nasa-gesdisc/es-publications-researchareas huggingface dataset:
Quantification of Urban Forest and Grassland Carbon Fluxes Using Field Measurements and a Satellite‐Based Model in Washington DC/Baltimore Area (2022)

Cities are taking the lead on climate change mitigation with ambitious goals to reduce carbon dioxide (CO<SUB>2</SUB>) emissions. The implementation of effective mitigation policies will require accurate measurements to guide policy decisions and monitor their efficacy. Here, we present a comprehensive CO<SUB>2</SUB> inventory of an urban temperate forest and unmanaged grassland using field observations. We estimate the annual storage of CO<SUB>2</SUB> by vegetation and soils and place our biogenic flux estimates in the context of local fossil fuel (FF) emissions to determine when, where, and by how much biogenic fluxes alter net CO<SUB>2</SUB> flux dynamics. We compare our hourly estimates of biogenic fluxes in the forest site to modeled estimates using a modified version of Urban-Vegetation Photosynthesis and Respiration Model (Urban-VPRM) in Washington DC/Baltimore area presenting the first urban evaluation of this model. We estimate that vegetation results in a net biogenic uptake of -2.62 ± 1.9 Mg C ha<SUP>-1</SUP> yr<SUP>-1</SUP> in the forest site. FF emissions, however, drive patterns in the net flux resulting in the region being a net source of CO<SUB>2</SUB> on daily and annual timescales. In the summer afternoons, however, the net flux is dominated by the uptake of CO<SUB>2</SUB> by vegetation. The Urban-VPRM closely approximates hourly forest inventory based estimates of gross ecosystem exchange but overestimates ecosystem respiration in the dormant season by 40%. Our study highlights the importance of including seasonal dynamics in biogenic CO<SUB>2</SUB> fluxes when planning and testing the efficacy of CO<SUB>2</SUB> emission reduction polices and development of monitoring programs.

Research areas: Greenhouse Gases
Soil moisture drives the spatiotemporal patterns of asymmetry in vegetation productivity responses across China (2023)

Increasingly drastic global change is expected to cause hydroclimatic changes, which will influence vegetation productivity and pose a threat to the terrestrial carbon sink. Asymmetry represents an imbalance between vegetation growth and loss of growth during dry and wet periods, respectively. However, the mechanisms of asymmetric plant responses to hydrological changes remain poorly understood. Here, we examined the spatiotemporal patterns of asymmetric responses of vegetation productivity across terrestrial ecosystems in China. We analyzed several observational and satellite-based datasets of plant productivity and several reanalyzed datasets of hydroclimatic variables from 2001 to 2020, and used a random forest model to assess the importance of hydroclimatic variables for these responses. Our results showed that the productivity of >50 % of China's vegetated areas showed a more positive asymmetry (2.3  9.4 %) over the study period, which were distributed broadly in northwest China (mainly grasslands and sparse vegetation ecosystems). Negative asymmetries were most common in forest ecosystems in northeast China. We demonstrated that one-third of vegetated areas tended to exhibit significant changes in asymmetry during 20012020. The trend towards stronger positive asymmetry (0.95 % yr1) was higher than that towards stronger negative asymmetry (0.55 % yr1), which is beneficial for the carbon sink. We further showed that in China, soil moisture was a more important driver of spatiotemporal changes in asymmetric productivity than precipitation. We identified thresholds of surface soil moisture (2030 %, volume water content) and root-zone soil moisture (200350 mm, equivalent water height) that were associated with changes in asymmetry. Our findings highlight the necessity of considering the dynamic responses of vegetation to hydrological factors in order to fully understand the physiological growth processes of plants and avoid the possible loss of productivity due to future climate change.

