nasa-gesdisc/es-publications-researchareas huggingface dataset:
HMRFS-TP: long-term daily gap-free snow cover products over the Tibetan (2022)

Abstract. Snow cover plays an essential role in climate change and
the hydrological cycle of the Tibetan Plateau. The widely used Moderate
Resolution Imaging Spectroradiometer (MODIS) snow products have two major
issues: massive data gaps due to frequent clouds and relatively low estimate
accuracy of snow cover due to complex terrain in this region. Here we
generate long-term daily gap-free snow cover products over the Tibetan
Plateau at 500 m resolution by applying a hidden Markov random field (HMRF)
technique to the original MODIS snow products over the past two decades. The
data gaps of the original MODIS snow products were fully filled by optimally
integrating spectral, spatiotemporal, and environmental information within
HMRF framework. The snow cover estimate accuracy was greatly increased by
incorporating the spatiotemporal variations of solar radiation due to
surface topography and sun elevation angle as the environmental contextual
information in HMRF-based snow cover estimation. We evaluated our snow
products, and the accuracy is 98.29 % in comparison with in situ observations, and
91.36 % in comparison with high-resolution snow maps derived from Landsat
images. Our evaluation also suggests that the incorporation of
spatiotemporal solar radiation as the environmental contextual information
in HMRF modeling, instead of the simple use of surface elevation as the
environmental contextual information, results in the accuracy of the snow
products increases by 2.71 % and the omission error decreases by 3.59 %.
The accuracy of our snow products is especially improved during snow
transitional period, and over complex terrains with high elevation and
sunny slopes. The new products can provide long-term and spatiotemporally
continuous information of snow cover distribution, which is critical for
understanding the processes of snow accumulation and melting, analyzing its
impact on climate change, and facilitating water resource management in
Tibetan Plateau. This dataset can be freely accessed from the National
Tibetan Plateau Data Center at https://doi.org/10.11888/Cryos.tpdc.272204
(Huang and Xu, 2022).

Research areas: Cryospheric Indicators
Soil moisture experiment in the Luan River supporting new satellite mission opportunities (2020)

The Soil Moisture Experiment in the Luan River (SMELR) was conducted from 2017 to 2018 in the semi-arid Luan River watershed located in the North of China. One of the objectives of SMELR is to serve as an assessment tool and demonstration for a new Terrestrial Water Resources Satellite (TWRS) concept with one-dimensional synthetic aperture microwave techniques, for which soil moisture retrieval under variable satellite observing configurations (mainly in terms of incidence angels) is the greatest challenge. This proposed mission is targeted to provide continuity for the current satellite L-band microwave observations, and further improve the accuracy and spatial resolution of soil moisture mapping through the synergistic use of active, passive and optical remote sensing data. Multi-resolution, multi-angle and multi-spectral airborne data were obtained four times over a 70 km by 12 km area in the Shandian River basin, and one time over a 165 km by 5 km area that includes the Xiaoluan River basin. The near surface soil moisture (0 cm5 cm) was measured extensively on the ground in fifty 1 km by 2 km quadrats (targeted to compare with the airborne radiometer), and two hundred and fifty 200 m by 200 m quadrats corresponding to radar observations. Two networks were established for continuous measurement of the soil moisture and temperature profile (3 cm, 5 cm, 10 cm, 20 cm, 50 cm) and precipitation in the Shandian and Xiaoluan River basin, respectively. Supporting ground measurements also included ground temperature, vegetation water content, surface roughness, continuous measurement of microwave emission and backscatter at a pasture site, reflectance of various land cover types, evapotranspiration and aerosol observations. Preliminary results within the experimental area indicate that (1) the near surface soil moisture spatial variability at a 200 m scale was up to ~0.1 cm3/cm3 at an intermediate value of ~0.35 cm3/cm3. (2) The difference of soil and vegetation temperature in grass and croplands reach its maximum of 11 K around solar noon time, and the soil temperature gradient is largest at around 15 P.M. (3) Both the airborne and ground measurements cover a wide range of conditions. The L-band active and passive observations exhibit a large variation of ~30 dB and ~80 K, respectively, corresponding to soil moisture range from 0.1 cm3/cm3 to 0.5 cm3/cm3. The sensitivity of both active and passive data to soil moisture is compared at corresponding spatial resolutions and show high information complementarity for better accuracy and resolution soil moisture retrieval.

