# Presentation: 297658

## Remote sensing of productivity, LUE, and stress

- NASA Biodiversity, Ecology, and Applied Science Workshop

- Robert Simmon, Earth Observatory

- Alfredo R. Huete
- Faiz Rahman
- Elizabeth Middleton
- Fred Huemmrich
- Kamel Didan
- Ramakrishna Nemani
- Ranga Myneni
- Scott Saleska
- Xiangming Xiao

## Controls on productivity (NPP/GPP)

- Fluxes of CO2 and energy are strongly influenced by temperature; variations in cellular metabolism; and rates of supply of limiting resources (water, nutrients, light,..).
- Both biotic and abiotic factors regulate metabolic rates of individuals, which combine to determine ecosystem flux rates.
- Plant characteristics (roots, litter), soils,
- 	climate, and disturbance regime
- 	influence production.
- Resources and resource
- 	regulators

**Nemani, *****Science***** 2003**

**Notes:**
Resources and resource regulators
Metabolism is the fundamental process dictating material and energy fluxes through organisms.
At a given site, organisms grow and fill physical space so that the rate of resource use by all individuals is proportional to the rate of limiting resource supply.

## Satellite-based Production Efficiency Models

**Light use efficiency model:**
**	****GPP = ********g**** **x** **FPAR*** ***x*** ***PAR,
- where ******g **is the light-use efficiency (g C/mol PAR),
- FPAR is the fraction of PAR absorbed by the vegetation canopy,
- APAR = FPAR x PAR.

-  *Light absorption process (chlorophyll absorbs PAR) and *
* **Carbon fixation process where absorbed energy is then used to   combine water and CO2.*

**Notes:**
Dierct connection with RS/  but difficulties in converting VI to FPAR and variability in LUE

- Xiao, 2006

- *LUE is difficult to characterize because it is affected by by both variations in climate, and by biological attributes (LC type, C3-C4, )

**Notes:**
All use ‘down-regulation’ scalars.
Various PEM models use substantially different maxLUE for same biome type.
Accurate derivation of LUE is difficult because it is affected by by both variations in climate, and by biological attributes (LC type, C3-C4, )

## Uncertainties

- Satellites can provide consistent measures of vegetation activity with spatial- and temporal- detail at the global scale, which can be linked to ecosystem health, productivity and carbon fluxes,
- There remains large uncertainties in estimating GPP at the canopy level associated with
- Seasonal dynamics
- Spatial variation due to climate, soils, and land use (disturbance, management,... )
- Uncertainties associated with coarse scale meteorology, remote sensing variables (LAI, FPAR, VI), and canopy biophysical attributes (land cover type, biome-specific, disturbance history)

*Heinsch et al.,* 2006

**Notes:**
Our ability to predict variation in ecosystem processes is currently limited by our ability to mechanistically link biological processes across both spatial and temporal scales.

## Conversion of VI to FPAR?

- Most common method to derive FPAR is through NDVI relationships
- Only PAR absorbed by chlorophyll is responsible for photosynthesis:
- FPARcanopy   =  FPARchl + FPARNPV
- Comparisons of FPARchl and FPARcanopy would help define to what degree the PEM models are consistent with light absorption process of photosynthesis at the chlorophyll level.
- Work by Gitelson on estimation of chlorophyll in crops with remote sensing chlorophyll indices
- Work by Xiao in using EVI to estimate FPARchl in different temperate/tropical forest types.
- Non-photochemical dissipation mechanisms

**Notes:**
Photosynthetic efficiency dynamically acclimates to fluctuations in the environment, and non-photochemical dissipation mechanisms protect the photosynthetic apparatus from excessiveirradiance. Growth, on the other hand, is responsible for the geometry of plants and of canopies to varying resource availability and thus plays a major role for the efficiency of resource acquisition.

## Seasonal Flux and FPAR

- _Seasonal Flux and FPAR_

**Prairie ****Shidler****, Oklahoma, C4 Grassland**

- Huemmrich et al.

**Notes:**
fPAR and GEP, again note the need for green fPAR rather than total fPAR.  Note the effect of two significant drought periods on GEP, one about day 180 and the other at day 258.  Here we see the GEP move far from the green fPAR.  The grey vertical lines indicate the days where reflectance data were collected.  Notice how those days capture a range of green fPAR and GEP.