Research areas: Land Surface/Agriculture Indicators
Quantifying Grass Coverage Trends to Identify the Hot Plots of Grassland Degradation in the Tibetan Plateau during 2000–2019 (2021)

Grassland covers 54% of the Tibetan Plateau (TP) and suffered overgrazing and degradation problems during past decades. To alleviate these problems, a series of policy measures have been implemented during recent two decades and inevitably caused changes of the grassland. To this end, this study quantitatively analyzed the grassland changes and the effects of reduced grazing intensity, and identified the hot plots of grassland degradation in the TP during 20002019. The grassland status was indicated by the Fractional Vegetation Cover in the green grass period (GP), i.e., FVCGP, and its changes and spatial variations were detected by analyzing the FVCGP trends and their distribution, using the MannKendal, Sens Slope, and ArcGIS buffering methods, and data of the MOD13Q1 Collection 6 products and other sources. The results showed that 62.12% of the grasslands were significantly increased in the FVCGP, and 28.34% had no apparent changes. The remaining 9.54% of the grassland significantly decreased in the FVCGP, mainly occurring in the areas nearby roads, rivers, and lakes, and distributed mostly in a point pattern. Of the total FVCGP decreased grassland area, 27.03% was clustered and identified as the hot plots of grassland degradation in six main regions. Decreased grazing intensity and increased precipitation contributed to the increase of grassland FVC in the TP, while local overgrazing could be the main cause of the FVC decrease. To strength the grassland restoration in the TP, the government supports and supervision should be enhanced to further mitigate the grassland pressure of animal grazing, particularly in the hot plot areas of degradation.

Research areas: Land Surface/Agriculture Indicators
Variations in surface albedo arising from flooding and desiccation cycles on the Bonneville Salt Flats, Utah (2019)

Abstract Desert playas, such as those in northern Utah, form a landscape often in stark contrast to surrounding mountain ranges due to their minimal topographic relief, lack of vegetation, and saline soils. Dry highly reflective halite surfaces, which make up many of the desert playas in northern Utah, are generally characterized by a surface albedo over 40%. However, their albedo can be reduced abruptly to less than 20% by flooding due to rainfall, runoff from surrounding higher terrain, or surface winds transporting shallow water across the playas. A weather station installed during September 2016 to study the Bonneville Salt Flats (BSF) in northern Utah provides estimates of surface albedo that can be related to cycles of flooding and desiccation of the halite surface. The normalized difference water index (NDWI) derived from the MODIS MOD09A1 land surface reflectance product estimates the fractional coverage of surface water over the BSF. NDWI values computed over 8-day periods from 2000 to 2018 highlight year-to-year and seasonal variations in playa flooding events over the BSF. Periods of playa flooding were observed with both ground-based observations and NDWI with sharp reductions in albedo when the surface is flooded.

Research areas: Floods
SoilGrids 2.0: producing soil information for the globe with quantified spatial uncertainty (2021)

Abstract. SoilGrids produces maps of soil properties for the entire globe at medium spatial resolution (250 m cell size) using state-of-the-art machine learning methods to generate the necessary models. It takes as inputs soil observations from about 240 000 locations worldwide and over 400 global environmental covariates describing vegetation, terrain morphology, climate, geology and hydrology. The aim of this work was the production of global maps of soil properties, with cross-validation, hyper-parameter selection and quantification of spatially explicit uncertainty, as implemented in the SoilGrids version 2.0 product incorporating state-of-the-art practices and adapting them for global digital soil mapping with legacy data. The paper presents the evaluation of the global predictions produced for soil organic carbon content, total nitrogen, coarse fragments, pH (water), cation exchange capacity, bulk density and texture fractions at six standard depths (up to 200 cm). The quantitative evaluation showed metrics in line with previous global, continental and large-region studies. The qualitative evaluation showed that coarse-scale patterns are well reproduced. The spatial uncertainty at global scale highlighted the need for more soil observations, especially in high-latitude regions.

Research areas: Validation
Current Global Land Systems Classifications: Comparison of Methods and Outputs (2022)

The anthropogenic impact on the functioning of natural systems and the concept of Anthropocene as a period of the human domination of the Earth has been widely discussed in literature in the past few decades. Consequently, several land systems classifications have been developed on a global scale to capture the diversity, intensity, and spatial distribution of the human suppression of natural stratification. This review presents the comparison of the most widely used complex global classifications, incorporating both natural conditions and the human influence on nature. Methods, input data, the number and type of output categories as well as their geographical extent and distribution are described and compared. The review will help potential users to find differences between available classifications and choose the right one for a particular use.

