interactive algorithm development and develop data products at the global scale. It makes a tremendous amount of widely-used data (e.g., Landsat, MODIS) available so that the users do not need to download or store these data. Downloading satellite data for multiple years for the globe could
be a formidable task to an individual scientist for the medium resolution (e.g., 1 km), not to mention finer spatial resolution (e.g., 30 m). With the massive computing and storage capabilities, various built-in functions, and tremendous amount of remote sensing and ancillary (e.g., climate) data, Google Earth Engine and potentially other geospatial processing platforms are anticipated to greatly facilitate planetary-scale remote sensing of the terrestrial C cycle.

## 8.6. Synergy and integration of remote sensing data in carbon cycle studies

Remotely sensed data from different platforms/sensors have been synergistically used. For example, the VIs from the AVHRR and MODIS have been merged to generate the Vegetation Index and Phenology product (Didan 2014). OCO-2 SIF soundings and MODIS data were blended to develop global, gridded SIF estimates for the period from 2000 to present (e.g., GOSIF) (Li and Xiao 2019). The lidar-radar synergies improved the accuracy in forest biomass estimates (Hyde et al. 2007; Sun et al. 2011), and the synergy of Landsat 8/OLI and ICESat-2 improved the estimation of shrub/herbaceous biomass (Glenn et al. 2016). Synergies of different remote sensing data will receive growing attention in future C cycle studies. For example, the synergies of VIs and SIF are likely to improve the estimation of C fluxes. The synergistic use of lidar, radar, and passive optical remote sensing data is expected to be more widely used for biomass estimation, particularly at regional to global scales. The synergy and integration of different remote sensing data and methods have the potential to quantify the different C flux components and C stocks and therefore to assess the entire terrestrial C cycle at regional to global scales. For example, VIs, SIF, LUE models, data-driven methods, and atmospheric inversions can be used to estimate C fluxes; biophysical variables and vegetation properties (e.g., LAI, FAPAR, canopy N content) derived from multispectral and hyperspectral imagery can be assimilated to process-based models for simulating C fluxes and/or stocks; lidar, SAR, and the
synergy between lidar/SAR and passive optical remote sensing data (e.g., Landsat, Sentinel, MODIS) can be used to quantify biomass C stocks; the impacts of disturbance on the C cycle can also be quantified using remote sensing data. The synergistic use and integration of various remote sensing data and methods can potentially provide a holistic picture of the terrestrial C cycle.

This study was financially supported by the National Aeronautics and Space Administration (NASA) (the Carbon Cycle Science Program: NNX14AJ18G; the Climate Indicators and Data Products for Future National Climate Assessments: NNX16AG61G; the Science of Terra and Aqua: NNX14AI70G) and National Science Foundation (NSF) (MacroSystems Biology: EF-1638688). The lead author (J. Xiao) would like to thank Dr. David Schimel for helpful discussion. We thank Dr. Xufeng Wang for processing the GIMMS3g NDVI and MODIS EVI data and Dr. Yibo Liu for providing BEPS GPP data. We thank Dr. Steve Running and the two anonymous reviewers for their constructive and insightful comments on the manuscript.