## Middleton et al.

**When leaves are stressed they can no longer photosynthesize at normal rates**
**They must dump excess energy**
**Xanthophyll cycle **
**    ****- the de-epoxidation of violaxanthin**
**         ****to zeaxanthin **
**    ****- a reversible reaction **
**    ****- dissipates excess energy **
**    ****- causes leaf reflectance change at**
**         ****531 nm**

**Notes:**
All of the stress responses in leaves affect apparent reflectance.
First response, the xanthophyll cycle changes pigment amounts altering reflectance at 531 nm.

## FPARcanopy, FPARleaf, and FPARchl 

**	****deciduous broadleaf forest (Harvard Forest)**

- Zhang et al.,2005, using a radiative transfer model (PROSAIL2) & daily MODIS data

**FPAR****leaf**** = *****f*****(NDVI)**
**FPAR****chl**** = *****f*****(EVI)**

**FPAR**

## EVI

**LSWI**

**EVI**

**Surface reflectance (MODIS)**

**Climate data**

**CO****2**** eddy flux tower**

**Validation**

**Literature**

**Satellite-based Vegetation Photosynthesis Model (VPM)**

_**Input data for simulation of the VPM model**_
**Air temperature, PAR, Vegetation indices (EVI, LSWI), Maximum light use efficiency (********0****)**

- Xiao et al. 2004,2006,2006

## Validation with Eddy-covariance flux towers

- They have measured fluxes (CO2, H2O, heat and momentum) continuously at many primary forest tower sites since 2001.
- These data are powerfully suited for vegetation dynamics and for deriving relationships between carbon fluxes and key driving variables.
- Can test model/remote-sensing estimates of carbon-exchange and seasonality.

**Notes:**
Test remote-sensing constrained model predictions of ecosystem structure and landscape scale carbon fluxes.

##   At hour and daily time intervals, ecosystem variables (VI, FPAR, LAI) would not be expected to correlate well with ecosystem fluxes of carbon, water, & energy.

-   At hour and daily time intervals, ecosystem variables (VI, FPAR, LAI) would not be expected to correlate well with ecosystem fluxes of carbon, water, & energy.

**Notes:**
Vegetation indices are not “intrinsic physical quantities” but are widely used as proxies in the assessment of canopy biophysical/ biochemical variables (leaf area index (LAI), fraction of absorbed photosynthetically-active radiation (fPAR), chlorophyll content, green vegetation fraction (Fg), biomass) and canopy biophysical processes (photosynthesis, transpiration) .

## At weekly time scales, these same remote sensing variables become more highly correlated- 

- At weekly time scales, these same remote sensing variables become more highly correlated-

**Notes:**
Due to plant adjustments and integrated response to past environmental forcings,
Riparian - where water is not limiting and where transpiration dominates water fluxes,

##  16-day MODIS NDVI with 16-day integrated GPP-

-  16-day MODIS NDVI with 16-day integrated GPP-
-  NDVI and FPAR become insensitive to GPP at peak season

##  16-day MODIS EVI with 16-day integrated GPP-

-  16-day MODIS EVI with 16-day integrated GPP-
-  EVI remains sensitive to GPP at peak season

## EVI(2-band) = 2.5 x  (nir - red) / (nir - red +L),   L=1

- L=1, C1 & C2 are 6 and 7.5

- EVI(2-band) = 2.5 x  (*nir* - *red*) / (*nir* - *red* +L),   L=1

- L=0
- L=1

**Notes:**
L= 0 to 1,  highest correlations occurred between L=0.5 and 1.

## LUE?

**Notes:**
LUE?

## Sellers 1987,1992

*Sellers* 1987,1992

## MMFS (Deciduous broadleaf forest)

**MMFS (Deciduous broadleaf forest)**

- Sirikul et al 2006

- EVI

- NDVI

## HOWLAND (Evergreen coniferous forest)

- Sirikul et al 2006

## Rahman et al. 2005 (GRL);  Sims et al. 2006 (JGR)

**Notes:**
Sims et al, 2006

## Sims et al. 2006

**Notes:**
Sims 06

## Sims et al. 2006

## Sims et al. (submitted)

**Notes:**
A new model of gross primary productivity for North
American ecosystems based solely on MODIS EVI and LST
Sims et al., (submitted)

## Sims et al. (submitted)

## Sims et al. (submitted)

-  Goal is to maximize the use of remote sensing data and maintain spatial detail (pixel level)

## The rainforests of the Amazon basin form the largest contiguous, intact tropical forest on Earth, a vast storehouse of carbon that could influence the trajectory of global climate change. 