Research areas: Habitat Conversion/Fragmentation
A novel similar habitat potential model based on sliding-window technique for vegetation restoration potential mapping (2020)

Vegetation restoration potential mapping (VRPM) is of great importance for regional ecosystem restoration planning after long‑term land degradation or short‑term disasters. However, there are some problems to be solved in the current models for evaluating the potential of vegetation restoration. First, the models for VRPM are mostly established based on a knowledge‑driven index system. Although this kind of model is logically rigorous, it relies too much on expert knowledge and is relatively inefficient, especially for large‑area vegetation restoration assessments. Second, because of the spatial heterogeneity, as well as the absence of important indicators, the traditional global‑based evaluation models are difficult to adapt to the entire study area. In this study, an improved data‑driven approach, that is, a sliding‑window based similar habitat potential model, is developed for VRPM. The advantages of the new model include: (a) it is more efficient in determining the importance of each influencing factor without resorting to expert knowledge; (b) it is more locally adaptive than the global model because it performs sample training, rule building, and vegetation restoration potential calculation in each current local window. We provide a case‑study to show the modeling process and result interpretation of the new model.

Research areas: Land Surface/Agriculture Indicators
Simulating carbon and water fluxes using a coupled process-based terrestrial biosphere model and joint assimilation of leaf area index and surface soil … (2022)

Abstract. Reliable modeling of carbon and water fluxes is essential for understanding the terrestrial carbon and water cycles and informing policy strategies aimed at constraining carbon emissions and improving water use efficiency. We designed an assimilation framework (LPJ-Vegetation and soil moisture Joint Assimilation, or LPJ-VSJA) to improve gross primary production (GPP) and evapotranspiration (ET) estimates globally. The integrated model, LPJ-PM (LPJ-PT-JPLSM Model) as the underlying model, was coupled from the LundPotsdamJena Dynamic Global Vegetation Model (LPJ-DGVM version 3.01) and a hydrology module (i.e., the updated PriestleyTaylor Jet Propulsion Laboratory model, PT-JPLSM). Satellite-based soil moisture products derived from the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active and Passive (SMAP) and leaf area index (LAI) from the Global LAnd and Surface Satellite (GLASS) product were assimilated into LPJ-PM to improve GPP and ET simulations using a proper orthogonal decomposition (POD)-based ensemble four-dimensional variational assimilation method (PODEn4DVar). The joint assimilation framework LPJ-VSJA achieved the best model performance (with an R2 ( coefficient of determination) of 0.91 and 0.81 and an ubRMSD (unbiased root mean square deviation) reduced by 40.3 % and 29.9 % for GPP and ET, respectively, compared with those of LPJ-DGVM at the monthly scale). The GPP and ET resulting from the assimilation demonstrated a better performance in the arid and semi-arid regions (GPP: R2 = 0.73, ubRMSD = 1.05 g C m2 d1; ET: R2 = 0.73, ubRMSD = 0.61 mm d1) than in the humid and sub-dry humid regions (GPP: R2 = 0.61, ubRMSD = 1.23 g C m2 d1; ET: R2 = 0.66; ubRMSD = 0.67 mm d1). The ET simulated by LPJ-PM that assimilated SMAP or SMOS data had a slight difference, and the SMAP soil moisture data performed better than SMOS data. Our global simulation modeled by LPJ-VSJA was compared with several global GPP and ET products (e.g., GLASS GPP, GOSIF GPP, GLDAS ET, and GLEAM ET) using the triple collocation (TC) method. Our products, especially ET, exhibited advantages in the overall error distribution (estimated error (): 3.4 mm per month; estimated standard deviation of : 1.91 mm per month). Our research showed that the assimilation of multiple datasets could reduce model uncertainties, while the model performance differed across regions and plant functional types. Our assimilation framework (LPJ-VSJA) can improve the model simulation performance of daily GPP and ET globally, especially in water-limited regions.