**Acronyms and abbreviations:**

* ABI: Advanced Baseline Imager
* ACGS: Atmospheric Carbon Dioxide Grating Spectrometer
* AGB: Aboveground biomass
* AHI: Advanced Himawari Imager
* AIRS: Atmospheric Infrared Sounder
* ALS: Airborne laser scanners
* AMSR-E: Advanced Microwave Scanning Radiometer for EOS
* ANPP: Aboveground net primary productivity (NPP)
* AOP: Airborne observation platform
* APAR: Absorbed photosynthetically active radiation
* AR: Autrophic respiration
* ASCII: American Standard Code for Information Interchange
<table>
  <tr>
    <td>1874</td>
    <td>ASTER</td>
    <td>Advanced Spaceborne Thermal Emission and Reflection Radiometer</td>
  </tr>
  <tr>
    <td>1875</td>
    <td>ATLAS</td>
    <td>Advanced Topographic Laser Altimeter System</td>
  </tr>
  <tr>
    <td>1876</td>
    <td>AVHRR</td>
    <td>Advanced very high resolution radiometer</td>
  </tr>
  <tr>
    <td>1877</td>
    <td>AVIRIS</td>
    <td>Airborne Visible/InfraRed Imaging Spectrometer</td>
  </tr>
  <tr>
    <td>1878</td>
    <td>BEAMS</td>
    <td>Biosphere model integrating Eco-physiological And Mechanistic approaches using Satellite data</td>
  </tr>
  <tr>
    <td>1879</td>
    <td>BEPS</td>
    <td>Boreal Ecosystems Productivity Simulator</td>
  </tr>
  <tr>
    <td>1881</td>
    <td>BLS</td>
    <td>Backpack laser scanning</td>
  </tr>
  <tr>
    <td>1882</td>
    <td>BRDF</td>
    <td>Bidirectional reflectance distribution function</td>
  </tr>
  <tr>
    <td>1883</td>
    <td>CASA</td>
    <td>Carnegie Ames Stanford Approach Biosphere model</td>
  </tr>
  <tr>
    <td>1884</td>
    <td>CHM</td>
    <td>Canopy height model</td>
  </tr>
  <tr>
    <td>1885</td>
    <td>CLM</td>
    <td>Community Land Model</td>
  </tr>
  <tr>
    <td>1886</td>
    <td>CMS</td>
    <td>Carbon Monitoring System</td>
  </tr>
  <tr>
    <td>1887</td>
    <td>CO<sub>2</sub></td>
    <td>Carbon dioxide</td>
  </tr>
  <tr>
    <td>1888</td>
    <td>CSA</td>
    <td>Canadian Space Agency</td>
  </tr>
  <tr>
    <td>1889</td>
    <td>DAIS</td>
    <td>Digital Airborne Imaging Spectrometer</td>
  </tr>
  <tr>
    <td>1890</td>
    <td>DBH</td>
    <td>Diameter at breast height</td>
  </tr>
  <tr>
    <td>1891</td>
    <td>DGVM</td>
    <td>Dynamic Global Vegetation Model</td>
  </tr>
  <tr>
    <td>1892</td>
    <td>DRL</td>
    <td>Discrete return lidar</td>
  </tr>
  <tr>
    <td>1893</td>
    <td>DSM</td>
    <td>Digital Surface Model</td>
  </tr>
  <tr>
    <td>1894</td>
    <td>DTM</td>
    <td>Digital Terrain Model</td>
  </tr>
  <tr>
    <td>1895</td>
    <td>EC</td>
    <td>Eddy covariance</td>
  </tr>
  <tr>
    <td>1896</td>
    <td>EC-MOD</td>
    <td>Eddy Covariance-MODIS</td>
  </tr>
  <tr>
    <td>1897</td>
    <td>EnMap</td>
    <td>Environmental Mapping and Analysis Program</td>
  </tr>
  <tr>
    <td>1898</td>
    <td>EnviSat</td>
    <td>ENVIronment SATellite</td>
  </tr>
  <tr>
    <td>1899</td>
    <td>EO-1</td>
    <td>Earth Observing-1</td>
  </tr>
  <tr>
    <td>1900</td>
    <td>EOS</td>
    <td>Earth Observing System</td>
  </tr>
  <tr>
    <td>1901</td>
    <td>ER</td>
    <td>Ecosystem respiration</td>
  </tr>
  <tr>
    <td>1902</td>
    <td>ERS</td>
    <td>European Remote Sensing</td>
  </tr>
  <tr>
    <td>1903</td>
    <td>ESA</td>
    <td>European Space Agency</td>
  </tr>
  <tr>
    <td>1904</td>
    <td>ETM+</td>
    <td>Enhanced Thematic Mapper Plus</td>
  </tr>
  <tr>
    <td>1905</td>
    <td>EVI</td>
    <td>Enhanced vegetation index</td>
  </tr>
  <tr>
    <td>1906</td>
    <td>EVI-2</td>
    <td>Two-band EVI</td>
  </tr>
  <tr>
    <td>1907</td>
    <td>FAPAR</td>
    <td>Fraction of absorbed photosynthetically active radiation</td>
  </tr>
  <tr>
    <td>1908</td>
    <td>FIA</td>
    <td>Forest Inventory Data</td>
  </tr>
  <tr>
    <td>1909</td>
    <td>FLEX</td>
    <td>FLuorescence EXplorer</td>
  </tr>
  <tr>
    <td>1910</td>
    <td>FOTO</td>
    <td>Fourier Transform Textural Ordination</td>
  </tr>
  <tr>
    <td>1911</td>
    <td>GEDI</td>
    <td>Global Ecosystem Dynamics Investigation</td>
  </tr>
  <tr>
    <td>1912</td>
    <td>GeoCARB</td>
    <td>Geostationary Carbon Observatory</td>
  </tr>
  <tr>
    <td>1913</td>
    <td>GIMMS</td>
    <td>Global Inventory Monitoring and Modeling System</td>
  </tr>
  <tr>
    <td>1914</td>
    <td>GLAS</td>
    <td>Geoscience Laser Altimeter System</td>
  </tr>
  <tr>
    <td>1915</td>
    <td>GLASS</td>
    <td>Global LAnd Surface Satellite</td>
  </tr>
  <tr>
    <td>1916</td>
    <td>GO</td>
    <td>Geometric-optical</td>
  </tr>
  <tr>
    <td>1917</td>
    <td>GOES</td>
    <td>Geostationary Operational Environmental Satellites</td>
