nasa-gesdisc/es-publications-researchareas huggingface dataset:
Spatio-temporal quality distribution of MODIS vegetation collections 5 and 6: implications for forest-non-forest separability (2019)

Moderate Resolution Imaging Spectroradiometer (MODIS) has been employed for continuous monitoring of land surface dynamics to facilitate the examination of spatial aspects of the environment. Periodical generation of MODIS products enables temporal analysis, and the interpretation of temporal patterns requires information about image quality. The MODIS Scientific Data Set (SDS) provides information on image properties. Some research has utilized the SDS to assist in analysis and interpretation, particularly in supporting time series forecasting and estimating invalid data from near-dates observation. Our research compares the usability and reliability information provided in the MODIS SDS for collections 5 and 6 to describe the spatio-temporal distribution of image quality. This research compared the ability of the MODIS collections to identify the extent of water and to differentiate forest from non-forest. Four sites representing tropical and temperate regions were selected in Brazil, Congo, Colorado (United States of America), and the European Alps. The robustness of usability and reliability information for assessing MODIS vegetation collections 5 and 6 was compared over these sites by using 16-day composite products over a year of observations (2015). The spatio-temporal distribution of invalid pixels and gaps derived from usability and reliability information were assessed by using TiSeG (Time series Generator) and GeoDa. Morans I indicated a large number of invalid pixels and temporal gaps were clustered in a few areas. Collection 6 appears more consistent in the identification of waterbodies, either for inland water or ocean, but the error detection of ice fractions in two tropical sites tends to increase. Masking data by using Quality Assurance (QA)-SDS information improved the separability between forest and non-forest. This research demonstrated that evaluating the quality of image products using the SDS assisted the selection of period and location to better differentiate forest and non-forest. The seasonal fluctuation of separability metrics showed the importance of exploring temporal pattern for better understanding of the dynamics of forest cover.

Research areas: Land Surface/Agriculture Indicators, Validation
The spatial distribution of critical wind erosion centers according to the dust event in Hormozgan province (south of Iran) (2018)

Wind erosion and its consequent dust storms has become one of the environmental risks in today's world, which has annually caused non-compensable destruction in environment and human health. Since wind erosion is one of the main factors of desertification which could result in dust storms, studying and locating the wind erosion centers in southern parts of Iran is of crucial importance. The aim of this study is to determine the spatial distribution of wind erosion centers associated to local dust event in Hormozgan province (south of Iran). Different factors such as soil features, climate, surface roughness, vegetation cover, topography and the length wind exposure should be investigated for zoning the potential wind erosion regions. Studies showed that many of these factors are relatively uniform for the study region. Therefore, zoning based on all of the above-mentioned factors is not efficient. For this reason, soil texture along with vegetation cover and topography were studied in the current study to zone wind erosion. First, satellite data of soil texture, normalized differential vegetation index (NDVI), and topography were used to address the potential regions of wind erosion in warm and cold periods of the year by ArcGIS. Then, the data of 14 synoptic stations in Hormozgan province were utilized to plot the maps of dust event occurrences. Finally, by combination of the satellite and synoptic data, the map of land sensitivity toward wind erosion was provided, and the obtained results were compared with observations of Forest, Ranges and Watershed Organization (FRWO) of Hormozgan province. The results indicated that there are potential regions for wind erosion and dust sources in the study region. In a way that coastal areas have the highest probability to become wind erosion centers. In this regard, Jask, Bandarabbas and Bandar Lengeh are the first three regions in terms of wind erosion; while Abu Musa has the lowest priority in terms of possessing wind erosion centers. Also, it was revealed that sensitivity to wind erosion and dust storms was higher in warm periods of the year as compared with the cold seasons. The results of this study are in agreement with the observations of FRWO of Hormozgan province. Therefore, desert greening measures and actions to prevent wind erosion can control many of dust storms in the regions.