**However, the present-day metabolism and carbon balance of Amaz****oni****a remains poorly characterized due to complex environmental controls (moisture, sunlight) and associated biologic responses.**

**Notes:**
and housing the largest reservoir of biological diversity.

## NOAA- Advanced Very High Resolution Radiometer (AVHRR) in the Amazon

- rainforest

**Notes:**
Tropical rainforests are among the most difficult biomes to monitor with satellite data as shown in many satellite studies using the NOAA- Advanced Very High Resolution Radiometer (AVHRR) measurements in which cloud contamination, sensitivity to seasonally variable atmosphere water vapor and aerosol conditions, and poor spatial resolution (> 4 km) have severely constrained rainforest studies. 

## MODIS EVI transects Cerrado to Rainforest

- Ratana et al. 2006

**Notes:**
Figure 6 Five-year average land surface phenology profiles over dense tropical forest, open tropical forest, intact and converted ecotone forest, and cerrado along the Tp-Br and Tp-Cu transects. The yellow shade shows the periods of the dry season.  Yellow, red, and blue lines are approximate start and end of the dry season in tropical rainforest region, transition zone, and cerrado areas respectively.

## Rainforest GPP & EVIwith modeled GPP (IBIS) at Tapajós 

- Rainforest GPP & EVIwith modeled GPP (IBIS) at Tapajós

- GPP from Lucy Hutyra (Harvard Univ)

**Notes:**
Ecosystem and general circulation models generally predict dry-season declines in photosynthesis and/or evapotranspiration in the Amazon.
A growing body of empirical studies, however, suggests, in line with a calculation first suggested by Shuttleworth (1988), that evapotranspiration (and hence photosynthesis) are not water limited [Rocha et al., 2004], and are thus driven by net radiation.

## This raises a question about model predictions

- The same model constructs affect both short-term (seasonal) and long-term variations of C and water exchange,
- but the performance of models at short time-scales (where they can now be tested with data) is problematic, hence affecting confidence in reliability of their long-term predictions?

**Notes:**
also see Souza-Filho et al, 2004 for observations of sustained dry-season ET from a tower in Caxiuana), and of canopy reflectance (the Enhanced Vegetation Index, or EVI, as record by MODIS sensor) at the large scale from satellites

## Tower GPP flux

**Notes:**
Kamel_Amazon091405.xls

## Santarem Pasture

- Tapajos Forest

## Huete, A

**Notes:**
Significant increases in rainforest activity occurred in the dry season over eastern Amazônia (5 month dry season) with smaller increases in the western portion near Manaus (~2 month dry season). A complete reversal in phenology was observed east of Cauaxi, along the “arc of deforestation”, where extensive pastures and secondary forests remain following deforestation 

## Basin-wide greening in dry season

- EVI

-  October EVI (dry season) minus June EVI (wet season)

- Huete, A

**Notes:**
This analysis shows basin-wide (5.8 x 106 km2) increases in EVI (photosynthetic activity) in the dry season, implying that Amazon rainforests green-up and are more productive in the dry season, precisely the opposite of the pattern predicted for GPP across the same region by models. 
The drier transitional forests to the south and deforested and disturbed portions of the Amazon to the east and south, showed either no green-up activity or declining greenness in the dry season. 
The standard deviation of EVI differences between October and June, across years, varied across the basin and averaged 0.03 units

## Ecotone Rainforests (Transitional)

- Ecotone Rainforests (Transitional)

-  Both light and moisture controls on ecosystem metabolism and productivity

- Ratana, P

**Notes:**
 Start-of-Dry season (SOD) profiles depicted greenning up in the dry season in tropical rainforests.  This ‘greenning’ pattern shifted and was reversed through the transition zone and cerrado, respectively.
 There was a delayed greenning in the transition forest/ zone until the middle of the dry season (except in T1 converted areas), whereas the cerrado greenned up with initiative rainfall.  

## Ratana, P

## Slide 38

**Notes:**
Relationships between MODIS CMG EVI (n=10x10) with photosynthetically active radiation, PAR (0.5 degree resolution) (a)  and monthly rainfall (b) at Manaus, Tapajos, Jaru tower, Sinop, and Brasilia

## MODIS Leaf Area Index (LAI) Product over Rainforests

-  Monthly solar radiation from Terra CERES and GOES-8 instrument,
-  Monthly precipitation from TRMM
-  MODIS LAI averaged over all Amazon rainforest pixels.