  </tr>
  <tr>
    <td>1918</td>
    <td>GOFC-GOLD</td>
    <td>Global Observation of Forest Cover and Land Dynamics</td>
  </tr>
  <tr>
    <td>1919</td>
    <td>GOME-2</td>
    <td>Global Monitoring Ozone Experiment 2</td>
  </tr>
</table>
<table>
  <tr>
    <td>1920</td>
    <td>GOSAT</td>
    <td>Greenhouse gases Observing SATellite</td>
  </tr>
  <tr>
    <td>1921</td>
    <td>GPP</td>
    <td>Gross primary productivity</td>
  </tr>
  <tr>
    <td>1922</td>
    <td>HIRS-2</td>
    <td>High resolution Infrared Radiation Sounder-2</td>
  </tr>
  <tr>
    <td>1923</td>
    <td>HLS</td>
    <td>Harmonized Landsat and Sentinel-2</td>
  </tr>
  <tr>
    <td>1924</td>
    <td>HR</td>
    <td>Heterotrophic respiration</td>
  </tr>
  <tr>
    <td>1925</td>
    <td>Hyperion</td>
    <td>Hyperspectral Imager</td>
  </tr>
  <tr>
    <td>1926</td>
    <td>HyspIRI</td>
    <td>Hyperspectral Infrared Imager</td>
  </tr>
  <tr>
    <td>1927</td>
    <td>ICESat</td>
    <td>Ice, Cloud, and land Elevation Satellite</td>
  </tr>
  <tr>
    <td>1928</td>
    <td>InSAR</td>
    <td>SAR interferometry</td>
  </tr>
  <tr>
    <td>1929</td>
    <td>InTEC</td>
    <td>Integrated Terrestrial Ecosystem C-budget model</td>
  </tr>
  <tr>
    <td>1930</td>
    <td>ISS</td>
    <td>International Space Station</td>
  </tr>
  <tr>
    <td>1931</td>
    <td>JAXA</td>
    <td>Japan Aerospace Exploration Agency</td>
  </tr>
  <tr>
    <td>1932</td>
    <td>JERS-1</td>
    <td>Japanese Earth Resources Satellite 1</td>
  </tr>
  <tr>
    <td>1933</td>
    <td>JPSS-1</td>
    <td>First Joint Polar Satellite System</td>
  </tr>
  <tr>
    <td>1934</td>
    <td>LAI</td>
    <td>Leaf area index</td>
  </tr>
  <tr>
    <td>1935</td>
    <td>lidar</td>
    <td>Light detection and ranging</td>
  </tr>
  <tr>
    <td>1936</td>
    <td>LST</td>
    <td>Land surface temperature</td>
  </tr>
  <tr>
    <td>1937</td>
    <td>LUE</td>
    <td>Light use efficiency</td>
  </tr>
  <tr>
    <td>1938</td>
    <td>LVIS</td>
    <td>Laser Vegetation Imaging Sensor</td>
  </tr>
  <tr>
    <td>1939</td>
    <td>MABEL</td>
    <td>Multiple Altimeter Beam Experimental Lidar</td>
  </tr>
  <tr>
    <td>1940</td>
    <td>MIR</td>
    <td>Middle infrared</td>
  </tr>
  <tr>
    <td>1941</td>
    <td>MLS</td>
    <td>Mobile laser scanning</td>
  </tr>
  <tr>
    <td>1942</td>
    <td>MODIS</td>
    <td>Moderate resolution imaging spectroradiometer</td>
  </tr>
  <tr>
    <td>1943</td>
    <td>MSI</td>
    <td>Multispectral Imager</td>
  </tr>
  <tr>
    <td>1944</td>
    <td>MTBS</td>
    <td>Monitoring Trends in Burn Severity</td>
  </tr>
  <tr>
    <td>1945</td>
    <td>MTCI</td>
    <td>MERIS total chlorophyll index</td>
  </tr>
  <tr>
    <td>1946</td>
    <td>NASA</td>
    <td>National Aeronautics and Space Administration</td>
  </tr>
  <tr>
    <td>1947</td>
    <td>NBP</td>
    <td>Net biome productivity</td>
  </tr>
  <tr>
    <td>1948</td>
    <td>NDVI</td>
    <td>Normalized difference vegetation index</td>
  </tr>
  <tr>
    <td>1949</td>
    <td>NDWI</td>
    <td>Normalized difference water index</td>
  </tr>
  <tr>
    <td>1950</td>
    <td>NEE</td>
    <td>Net ecosystem exchange</td>
  </tr>
  <tr>
    <td>1951</td>
    <td>NEON</td>
    <td>National Ecological Observatory Network</td>
  </tr>
  <tr>
    <td>1952</td>
    <td>NEP</td>
    <td>Net ecosystem productivity</td>
  </tr>
  <tr>
    <td>1953</td>
    <td>NIR</td>
    <td>Rear-infrared</td>
  </tr>
  <tr>
    <td>1954</td>
    <td>NISAR</td>
    <td>NASA-ISRO SAR Mission</td>
  </tr>
  <tr>
    <td>1955</td>
    <td>NOAA</td>
    <td>National Oceanic and Atmospheric Administration</td>
  </tr>
  <tr>
    <td>1956</td>
    <td>NPP</td>
    <td>Net primary productivity</td>
  </tr>
  <tr>
    <td>1957</td>
    <td>OCO-2</td>
    <td>Orbiting Carbon Observatory-2</td>
  </tr>
  <tr>
    <td>1958</td>
    <td>OCO-3</td>
    <td>Orbiting Carbon Observatory-3</td>
  </tr>
  <tr>
    <td>1959</td>
    <td>OLI</td>
    <td>Operational Land Imager</td>
  </tr>
  <tr>
    <td>1960</td>
    <td>ORCHIDEE</td>
    <td>Organizing Carbon and Hydrology in Dynamics Ecosystems model</td>
  </tr>
  <tr>
    <td>1961</td>
    <td>PALSAR</td>
    <td>Phased Array type L-band Synthetic Aperture Radar</td>
  </tr>
  <tr>
    <td>1962</td>
    <td>PAR</td>
    <td>Photosynthetically active radiation</td>
  </tr>
  <tr>
    <td>1963</td>
    <td>PCA</td>
    <td>Principal component analysis</td>
  </tr>
  <tr>
    <td>1964</td>
    <td>PolInSAR</td>
    <td>Polarimetric SAR interferometry</td>
  </tr>
</table>
<table>
  <tr>
    <td>1965</td>
    <td>PRISMA</td>