Research areas: Land Surface/Agriculture Indicators
Assimilation of gridded GRACE terrestrial water storage estimates in the North American Land Data Assimilation System (2016)

Abstract The objective of the North American Land Data Assimilation System (NLDAS) is to provide best-available estimates of near-surface meteorological conditions and soil hydrological status for the continental United States. To support the ongoing efforts to develop data assimilation (DA) capabilities for NLDAS, the results of Gravity Recovery and Climate Experiment (GRACE) DA implemented in a manner consistent with NLDAS development are presented. Following previous work, GRACE terrestrial water storage (TWS) anomaly estimates are assimilated into the NASA Catchment land surface model using an ensemble smoother. In contrast to many earlier GRACE DA studies, a gridded GRACE TWS product is assimilated, spatially distributed GRACE error estimates are accounted for, and the impact that GRACE scaling factors have on assimilation is evaluated. Comparisons with quality-controlled in situ observations indicate that GRACE DA has a positive impact on the simulation of unconfined groundwater variability across the majority of the eastern United States and on the simulation of surface and root zone soil moisture across the country. Smaller improvements are seen in the simulation of snow depth, and the impact of GRACE DA on simulated river discharge and evapotranspiration is regionally variable. The use of GRACE scaling factors during assimilation improved DA results in the western United States but led to small degradations in the eastern United States. The study also found comparable performance between the use of gridded and basin-averaged GRACE observations in assimilation. Finally, the evaluations presented in the paper indicate that GRACE DA can be helpful in improving the representation of droughts.

Research areas: Validation
Seasonal Precipitation Legacy Effects Determine the Carbon Balance of a Semiarid Grassland (2019)

Semiarid grasslands are water-limited ecosystems where precipitation (PPT) controls the onset and duration of the growing season; however, this variable does not fully explain interannual variability of productivity at temporal scales. We examined the relationship between PPT and carbon (C) fluxes in a semiarid grassland at both seasonal and interannual scales, as well as the role of lagged effects of PPT and asymmetric sensitivities of net ecosystem carbon exchange to PPT and its components (gross ecosystem exchange [GEE] and ecosystem respiration [ER]). Six years of continuous net ecosystem C exchange data measured with the eddy covariance technique and GEE estimated with 15 years of enhanced vegetation index and the gross primary productivity of Moderate Resolution Imaging Spectroradiometer were used. The semiarid grassland was a C source and a C sink among contrasting PPT years (114 to -107 g C·m<SUP>-2</SUP>·year<SUP>-1</SUP>). At seasonal scale, PPT relationship with the 15 years of GEE derived from enhanced vegetation index and gross primary productivity was sigmoidal. Moreover, PPT legacies of the previous dry season determined the C balance of the grassland by affecting the C uptake and ecosystem respiration of the following growing season, but productivity was more sensitive to PPT changes than respiration. Models of climate change for semiarid grasslands in North America predict up to 30% reduction of winter-spring PPT and slight summer PPT decrease. Thus, our results suggest that future changes in PPT may have a strong impact on the C uptake capacity of this ecosystem due to weakened legacy effects in summer C uptake.

Research areas: Ecosystems
Landslides associated with recent road constructions in the Río Lucma catchment, eastern Cordillera Blanca, Peru (2022)

Abstract Extensive road construction works recently took place in the remote eastern part of the Peruvian Cordillera Blanca, aiming at a better connection of isolated mountain communities with regional administrative centres. Here we document and characterize landslides associated with these road construction efforts in the Rio Lucma catchment, Peru. We show that a total area of 321,332 m2 has been affected by landslides along the 47.1 km of roads constructed between 2015 and 2018. While landslides downslope the roads (48.2%) and complex landslides crossing the roads (46.4%) were the most frequent landslide types in relation to the position of the road; slide-type movement (60.7%) prevails over the flow-type movement (39.3%). Timewise, we found that 75.0% of landslides were observed simultaneously with road construction work, while the remaining 25.0% occurred up to seven months after the roads had been constructed. We plotted the lagged occurrence of these subsequent landslides against precipitation data, showing that 85.7% of them were observed during the wet season (November to April). We conclude that the majority of mapped landslides were directly associated with road constructions and that the road constructions also may set preconditions for landslides, which mainly occurred during the subsequent wet season.