- Myneni, R

**Notes:**
The accompanying changes in leaf area which regulate vegetation-atmosphere exchanges of energy, momentum and mass. 
In analysis of five years of MODIS, seasonal swings in green leaf area of about 25% in a majority of the Amazon rainforests were found. That is, leaf area equivalent to nearly 28% the size of South America appears and disappears each year in the Amazon. This seasonal cycle is timed to the seasonality of solar radiation in a manner that is suggestive of anticipatory and opportunistic patterns of net leaf flushing during the light rich dry season and net leaf abscission during the cloudy wet season. 
These heretofore unknown seasonal swings in leaf area are critical to initiation of the transition from dry to wet season, seasonal carbon balance between photosynthetic gains and respiratory losses, and litterfall nutrient cycling in moist tropical forests.

## MODIS Leaf Area Index (LAI) Product over Savanna

- Myneni, R

**Notes:**
Same as (A) except that the data are from savanna and grassland pixels adjacent to the Amazon basin in Brazil and south of the equator. The shaded areas denote dry seasons, defined as months with precipitation less than 50 mm.

## Hyperion data, Asner

## Spectral Measurements of Ecosystem Light Use Efficiency

- E.M. Middleton & K.F. Huemmrich

- 	GPP = ∫(*t*) *f*PAR(*t*) PARin(*t*)

- We want to find , the light use efficiency, through remote sensing,
**Determining LUE from remote sensing requires both hyperspectral and hypertemporal time series data to observe transient and sustained physiological stress effects on plants.**

## The Effect of VPD on GEE

** ****Very high VPDs occurred on the afternoons of June 22-****	****24, causing PARin and GEE to separate.**
** ****When VPD drops for the last few days of this period,GEE ****	****and PARin again track each other.**

**The Effect of VPD on GEE**

## The Approach: 

-     Leaves have multiple responses to stress, including
- Xanthophyll cycle pigments (531nm)
- Solar Induced Fluorescence (690, and 735nm)
- and, over a longer period, reducing the amount of photosynthetic machinery, decreasing chlorophyll concentration (multiple wavelengths)
- All of these responses have specific effects on leaf spectral reflectance

*Xanthophyll*

*Fluorescence*

*Chlorophyll*

## Photochemical Reflectance Index (PRI)

**PRI uses the narrow-band reflectance at 531 nm and a second reference band to detect changes in the xanthophyll cycle**

- • Scaled PRI (sPRI)
- Makes all values positive
-     sPRI = (1+PRI)/2

_**Photochemical Reflectance Index**_** (PRI)**

**Notes:**
PRI is designed to detect changes in xanthophyll pigments

## Moisture Stress Creates Changes in Leaf Biochemistry and Leaf Reflectance 

- Absorbed Photosynthetically Active Radiation Cannot Be Utilized When Stomata Close.
- As a protective mechanism, leaf xanthophyll cycle responds to dissipate excess energy.
- This process can be detected in leaves through a change in the reflectance at 531 nm.

**ENVIRONMENTAL STRESS**
**Reduces g****c**
**( h, T, M, N)**

**g****c****=Ah/C**

**g****c**** = stomatal conductance**
**A (T, M, N) = assimilation rate**
**h = humidity**
**C = CO****2**** concentration**

**Decreasing g****c**** reduces ********531**** **

## Changes in PRI are closely linked to xanthophyll cycle pigments

- Gamon and Surfus 1999

**Changes in PRI are closely linked to xanthophyll cycle pigments**

## Stand-level PRI

**Stand-level PRI**

- (Nichol et al., 2002)

- Tower (Scots Pine)

- Helicopter

**Diurnal** variations in LUE/PRI

**Seasonal** variations in LUE/PRI

## Daily APAR and GEP

**Daily GEP (gC m****-2**** d****-1****)       **

**Daily APAR (MJ m****-2**** d****-1****)       **

- _Daily APAR and GEP_

**Prairie ****Shidler****, Oklahoma, C4 Grassland**

## Temporal Variability of LUE

- _Temporal Variability of LUE_

**LUE (gC MJ****-1****)**

**Notes:**
LUE varies within days as well as over the season, again the reflectance days capture a range of LUE values.

## Spectral Indexes

- _Spectral Indexes_
-      • Match reflectance and flux measurements in time
-      • Look at relationships within days and over season
-      • PRI related to changes in Xanthophyll pigments

**LUE (mol C/mol Q)   **

**Notes:**
LUE is calculated using 3 half hours of flux data surrounding the time of the reflectance measurement.  These data can be used to look at variations within a day as well as seasonal variations.  Notice that the daily change in LUE is a significant  portion of the total variation.