    <td>Prototype Research Instruments and Space Mission technology</td>
  </tr>
  <tr>
    <td>1966</td>
    <td></td>
    <td>Advancement</td>
  </tr>
  <tr>
    <td>1967</td>
    <td>PVI</td>
    <td>Perpendicular vegetation index</td>
  </tr>
  <tr>
    <td>1968</td>
    <td>RAR</td>
    <td>Real aperture radar</td>
  </tr>
  <tr>
    <td>1969</td>
    <td>REDD+</td>
    <td>Reducing emissions from deforestation and forest degradation</td>
  </tr>
  <tr>
    <td>1970</td>
    <td>RMSE</td>
    <td>Root mean squared error</td>
  </tr>
  <tr>
    <td>1971</td>
    <td>RT</td>
    <td>Radiative transfer</td>
  </tr>
  <tr>
    <td>1972</td>
    <td>SAR</td>
    <td>Synthetic aperture radar</td>
  </tr>
  <tr>
    <td>1973</td>
    <td>SCIAMACHY</td>
    <td>SCanning Imaging Absorption spectroMeter for Atmospheric</td>
  </tr>
  <tr>
    <td>1974</td>
    <td></td>
    <td>CartographY</td>
  </tr>
  <tr>
    <td>1975</td>
    <td>SCOPE</td>
    <td>Soil Canopy Observation, Photochemistry and Energy fluxes</td>
  </tr>
  <tr>
    <td>1976</td>
    <td>SCORPAN</td>
    <td>soils and/or soil properties, climate and/or climate properties, organisms</td>
  </tr>
  <tr>
    <td>1977</td>
    <td></td>
    <td>like flora and fauna and human activities, relief settings, parent</td>
  </tr>
  <tr>
    <td>1978</td>
    <td></td>
    <td>material, age, and spatial coordinate n</td>
  </tr>
  <tr>
    <td>1979</td>
    <td>SeaSat</td>
    <td>Seafaring Satellite</td>
  </tr>
  <tr>
    <td>1980</td>
    <td>SiB2</td>
    <td>Simple Biosphere Model 2</td>
  </tr>
  <tr>
    <td>1981</td>
    <td>SIF</td>
    <td>Solar-induced chlorophyll fluorescence</td>
  </tr>
  <tr>
    <td>1982</td>
    <td>SIR-C/X-SAR</td>
    <td>Spaceborne Imaging Radar-C / X-Band Synthetic Aperture Radar</td>
  </tr>
  <tr>
    <td>1983</td>
    <td>SLICER</td>
    <td>Scanning LiDAR Imager of Canopies by Echo Recovery</td>
  </tr>
  <tr>
    <td>1984</td>
    <td>SMA</td>
    <td>Spectral mixture analysis</td>
  </tr>
  <tr>
    <td>1985</td>
    <td>SMAP</td>
    <td>Soil Moisture Active Passive Mission</td>
  </tr>
  <tr>
    <td>1986</td>
    <td>SOC</td>
    <td>Soil organic carbon</td>
  </tr>
  <tr>
    <td>1987</td>
    <td>SPOT</td>
    <td>Satellite Pour l'Observation de la Terre</td>
  </tr>
  <tr>
    <td>1988</td>
    <td>SR</td>
    <td>Simple Ratio</td>
  </tr>
  <tr>
    <td>1989</td>
    <td>STFs</td>
    <td>SpectroTransfer Functions</td>
  </tr>
  <tr>
    <td>1990</td>
    <td>SWIR</td>
    <td>Shortwave infrared</td>
  </tr>
  <tr>
    <td>1991</td>
    <td>TanSat</td>
    <td>Chinese Carbon Dioxide Observation Satellite</td>
  </tr>
  <tr>
    <td>1992</td>
    <td>TANSO-FTS</td>
    <td>Carbon Observation-Fourier Transform Spectrometer</td>
  </tr>
  <tr>
    <td>1993</td>
    <td>TC</td>
    <td>Tasseled cap transformation</td>
  </tr>
  <tr>
    <td>1994</td>
    <td>TC-GVI</td>
    <td>Tasselled cap green vegetation index</td>
  </tr>
  <tr>
    <td>1995</td>
    <td>TEMPO</td>
    <td>Tropospheric Emissions: Monitoring of Pollution</td>
  </tr>
  <tr>
    <td>1996</td>
    <td>TES</td>
    <td>Tropospheric Emission Spectrometer</td>
  </tr>
  <tr>
    <td>1997</td>
    <td>TIR</td>
    <td>Thermal infrared</td>
  </tr>
  <tr>
    <td>1998</td>
    <td>TIRS-2</td>
    <td>Thermal Infrared Sensor 2</td>
  </tr>
  <tr>
    <td>1999</td>
    <td>TLS</td>
    <td>Terrestrial laser scanning</td>
  </tr>
  <tr>
    <td>2000</td>
    <td>TM</td>
    <td>Thematic Mapper</td>
  </tr>
  <tr>
    <td>2001</td>
    <td>TomoSAR</td>
    <td>Tomographic SAR</td>
  </tr>
  <tr>
    <td>2002</td>
    <td>TROPOMI</td>
    <td>TROPospheric Monitoring Instrument</td>
  </tr>
  <tr>
    <td>2003</td>
    <td>UAV</td>
    <td>Unmanned aerial vehicles</td>
  </tr>
  <tr>
    <td>2004</td>
    <td>USDA</td>
    <td>U.S. Department of Agriculture</td>
  </tr>
  <tr>
    <td>2005</td>
    <td>USGS</td>
    <td>U.S. Geological Survey</td>
  </tr>
  <tr>
    <td>2006</td>
    <td>VI</td>
    <td>Vegetation index</td>
  </tr>
  <tr>
    <td>2007</td>
    <td>VIIRS</td>
    <td>Visible Infrared Imaging Radiometer Suite</td>
  </tr>
  <tr>
    <td>2008</td>
    <td>VNIR/SWIR</td>
    <td>Visible Near Infrared and Short Wave Infrared</td>
  </tr>
  <tr>
    <td>2009</td>
    <td>WDRVI</td>
    <td>Wide dynamic range vegetation index</td>
  </tr>
  <tr>
    <td>2010</td>
    <td></td>
    <td></td>
  </tr>
</table>
2013 Ai, J.L., Jia, G.S., Epstein, H.E., Wang, H.S., Zhang, A.Z., Hu, Y.H. 2018. MODIS-Based Estimates of Global Terrestrial Ecosystem Respiration. *Journal of Geophysical Research-Biogeosciences*, 123, 326-352.