Research areas: Land Surface/Agriculture Indicators
A Newly Identified Role of the Deciduous Forest Floor in the Timing of Green-Up (2017)

Plant phenology studies rarely consider controlling factors other than air temperature. We evaluate here the potential significance of physical and chemical properties of soil (edaphic factors) as additional important controls on phenology. More specifically, we investigate causal connections between satellite-observed green-up dates of small forest watersheds and soil properties in the Adirondack Mountains of New York, USA. Contrary to the findings of previous studies, where edaphic controls of spring phenology were found to be marginal, our analyses show that at least three factors manifest themselves as significant controls of seasonal patterns of variation in vegetated land surfaces observed from remote sensing: (1) thickness of the forest floor, (2) concentration of exchangeable soil potassium, and (3) soil acidity. For example, a thick forest floor appears to delay the onset of green-up. Watersheds with elevated concentrations of potassium are associated with early surface greening. We also found that trees growing in strongly acidified watersheds demonstrate delayed green-up dates. Overall, our work demonstrates that, at the scale of small forest watersheds, edaphic factors can explain a significant percentage of the observed spatial variation in land surface phenology that is comparable to the percentage that can be explained by climatic and landscape factors. We conclude that physical and chemical properties of forest soil play important roles in forest ecosystems as modulators of climatic drivers controlling the rate of spring soil warming and the transition of trees out of winter dormancy.

Research areas: Land Surface/Agriculture Indicators
Abiotic and biotic factors controlling the dynamics of soil respiration in a coastal dune ecosystem in western Japan (2022)

In this study, we examined the abiotic and biotic factors controlling the dynamics of soil respiration (Rs) while considering the zonal distribution of plant species in a coastal dune ecosystem in western Japan, based on periodic Rs data and continuous environmental data. We set four measurement plots with different vegetation compositions: plot 1 on bare sand; plot 2 on a cluster of young Vitex rotundifolia seedlings; plot 3 on a mixture of Artemisia capillaris and V. rotundifolia; and plot 4 on the inland boundary between the coastal vegetation zone and a Pinus thunbergii forest. Rs increased exponentially along with the seasonal rise in soil temperature, but summer drought stress markedly decreased Rs in plots 3 and 4. There was a significant positive correlation between the natural logarithm of belowground plant biomass and Rs in autumn. Our findings indicate that the seasonal dynamics of Rs in this coastal dune ecosystem are controlled by abiotic factors (soil temperature and soil moisture), but the response of Rs to drought stress in summer varied among plots that differed in dominant vegetation species. Our findings also indicated that the spatial dynamics of Rs are mainly controlled by the distribution of belowground plant biomass and autotrophic respiration.

Research areas: Ecosystems, Land Surface/Agriculture Indicators
Analysis of groundwater and total water storage changes in Poland using GRACE observations, in-situ data, and various assimilation and climate models (2019)

The Gravity Recovery and Climate Experiment (GRACE) observations have provided global observations of total water storage (TWS) changes at monthly intervals for over 15 years, which can be useful for estimating changes in GWS after extracting other water storage components. In this study, we analyzed the TWS and groundwater storage (GWS) variations of the main Polish basins, the Vistula and the Odra, using GRACE observations, in-situ data, GLDAS (Global Land Data Assimilation System) hydrological models, and CMIP5 (the World Climate Research Programmes Coupled Model Intercomparison Project Phase 5) climate data. The research was conducted for the period between September 2006 and October 2015. The TWS data were taken directly from GRACE measurements and also computed from four GLDAS (VIC, CLM, MOSAIC, and NOAH) and six CMIP5 (FGOALS-g2, GFDL-ESM2G, GISS-E2-H, inmcm4, MIROC5, and MPI-ESM-LR) models. The GWS data were obtained by subtracting the model TWS from the GRACE TWS. The resulting GWS values were compared with in-situ well measurements calibrated using porosity coefficients. For each time series, the trends, spectra, amplitudes, and seasonal components were computed and analyzed. The results suggest that in Poland there has been generally no major TWS or GWS depletion. Our results indicate that when comparing TWS values, better compliance with GRACE data was obtained for GLDAS than for CMIP5 models. However, the GWS analysis showed better consistency of climate models with the well results. The results can contribute toward selection of an appropriate model that, in combination with global GRACE observations, would provide information on groundwater changes in regions with limited or inaccurate ground measurements.