## BOREAS Sites

**LUE calculated from flux tower data and PRI from helicopter hyperspectral**
**reflectances averaged to MODIS bands 11 and 13 are correlated (r = 0.75).**
**	** **LUE = GEE / *****f*****PAR Qin**
**Data from BOREAS, acquired throughout the 1994 growing season in four boreal ecosystems.**

**BOREAS Sites**

## BOREAS Sites

**We replaced the weekly ground-measured Fpar with NDVI from the helicopter, collected at the same time as GEE.**

**BOREAS Sites**

## MODIS PRI and Aspen LUE

**MODIS PRI and Aspen LUE**

- Data from summers of 2001, 2002, and 2003
- Backscatter observations only

## Summary

**The “LAI – FPAR****canopy**** – NDVI” paradigm is likely to continue to play a role in modeling of biogeochemical cycles and climate system.**
**The “Leaf chlorophyll – FPAR****chl**** – EVI” framework , a biochemical perspective of vegetation canopy, opens new doors for improving GPP modeling, reducing uncertainty in modeling of terrestrial carbon cycle, and better characterizing leaf phenology.**
**The PRI offers much potential in pixel-based assessments of LUE and canopy stress at near real-time time scales,**
**Land surface and vegetation water indices are sensitive to changes in leaf water content/ soil moisture, useful in LUE derivations.**
**LST provides information useful in constraining C-fluxes (GPP and Respiration)**

## Conclusions

- This analysis illustrates the power of integrating remote sensing and in-situ tower data.  The generality of single-site studies may be questioned, and an unexpected remote sensing observation may be due to an artifact (arising, e.g., from a seasonal interference pattern induced by cloud cover or aerosol), but the consistency between the independent EVI and tower-derived GPP observations in the Tapajos forest, lends confidence to both findings.

**Notes:**
In this study we showed basin-wide (5.8 x 106 km2) increases in photosynthetic activity in the dry season using a new NASA satellite and suggest that, contrary to intuition, Amazon rainforests green-up and are more productive in the dry season.
complete reversals in phenology were observed in areas where secondary forests and pastures remain following deforestation.
the disparity is *general* and basin-wide.
This confirms many plot-based studies in the Amazon and other rainforests documenting new leaf growth production in the dry season that closely coincide with seasonal peaks in solar irradiance.

## This offers new approaches to constrain rooting depth in terrestrial ecosystem models over the Amazon using MODIS EVI satellite data and Biome-BGC terrestrial ecosystem model.

- Ichii, K

**Notes:**
Accurate parameterization of rooting depth is difficult but important for capturing the spatio-temporal dynamics of carbon, water and energy cycles in tropical forests.
We simulated seasonal variations in Gross Primary Production (GPP) using different rooting depths (1, 3, 5, and 10 m) at point and spatial scales to investigate how rooting depth affects modeled seasonal GPP variations and to determine which rooting depth simulates GPP consistent with satellite-based observations. First, we confirmed that rooting depth strongly controls modeled GPP seasonal variations and that only deep rooting systems can successfully track flux-based GPP seasonality at the Tapajos km67 flux site. Second, spatial analysis showed that the model can reproduce the seasonal variations in satellite-based EVI seasonality, however, with required rooting depths strongly dependent on precipitation and the dry season length. For example, a shallow rooting depth (1-3m) is sufficient in regions with a short dry season (e.g. 0-2 months), and deeper roots are required in regions with a longer dry season (e.g. 3-5m and 5-10m for the regions with 3-4 and 5-6 months dry season, respectively). Our analysis suggests that setting of proper rooting depths is important to simulating GPP seasonality in tropical forests, and the use of satellite data can help to constrain the spatial variability of rooting depth. 

## Ichii, K

**Notes:**
Seasonal (MAM, JJA, SON, and DJF) variations in simulated GPP under different rooting depth (1, 5, and 10m) and satellite-based EVI, and correlation coefficients (R) of monthly variations of ∆EVI and ∆GPP. Averages from 1984 to 2000 for GPP and from 2001 to 2004 for EVI are calculated, and anomalies from May are shown. Positive ∆GPP shows more carbon fixation by vegetation. Three regions (shown as ovals in MAM) are defined to characterize the sensitivity of GPP to rooting depth