2014 Alemohammad, S.H., Fang, B., Konings, A.G., Aires, F., Green, J.K., Kolassa, J., Miralles, D., Prigent, C., Gentine, P. 2017. Water, energy, and carbon with artificial neural networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence. *Biogeosciences*, 14, 4101-4124.

2015 Asner, G.P., Knapp, D.E., Kennedy-Bowdoin, T., Jones, M.O., Martin, R.E., Boardman, J., Field, C.B. 2007. Carnegie Airborne Observatory: in-flight fusion of hyperspectral imaging and waveform light detection and ranging (wLiDAR) for three-dimensional studies of ecosystems. *Journal of Applied Remote Sensing*, 1, 013536, https://doi.org/10.1117/1.2794018.

2016 Asrar, G., Fuchs, M., Kanemasu, E.T., Hatfield, J.L. 1984. Estimating absorbed photosynthetic radiation and leaf-area index from spectral reflectance in wheat. *Agronomy Journal*, 76, 300-306.

2017 Astrup, R., Ducey, M.J., Granhus, A., Ritter, T., von Lupke, N. 2014. Approaches for estimating stand-level volume using terrestrial laser scanning in a single-scan mode. *Canadian Journal of Forest Research-Revue Canadienne De Recherche Forestiere*, 44, 666-676.

2018 Atkin, O.K., Tjoelker, M.G. 2003. Thermal acclimation and the dynamic response of plant respiration to temperature. *Trends in Plant Science*, 8, 343-351.

2019 Baccini, A., Friedl, M.A., Woodcock, C.E., Warbington, R. 2004. Forest biomass estimation over regional scales using multisource data. *Geophysical Research Letters*, 31, L10501, doi:10.1029/2004GL019782.

2020 Baccini, A., Laporte, N., Goetz, S.J., Sun, M., Dong, H. 2008. A first map of tropical Africa's above-ground biomass derived from satellite imagery. *Environmental Research Letters*, 3, 045011, doi:10.1088/1748-9326/3/4/045011.

2021 Baillarin, S.J., Meygret, A., Dechoz, C., Petrucci, B., Lacherade, S., Tremas, T., Isola, C., Martimort, P., Spoto, F., Ieee (2012). Sentinel-2 level 1 products and image processing performances. *2012 Ieee International Geoscience and Remote Sensing Symposium* (pp. 7003-7006). New York: Ieee.

2022 Baldocchi, D., Falge, E., Gu, L.H., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, C., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B., Lee, X.H., Malhi, Y., Meyers, T., Munger, W., Oechel, W., U, K.T.P., Pilegaard, K., Schmid, H.P., Valentini, R., Verma, S., Vesala, T., Wilson, K., Wofsy, S. 2001. FLUXNET: A new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. *Bulletin of the American Meteorological Society*, 82, 2415-2434.

2023 Barkley, M.P., Monks, P.S., Engelen, R.J. 2006. Comparison of SCIAMACHY and AIRS CO2 measurements over North America during the summer and autumn of 2003. *Geophysical Research Letters*, 33, L20805, doi:10.1029/2006GL026807.

2024 Bartholomeus, H., Kooistra, L., Stevens, A., van Leeuwen, M., van Wesemael, B., Ben-Dor, E., Tychon, B. 2011. Soil Organic Carbon mapping of partially vegetated agricultural fields
2055 with imaging spectroscopy. *International Journal of Applied Earth Observation and Geoinformation*, 13, 81-88.

2057 Basu, S., Guerlet, S., Butz, A., Houweling, S., Hasekamp, O., Aben, I., Krummel, P., Steele, P., 2008. Global CO2 fluxes estimated from GOSAT retrievals of total column CO2. *Atmospheric Chemistry and Physics*, 13, 8695-8717.

2061 Beaudoin, A., Letoan, T., Goze, S., Nezry, E., Lopes, A., Mougin, E., Hsu, C.C., Han, H.C., 2006. Retrieval of forest biomass from SAR data. *International Journal of Remote Sensing*, 15, 2777-2796.

2064 Ben-dor, E., Banin, A. 1995. Near-infrared analysis as a rapid method to simultaneously evaluate several soil properties. *Soil Science Society of America Journal*, 59, 364-372.

2066 Ben-Dor, E., Inbar, Y., Chen, Y. 1997. The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400-2500 nm) during a controlled decomposition process. *Remote Sensing of Environment*, 61, 1-15.