Research areas: Land Surface/Agriculture Indicators, Validation
A novel framework for evaluating the effect of vegetation restoration via the grazing‐exclusion‐by‐fencing project: a case study from the Qinghai‐Tibet Plateau (2022)

Grazing exclusion by fencing (GEF) has been implemented to prevent the deterioration of grassland ecosystems in China since the beginning of this century; meanwhile, the effects have attracted widespread attention from the academic community. However, due to the simultaneity of different factors, it is difficult to separate the effect of different policies from natural resource endowments and identify the independent role of each policy. In this study, a novel framework was established based on spatiotemporal statistics. First, an indicator‑denoted vegetation restoration potential achievement degree (VRPAD) was introduced to lessen the impact of resource endowment conditions. Second, a double‑difference model of both space and time was developed to reflect the net improvement of VRPAD brought by GEF. The case study showed that 15 out of the 17 fenced‑off enclosures achieved positive VRPAD growth since the GEF implementation, while only 58.8% showed a much faster improvement or a slower degeneration than their adjacent areas. It was also found GEF effect presented apparent administrative differences in terms of spatial distribution. The number of effective fences accounted for 75% in Mami Town, but it was 0 in Chabu Town. It is concluded that the role of GEF may be overestimated, multi‑pronged approaches are more conducive to vegetation restoration, and strengthening grassroots management and self‑policing of fences is important to improve GEF efficiency. This research provides a novel framework for distinguishing policy effect and is expected to inspire new ideas and methodological support for policy formulation and evaluation.

Research areas: Land Surface/Agriculture Indicators
Increased Surface Broadband Emissivity Driven by Denser Vegetation on the Tibetan Plateau Grassland Area (2020)

Global changes are profoundly affecting the global terrestrial ecosystems, especially for the vegetation. Simultaneously, the affected vegetation gives feedback to the climates. The Tibetan Plateau (TP), one of the most sensitive areas to global changes, has undergone extraordinary changes on its ecosystem processes. In the multitudinous land surface ecosystem processes affecting the climate, the process of land surface energy balance affecting by vegetation activity is one of the most important and still has not been well recognized. The spatial and temporal patterns of the broadband emissivity (BBE) on the TP and its relations to the vegetation activity and land surface temperature were examined in this research. We find that elevated BBE is regulated by increasing vegetation activity for grasslands over the TP from 2000 to 2015. The spatial patterns of BBE and its interannual changes are highly correlated with vegetation activity. The BBE changing rate generally declines along rising elevation, due to the shrunk effects from vegetation activity. A greater sensitivity of BBE to vegetation activity occurs in the sparse vegetation area or high elevation zone than in the dense vegetation area or low elevation zone. Increasing BBE has a cooling effect on the land surface, especially at night. This cooling effect is related to wind speed. The growing season BBE trend as regulated by vegetation activity highlights the importance to take mounting notice of the growing season long-wave energy fluxes of surface energy balance studies in the future.

Research areas: Land Surface/Agriculture Indicators
Spatial patterns and climatic drivers of leaf spring phenology of maple in eastern North America (2023)

The resurgent frequency of extreme weather events and their strongly distinctive spatial patterns lead to a growing interest in phenology as an indicator of tree susceptibility. Using a long-term chronology of observations collected in situ, we predicted and investigated the spatial patterns and environmental drivers of spring leaf phenology across maple stand polygons dominated by Acer saccharum Marsh. and/or Acer rubra L. in eastern North America for 20002018. Model calibration was based on Bayesian ordinal regressions relating the timing of the phenological events' observations to the MODIS vegetation indices EVI, NDVI and LAI. DAYMET data have been extracted to compute temperature and precipitation during spring phenology. Model accuracy increased as the season progressed, with prediction uncertainty spanning from 9 days for bud swelling to 4 days for leaf unfolding. NDVI and LAI were the best predictors for the onset and ending of spring phenology, respectively. Bud swelling occurred at the end of March in the early stands and at the onset of May in the late stands, while leaf unfolding was completed at the beginning of April for the early and in mid-June for the late stands. Early and late stands polarized towards a south-westnorth-east gradient. In the south-western regions, which are also the driest, total precipitation and minimum temperature explained respectively 73 % and 25 % of the duration of spring phenology. In the north-eastern regions, precipitation and minimum temperature explained 62 % and 26 % of the duration of spring phenology. Our results suggest high vulnerability to extreme weather events in stands located in the south-west of the species distribution. The increasing incidence of drought in these locations might affect spring phenology, decreasing net primary production in these stands. Warmer nights might expose the buds to late frosts, events that are expected to become more frequent in the coming years.