Research areas: Land Surface/Agriculture Indicators
A parametric sensitivity analysis for prioritizing regolith knowledge needs for modeling water transfers in the West African critical zone (2021)

Hard rock aquifers (HRAs) in West Africa (WA) are located within a thick regolith layer. The representation of thick tropical regolith in integrated hydrological models lacks consensus on aquifer geometries and parameter ranges. Our main objective was to determine the knowledge requirements on saturated hydraulic conductivity (Ks) to model the critical zone (CZ) of HRAs in WA. A parametric sensitivity analysis with a focus on the representation of the Ks heterogeneity of the regolith was conducted with a critical zone model (Parflow‑CLM [Community Land Model]) of the Upper Ouémé catchment in Benin (14,000 km2) at a 1‑ × 1‑km2 resolution. The impact of parameter changes in the near subsurface (0.3‑to‑5‑m depth) and in the deeper regolith aquifer (24‑ and 48‑m maximum depth) was assessed in five modeling experiments. Streamflow was largely dependent on Ks and on clay distribution in the near subsurface and less on the properties of the deeper subsurface. Groundwater table depths and amplitudes were controlled by vegetation and topography as observed on instrumented hillslopes and for Ks within the literature range. Experiments with higher Ks suggested a Ks threshold where dynamics become less determined by one‑dimensional vertical and more determined by lateral processes. Such heterogeneity impacts from smaller scales need to be accounted for when hydrological models are upscaled to larger domains (1‑ × 1‑km2 resolution or coarser). Our findings highlight the need for a new conceptual approach to represent clay distribution in order to develop catchment‑scale CZ models of HRAs in WA that capture the observed processes.Core Ideas Simulated water balance components for a catchment in West Africa were confirmed by observations. Subsurface (0.3‑to‑5‑m depth) exerts stronger control on streamflow than deeper regolith. Ks magnitude determines transition from topography‑ to recharge‑controlled water table dynamics. We identified a limit of Ks where the impact of one‑dimensional processes on water table dynamics ceases. A high‑permeability fissured zone at the bottom shows little impact on the simulations.

Research areas: Land Surface/Agriculture Indicators
Future Global Soil Respiration Rates Will Swell Despite Regional Decreases in Temperature Sensitivity Caused by Rising Temperature (2018)

Between 1960 and 2014, the global soil respiration (R<SUB>SG</SUB>) flux increased at a rate of 0.05 Pg C year<SUP>-1</SUP>; however, future increase is uncertain due to variations in projected temperature and regional heterogeneity. Regional differences in the sensitivity of soil respiration (R<SUB>S</SUB>) to temperature may alter the overall increase in rates of R<SUB>S</SUB> because the R<SUB>S</SUB> rates of some regions may decelerate while others continue to rise. Using monthly global R<SUB>S</SUB> data, we modeled the relationship between R<SUB>S</SUB> and temperature for the globe and eight climate regions and estimated R<SUB>SG</SUB> between 1961and 2100 using historical (1961-2014) and future (2015-2100) temperature data [Representative Concentration Pathways (RCP2.6 and RCP8.5)]. Importantly, our approach allowed for estimation of regional sensitivity, where respiration rates may peak or decline as temperature rises. Estimated historical R<SUB>SG</SUB> increase (0.05 Pg C year<SUP>-1</SUP>) was similar to the R<SUB>SG</SUB> increase of previous estimates. However, under the RCP8.5 scenario, which estimates approximately 3 °C of warming globally, the forecasted acceleration of R<SUB>SG</SUB> increased to an average of 0.12 Pg C year<SUP>-1</SUP>. Under RCP8.5, the temperature sensitivity of R<SUB>S</SUB> declined in the arid, winter-dry temperate, and tropic. These regional declines were offset by increased R<SUB>S</SUB> sensitivity and fluxes from the boreal and polar regions. In contrast, under RCP2.6 R<SUB>SG</SUB> decelerated slightly from current rates. If rising greenhouse gas emission remains unmitigated, future increases in R<SUB>SG</SUB> will be much faster than current and historical rates, thereby possibly enhancing future losses of soil carbon and contributing to positive feedback loops of climate change.