2069 Ben-Dor, E., Irons, J.A., Epema, G.F. (1999). Soil reflectance. *Manual of Remote Sensing: Remote Sensing for the Earth Sciences* (pp. 111-189): New York: John Wiley & Sons Inc.

2071 Ben-Dor, E., Patkin, K., Banin, A., Karnieli, A. 2002. Mapping of several soil properties using DAIS-7915 hyperspectral scanner data - a case study over clayey soils in Israel. *International Journal of Remote Sensing*, 23, 1043-1062.

2074 Blackard, J.A., Finco, M.V., Helmer, E.H., Holden, G.R., Hoppus, M.L., Jacobs, D.M., Lister, A.J., Moisen, G.G., Nelson, M.D., Riemann, R., Ruefenacht, B., Salajanu, D., Weyermann, D.L., Winterberger, K.C., Brandeis, T.J., Czaplewski, R.L., McRoberts, R.E., Patterson, P.L., & Tymcio, R.P. 2008. Mapping US forest biomass using nationwide forest inventory data and moderate resolution information. *Remote Sensing of Environment*, 112, 1658-1677.

2080 Bodesheim, P., Jung, M., Gans, F., Mahecha, M.D., Reichstein, M. 2018. Upscaled diurnal cycles of land-atmosphere fluxes: a new global half-hourly data product. *Earth System Science Data*, 10, 1327-1365.

2083 Bolton, D.K., Coops, N.C., Wulder, M.A. 2015. Characterizing residual structure and forest recovery following high-severity fire in the western boreal of Canada using Landsat time-series and airborne lidar data. *Remote Sensing of Environment*, 163, 48-60.

2086 Boyte, S.P., Wylie, B.K., Howard, D.M., Dahal, D., Gilmanov, T. 2018. Estimating carbon and showing impacts of drought using satellite data in regression-tree models. *International Journal of Remote Sensing*, 39, 374-398.

2089 Breiman, L. 2001. Random forests. *Machine Learning*, 45, 5-32.

2090 Bright, B.C., Hicke, J.A., Hudak, A.T. 2012a. Estimating aboveground carbon stocks of a forest affected by mountain pine beetle in Idaho using lidar and multispectral imagery. *Remote Sensing of Environment*, 124, 270-281.

2093 Bright, B.C., Hicke, J.A., Hudak, A.T. 2012b. Landscape-scale analysis of aboveground tree carbon stocks affected by mountain pine beetles in Idaho. *Environmental Research Letters*, 7, 045702.

2096 Bright, B.C., Hicke, J.A., Meddens, A.J.H. 2013. Effects of bark beetle-caused tree mortality on biogeochemical and biogeophysical MODIS products. *Journal of Geophysical Research: Biogeosciences*, 118, 974-982.
2145 Chopping, M., Schaaf, C.B., Zhao, F., Wang, Z.S., Nolin, A.W., Moisen, G.G., Martonchik, J.V.,
2146 Bull, M. 2011. Forest structure and aboveground biomass in the southwestern United
2147 States from MODIS and MISR. *Remote Sensing of Environment*, 115, 2943-2953.

2148 Ciais, P., Sabine, C., Bala, G., Bopp, L., Brovkin, V., Canadell, J., Chhabra, A., DeFries, R.,
2149 Galloway, J., Heimann, M., Jones, C., Le Quéré, C., Myneni, R.B., Piao, S., Thornton, P.
2150 (2013). Carbon and Other Biogeochemical Cycles. In T.F. Stocker, D. Qin, G.-K. Plattner,
2151 M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, & P.M. Midgley (Eds.),
2152 *Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to
2153 the Fifth Assessment Report of the Intergovernmental Panel on Climate Change*.
2154 Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.

2155 Claverie, M., Ju, J., Masek, J.G., Dungan, J.L., Vermote, E.F., Roger, J.C., Skakun, S.V., Justice,
2156 C. 2018. The Harmonized Landsat and Sentinel-2 surface reflectance data set. *Remote
2157 Sensing of Environment*, 219, 145-161.

2158 Cloude, S.R., Papathanassiou, K.P. 1998. Polarimetric SAR interferometry. *IEEE Transactions
2159 on Geoscience and Remote Sensing*, 36, 1551-1565.

2160 Cohen, W.B., Maiersperger, T.K., Gower, S.T., Turner, D.P. 2003. An improved strategy for
2161 regression of biophysical variables and Landsat ETM+ data. *Remote Sensing of
2162 Environment*, 84, 561-571.

2163 Cohen, W.B., Maiersperger, T.K., Turner, D.P., Ritts, W.D., Pflugmacher, D., Kennedy, R.E.,
2164 Kirschbaum, A., Running, S.W., Costa, M., Gower, S.T. 2006. MODIS land cover and
2165 LAI collection 4 product quality across nine sites in the western hemisphere. *Ieee
2166 Transactions on Geoscience and Remote Sensing*, 44, 1843-1857.

2167 Cohen, W.B., Yang, Z.Q., Stehman, S.V., Schroeder, T.A., Bell, D.M., Masek, J.G., Huang, C.Q.,
2168 Meigs, G.W. 2016. Forest disturbance across the conterminous United States from 1985-
2169 2012: The emerging dominance of forest decline. *Forest Ecology and Management*, 360,
2170 242-252.