Research areas: Ecosystems
Sedimentary organic carbon storage of thermokarst lakes and ponds across Tibetan permafrost region (2022)

Sedimentary soil organic carbon (SOC) stored in thermokarst lakes and ponds (hereafter referred to as thaw lakes) across high-latitude/altitude permafrost areas is of global significance due to increasing thaw lake numbers and their high C vulnerability under climate warming. However, to date, little is known about the SOC storage in these lakes, which limits our better understanding of the fate of these active carbon in a warming future. Here, by combining large-scale field observation data and published deep (e.g., 0300 cm) permafrost SOC data with a random forest (RF) machine learning technique, we provided the first comprehensive estimation of thaw lake SOC stocks to 3 m depth on the Tibetan Plateau. This study demonstrated that combining multiple environmental factors with the RF model could effectively predict the spatial distributions of the thaw lake SOC density values (SOCDs). The model results revealed that the soil respiration, normalized difference vegetation index (NDVI), and mean annual precipitation (MAP) were the most influential factors for predicting thaw lake SOCDs. In total, the sedimentary SOC stocks in the thaw lakes were approximately 52.62 Tg in the top 3 m, with 53% of the SOC stored in the upper layers (0100 cm). The SOCDs generally exhibited high values in eastern Tibetan Plateau, and low values in mid- and western Tibetan Plateau, which were similar to the patterns of the land cover types that affected the SOCDs. We further found that the SOCDs of thaw lakes were generally higher than those of their surrounding permafrost soils at different layer depths, which could be ascribed to the erosion of soil particles or leaching solution from the thawing permafrost soils to lakes and/or enhanced vegetation growth at the lake bottom. This research highlights the necessity of explicitly considering the thaw lake SOC stocks in Earth system models for more comprehensive future projections of the carbon dynamics on the plateau.

Research areas: Cryospheric Indicators, Land Surface/Agriculture Indicators
Deciphering the many maps of the Xingu River Basin–an assessment of land cover classifications at multiple scales (2020)

Remote sensing is an invaluable tool to objectively illustrate the rapid decline in habitat extents worldwide. The many operational Earth Observation platforms provide options for the generation of land cover maps, each with unique characteristics and considerable semantic differences in the definition of classes. As a result, differences in baseline estimates are inevitable. Here we compare forest cover and surface water estimates over four time periods spanning three decades (19892018) for 1.3 million km2 encompassing the Xingu River Basin, Brazil, from published, freely accessible remotely sensed land cover classifications. While all showed a decrease in forest extent over time, the total deforested area reported by each ranged widely for all time periods. The greatest differences ranged from 9% to 17% (116,958 to 219,778 km2) deforestation of the total area for year 2000 and 20142018 time period, respectively. We also show the high sensitivity of forest fragmentation metrics (entropy and foreground area density) to data quality and spatial resolution, with cloud cover and sensor artefacts resulting in errors. Surface water classifications must be chosen carefully because sources differ greatly in location and mapped area of surface water. After operationalization of the Belo Monte dam complex, the large reservoirs are notably absent from several of the classifications illustrating land cover. Freshwater ecosystem health is influenced by the land cover surrounding water bodies (e.g., riparian zones). Understanding differences between the many remotely sensed baselines is fundamentally important to avoid information misuse, and to objectively choose the most appropriate classification for ecological studies, conservation, or policy making. The differences between the classifications examined here are not a failure of the technology, but due to different interpretations of forest cover and characteristics of the input data (e.g., spatial resolution). Our findings demonstrate the importance of transparency in the generation of remotely sensed classifications and the need for users to familiarize themselves with the characteristics and limitations of each data set.

Research areas: Habitat Conversion/Fragmentation