Research areas: Greenhouse Gases
Land surface phenology as indicator of global terrestrial ecosystem dynamics: A systematic review (2020)

Vegetation phenology is considered an important biological indicator in understanding the behaviour of ecosystems and how it responds to environmental cues. Changes in vegetation dynamics have been strongly linked to the variability of climate patterns and may have an important impact on the ecological processes of ecosystems, such as the land surface-atmosphere exchange of water and carbon, energy flows and interaction between different species. Land surface phenology (LSP) is the study of seasonal patterns in plant phenophases based on time series from vegetation indices (VI) or biophysical variables derived from satellite data, and has played an essential role in monitoring the response of terrestrial ecosystems to environmental changes from local to global scales. The goal of this systematic literature review is to provide a detailed synthesis of the main contributions of the global LSP research to the development of environmental knowledge and remote sensing science and technology, identifying possible gaps that could be addressed in the coming years. This systematic review found that the number of LSP studies has grown exponentially since the 1980s, although the analysis of phenological metrics or phenometrics derived from satellite data (i.e. proxies for the biological phenophases of plants) has focused specifically on ecosystems located in the mid- and high-altitude in the Northern Hemisphere (e.g. boreal forest/taiga, evergreen, deciduous or mixed temperate forest). LSP studies use different satellite dataset and methods to estimate phenometrics. These studies identified an advance in spring and a delay in autumn phenophases as general trends. Although these trends were associated mainly to changes in temperature and precipitation, phenological cycle dynamics might be related to other drivers, such as photoperiod, soil moisture or organic carbon content, among others. Therefore, this interaction between different climatic and non-climatic drivers make phenology modelling a difficult task. Hence, in the coming years, a greater integration of LSP data into ecological process modelling could provide a more complete overview on the terrestrial ecosystems functioning. Furthermore, different technical and methodological aspects (e.g. greater temporal coverage of recent high-spatial-resolution satellites, advances in remote-sensing technology or improved efficiency in the computational processing of geospatial data) may also contribute to improve our understanding of Earths ecosystem dynamics and their environmental drivers.

Research areas: Land Surface/Agriculture Indicators
Leveraging Pre‐Storm Soil Moisture Estimates for Enhanced Land Surface Model Calibration in Ungauged Hydrologic Basins (2022)

Despite long-standing efforts, hydrologists still lack robust tools for calibrating land surface model (LSM) streamflow estimates within ungauged basins. Using surface soil moisture estimates from the Soil Moisture Active Passive Level 4 Soil Moisture (L4_SM) product, precipitation observations, and streamflow gauge measurements for 617 medium-scale (200-10,000 km<SUP>2</SUP>) basins in the contiguous United States, we measure the temporal (Spearman) rank correlation between antecedent (i.e., pre-storm) surface soil moisture (ASM) and the storm-scale runoff coefficient (RC; the fraction of storm-scale precipitation accumulation converted into streamflow). In humid and semi-humid basins, this rank correlation is shown to be sufficiently strong to allow for the substitution of storm-scale RC observations (available only in basins that are both lightly regulated and gauged) with high-quality ASM values (available quasi-globally from L4_SM) in streamflow calibration procedures. Using this principle, we define a new, basin-wise LSM streamflow calibration approach based on L4_SM alone and successfully apply it to identify LSM configurations that produce a high rank correlation with observed RC. However, since the approach cannot detect RC bias, it is less successful in identifying LSM configurations with low mean-absolute error.