2171 Collatz, G.J., Ball, J.T., Grivet, C., Berry, J.A. 1991. Physiological and environmental regulation
2172 of stomatal conductance, photosynthesis, and transpiration: a model that includes a
2173 laminar boundary layer. *Agricultural and Forest Meteorology*, 54, 107-136.

2174 Cook, B.D., Corp, L.A., Nelson, R.F., Middleton, E.M., Morton, D.C., McCorkel, J.T., Masek,
2175 J.G., Ranson, K.J., Ly, V., Montesano, P.M. 2013. NASA Goddard's LiDAR,
2176 Hyperspectral and Thermal (G-LiHT) Airborne Imager. *Remote Sensing*, 5, 4045-4066.

2177 Cook, R.B., Margle, S.M., Holladay, S.K., Heinsch, F.A., Schaaf, C.B. (2004). Subsets of remote
2178 sensing products for AmeriFlux sites: MODIS ASCII Subsets. In, *Ameriflux Annual
2179 Meeting*. Boulder, CO.

2180 Damm, A., Guanter, L., Paul-Limoges, E., van der Tol, C., Hueni, A., Buchmann, N., Eugster,
2181 W., Ammann, C., Schaepman, M.E. 2015. Far-red sun-induced chlorophyll fluorescence
2182 shows ecosystem-specific relationships to gross primary production: An assessment
2183 based on observational and modeling approaches. *Remote Sensing of Environment*, 166,
2184 91-105.

2185 Dash, J., Curran, P.J. 2004. The MERIS terrestrial chlorophyll index. *International Journal of
2186 Remote Sensing*, 25, 5403-5413.

2187 Daumard, F., Champagne, S., Fournier, A., Goulas, Y., Ounis, A., Hanocq, J.F., Moya, I. 2010.
2188 A field platform for continuous measurement of canopy fluorescence. *IEEE Transactions
2189 on Geoscience and Remote Sensing*, 48, 3358-3368.
2190 Demarty, J., Chevallier, F., Friend, A.D., Viovy, N., Piao, S.L., Ciais, P. 2007. Assimilation of global MODIS leaf area index retrievals within a terrestrial biosphere model. *Geophysical Research Letters*, 34, L15402, doi:10.1029/2007GL030014.

2191
2192 Detmers, R.G., Hasekamp, O., Aben, I., Houweling, S., van Leeuwen, T.T., Butz, A., Landgraf, J., Kohler, P., Guanter, L., Poulter, B. 2015. Anomalous carbon uptake in Australia as seen by GOSAT. *Geophysical Research Letters*, 42, 8177-8184.

2196 Didan, K. 2014. Multi-Satellite Earth Science Data Record for Studying Global Vegetation Trends and changes, The University of Arizona, Tucson, AZ.

2198 Disney, M.I., Vicari, M.B., Burt, A., Calders, K., Lewis, S.L., Raumonen, P., Wilkes, P. 2018. Weighing trees with lasers: advances, challenges and opportunities. *Interface Focus*, 8, 20170048. http://dx.doi.org/10.1098/rsfs.2017.0048.

2201 Dobson, M.C., Ulaby, F.T., LeToan, T., Beaudoin, A., Kasischke, E.S., Christensen, N. 1992. Dependence of radar backscatter on coniferous forest biomass. *IEEE Transactions on Geoscience and Remote Sensing*, 30, 412-415.

2204 Dolan, K.A., Hurtt, G.C., Chambers, J.Q., Dubayah, R.O., Frolking, S., Masek, J.G. 2011. Using ICESat's Geoscience Laser Altimeter System (GLAS) to assess large-scale forest disturbance caused by hurricane Katrina. *Remote Sensing of Environment*, 115, 86-96.

2207 Dong, J.R., Kaufmann, R.K., Myneni, R.B., Tucker, C.J., Kauppi, P.E., Liski, J., Buermann, W., Alexeyev, V., Hughes, M.K. 2003. Remote sensing estimates of boreal and temperate forest woody biomass: carbon pools, sources, and sinks. *Remote Sensing of Environment*, 84, 393-410.

2211 Dou, X.M., Yang, Y.G. 2018. Estimating forest carbon fluxes using four different data-driven techniques based on long-term eddy covariance measurements: Model comparison and evaluation. *Science of the Total Environment*, 627, 78-94.

2214 Drake, J.B., Dubayah, R.O., Clark, D.B., Knox, R.G., Blair, J.B., Hofton, M.A., Chazdon, R.L., Weishampel, J.F., Prince, S.D. 2002. Estimation of tropical forest structural characteristics using large-footprint lidar. *Remote Sensing of Environment*, 79, 305-319.

2217 Drusch, M., Moreno, J., Del Bello, U., Franco, R., Goulas, Y., Huth, A., Kraft, S., Middleton, E.M., Miglietta, F., Mohammed, G., Nedbal, L., Rascher, U., Schttemeyer, D., Verhoef, W. 2017. The FLuorescence EXplorer Mission Concept-ESA's Earth Explorer 8. *IEEE Transactions on Geoscience and Remote Sensing*, 55, 1273-1284.

2221 Dube, T., Mutanga, O. 2015a. Evaluating the utility of the medium-spatial resolution Landsat 8 multispectral sensor in quantifying aboveground biomass in uMgeni catchment, South Africa. *Isprs Journal of Photogrammetry and Remote Sensing*, 101, 36-46.

2224 Dube, T., Mutanga, O. 2015b. Investigating the robustness of the new Landsat-8 Operational Land Imager derived texture metrics in estimating plantation forest aboveground biomass in resource constrained areas. *ISPRS Journal of Photogrammetry and Remote Sensing*, 108, 12-32.

2228 Eckert, S. 2012. Improved forest biomass and carbon estimations using texture measures from WorldView-2 satellite data. *Remote Sensing*, 4, 810-829.

2230 Eidenshink, J., Schwind, B., Brewer, K., Zhu, Z., Quayle, B., Howard, S. 2007. A project for monitoring trends in burn severity. *Fire Ecology*, 3, 3-12.

2232 Eldering, A., Wennberg, P.O., Crisp, D., Schimel, D.S., Gunson, M.R., Chatterjee, A., Liu, J., Schwandner, F.M., Sun, Y., O'Dell, C.W., Frankenberg, C., Taylor, T., Fisher, B., Osterman, G.B., Wunch, D., Hakkarainen, J., Tamminen, J., Weir, B. 2017. The Orbiting
2235 Carbon Observatory-2 early science investigations of regional carbon dioxide fluxes.

2236 *Science*, 358, eaam5745. DOI: 10.1126/science.aam5745

2237 Engelen, R.J., Andersson, E., Chevallier, F., Hollingsworth, A., Matricardi, M., McNally, A.P.,

2238 Thepaut, J.N., Watts, P.D. 2004. Estimating atmospheric CO2 from advanced infrared

2239 satellite radiances within an operational 4D-Var data assimilation system: Methodology

2240 and first results. *Journal of Geophysical Research-Atmospheres*, 109, D19309,

2241 doi:10.1029/2004JD004777.

2242 Enting, I.G., Trudinger, C.M., Francey, R.J. 1995. A synthesis inversion of the concentration and

2243 delta-C-13 of atmospheric CO2. *Tellus Series B-Chemical and Physical Meteorology*, 47,

2244 35-52.

2245 Epstein, H.E., Raynolds, M.K., Walker, D.A., Bhatt, U.S., Tucker, C.J., Pinzon, J.E. 2012.

2246 Dynamics of aboveground phytomass of the circumpolar Arctic tundra during the past

2247 three decades. *Environmental Research Letters*, 7, 015506, oi:10.1088/1748-

2248 9326/7/1/01550.

2249 Fang, H.L., Wei, S.S., Jiang, C.Y., Scipal, K. 2012. Theoretical uncertainty analysis of global

2250 MODIS, CYCLOPES, and GLOBCARBON LAI products using a triple collocation

2251 method. *Remote Sensing of Environment*, 124, 610-621.

2252 Farquhar, G.D., Caemmerer, S.V., Berry, J.A. 1980. A biochemical model of photosynthetic CO3

2253 assimilation in leaves of C3 species. *Planta*, 149, 78-90.

2254 Fazakas, Z., Nilsson, M., Olsson, H. 1999. Regional forest biomass and wood volume estimation

2255 using satellite data and ancillary data. *Agricultural and Forest Meteorology*, 98-9, 417-

2256 425.

2257 Fensholt, R., Sandholt, I., Rasmussen, M.S. 2004. Evaluation of MODIS LAI, fAPAR and the

2258 relation between fAPAR and NDVI in a semi-arid environment using in situ

2259 measurements. *Remote Sensing of Environment*, 91, 490-507.

2260 Field, C.B., Randerson, J.T., Malmstrom, C.M. 1995. Global net primary production: Combining

2261 ecology and remote sensing. *Remote Sensing of Environment*, 51, 74-88.

2262 Flexas, J., Briantais, J.M., Cerovic, Z., Medrano, H., Moya, I. 2000. Steady-state and maximum

2263 chlorophyll fluorescence responses to water stress in grapevine leaves: A new remote

2264 sensing system. *Remote Sensing of Environment*, 73, 283-297.

2265 Folberth, G., Hauglustaine, D.A., Ciais, P., Lathiere, J. 2005. On the role of atmospheric

2266 chemistry in the global CO2 budget. *Geophysical Research Letters*, 32,

2267 L08801, doi:10.1029/2004GL021812.

2268 Foody, G.M., Boyd, D.S., Cutler, M.E.J. 2003. Predictive relations of tropical forest biomass

2269 from Landsat TM data and their transferability between regions. *Remote Sensing of

2270 Environment*, 85, 463-474.

2271 Foody, G.M., Cutler, M.E., McMorrow, J., Pelz, D., Tangki, H., Boyd, D.S., Douglas, I. 2001.

2272 Mapping the biomass of Bornean tropical rain forest from remotely sensed data. *Global

2273 Ecology and Biogeography*, 10, 379-387.

2274 Fournier, A., Daumard, F., Champagne, S., Ounis, A., Goulas, Y., Moya, I. 2012. Effect of

2275 canopy structure on sun-induced chlorophyll fluorescence. *ISPRS Journal of*

2276 *Photogrammetry and Remote Sensing*, 68, 112-120.

2277 Frankenberg, C., Fisher, J.B., Worden, J., Badgley, G., Saatchi, S.S., Lee, J.E., Toon, G.C., Butz,

2278 A., Jung, M., Kuze, A., Yokota, T. 2011. New global observations of the terrestrial

2279 carbon cycle from GOSAT: Patterns of plant fluorescence with gross primary

2280 productivity. *Geophysical Research Letters*, 38, L17706, doi:10.1029/2011GL048738.