Research areas: Validation
Empirical evidence of contrasting soil moisture–precipitation feedbacks across the United States (2016)

The effects of rainfall on rainfall
            Soil moisture, which is controlled in part by past rainfall, can affect the probability of future rainfall over large areas. This is because the water contained in soils helps determine how sunlight is converted into latent heat (evaporation) and sensible heat (which increases overlying air temperatures). Tuttle and Salvucci used data collected for the contiguous United States over 10 years to study this relationship. The feedback between soil moisture and rainfall is generally positive in the western United States but negative in the east. This regional dependence could be a function of large-scale differences in aridity.
            
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            Past rainfall can affect the probability of future rainfall over large areas.
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            Soil moisture influences fluxes of heat and moisture originating at the land surface, thus altering atmospheric humidity and temperature profiles. However, empirical and modeling studies disagree on how this affects the propensity for precipitation, mainly owing to the difficulty in establishing causality. We use Granger causality to estimate the relationship between soil moisture and occurrence of subsequent precipitation over the contiguous United States using remotely sensed soil moisture and gauge-based precipitation observations. After removing potential confounding effects of daily persistence, and seasonal and interannual variability in precipitation, we find that soil moisture anomalies significantly influence rainfall probabilities over 38% of the area with a median factor of 13%. The feedback is generally positive in the west and negative in the east, suggesting dependence on regional aridity.

Research areas: Atmospheric/Ocean Indicators
Revising precipitation  water storages  vegetation signatures with GRACE-based data assimilation (2022)

The availability of freshwater is highly influenced by climate change, extreme climate events and by anthropogenic use. Countries where a large part of the population depends on the agricultural sector, such as South Africa, are strongly affected by changes in climate, which emphasizes that water is an essential source for food production and drinking water. To analyze changes in surface and subsurface water, model simulations and in situ data are commonly used. However, both have limitations, for example, the models rely on potentially erroneous forcing data and insufficient process representations and the in situ data do not represent the larger-scale weather and climate due to spatial and temporal heterogeneity. This can be mitigated by assimilating remote-sensed satellite data into models. In this research, we build a realistic picture of the water and its propagation (measured between peak times) from fluxes as precipitation, to its way through the storages and its impact on vegetation at the 50 km scale by using observation-based data. The observations are derived from MODIS remote-sensing and integrating GRACE total water storage anomaly (TWSA) observations into a hydrological model via data assimilation. Our objective is to identify shortcomings in model simulations by confronting them with the (synthesized) observations. Moreover, we demonstrate the importance of integrating observations into the models. We base these comparisons on signatures or sub-signals (e.g., temporal lags and annual amplitudes) that we derive via regression analysis, principal component analysis, sensitivity analysis and correlation analysis from the synthetic data and the model output. Our main results show that correlations and signatures in real observations are found weaker as compared to what is simulated in the model, e.g. for the contribution of precipitation to groundwater. Lag times between precipitation and surface and groundwater storage peaks are observed to be longer than in the model. The observed propagation of soil water from storages to vegetation is often shorter than in the model, while for groundwater it is longer. We believe our findings will be highly relevant for modelers; the gained knowledge can be used to improve models. In addition, we feel our study underlines the potential of GRACE assimilation into hydrological models.

Research areas: Validation, Land Surface/Agriculture Indicators
Multi-modal temporal CNNs for live fuel moisture content estimation (2022)

Live fuel moisture content (LFMC) is an important environmental indicator used to measure vegetation conditions and monitor for high fire risk conditions. However, LFMC is challenging to measure on a wide scale, thus reliable models for estimating LFMC are needed. Therefore, this paper proposes a new deep learning architecture for LFMC estimation. The architecture comprises an ensemble of temporal convolutional neural networks that learn from year-long time series of meteorological and reflectance data, and a few auxiliary inputs including the climate zone. LFMC estimation models are designed for two training and evaluation scenarios, one for sites where historical LFMC measurements are available (within-site), the other for sites without historical LFMC measurements (out-of-site). The models were trained and evaluated using a large database of LFMC samples measured in the field from 2001 to 2017 and achieved an RMSE of 20.87% for the within-site scenario and 25.36% for the out-of-site scenario.

Research areas: Wildfires