Research areas: Land Surface/Agriculture Indicators
Phenological Responses to Snow Seasonality in the Qilian Mountains Is a Function of Both Elevation and Vegetation Types (2022)

In high-elevation mountains, seasonal snow cover affects land surface phenology and the functioning of the ecosystem. However, studies regarding the long-term effects of snow cover on phenological changes for high mountains are still limited. Our study is based on MODIS data from 2003 to 2021. First, the NDPI was calculated, time series were reconstructed, and an SG filter was used. Land surface phenology metrics were estimated based on the dynamic thresholding method. Then, snow seasonality metrics were also estimated based on snow seasonality extraction rules. Finally, correlation and significance between snow seasonality and land surface phenology metrics were tested. Changes were analyzed across elevation and vegetation types. Results showed that (1) the asymmetry in the significant correlation between the snow seasonality and land surface phenology metrics suggests that a more snow-prone non-growing season (earlier first snow, later snowmelt, longer snow season and more snow cover days) benefits a more flourishing vegetation growing season in the following year (earlier start and later end of growing season, longer growing season). (2) Vegetation phenology metrics above 3500 m is sensitive to the length of the snow season and the number of snow cover days. The effect of first snow day on vegetation phenology shifts around 3300 m. The later snowmelt favors earlier and longer vegetation growing season regardless of the elevation. (3) The sensitivity of land surface phenology metrics to snow seasonality varied among vegetation types. Grass and shrub are sensitive to last snow day, alpine vegetation to snow season length, desert to number of snow cover days, and forest to first snow day. In this study, we used a more reliable NDPI at high elevations and confirmed the past conclusions about the impact of snow seasonality metrics. We also described in detail the curves of snow seasonal metrics effects with elevation change. This study reveals the relationship between land surface phenology and snow seasonality in the Qilian Mountains and has important implications for quantifying the impact of climate change on ecosystems.

Research areas: Cryospheric Indicators, Land Surface/Agriculture Indicators
Improvement of operational airborne gamma radiation snow water equivalent estimates using SMAP soil moisture (2020)

Knowledge of snow water equivalent (SWE) magnitude and spatial distribution are keys to improving snowmelt flood predictions. Since the 1980s, the operational National Oceanic and Atmospheric Administration's (NOAA) airborne gamma radiation soil moisture (SM) and SWE survey has provided over 20,000 SWE observations to regional National Weather Service (NWS) River Forecast Centers (RFCs). Because the gamma SWE algorithm is based on the difference in natural gamma emission measurements from the soil between bare (fall) and snow-covered (winter) conditions, it requires a baseline fall SM for each flight line. The operational approach assumes the fall SM remains constant throughout that winter's SWE survey. However, early-winter snowmelt and rainfall events after the fall SM surveys have the potential to introduce large biases into airborne gamma SWE estimates. In this study, operational airborne gamma radiation SWE measurements were improved by updating the baseline fall SM with Soil Moisture Active Passive (SMAP) enhanced SM measurements immediately prior to winter onset over the north-central and eastern United States and southern Canada from September 2015 to April 2018. The operational airborne gamma SM had strong agreement with the SMAP SM (Pearson's correlation coefficient, R = 0.69, unbiased root mean square difference, ubRMSD = 0.057 m3/m3), compared to the Advanced Microwave Scanning Radiometer 2 (AMSR2) SM (R = 0.45, ubRMSD = 0.072 m3/m3) and the North American Land Data Assimilation System Phase 2 (NLDAS-2) Mosaic SM products (R = 0.53, ubRMSD = 0.069 m3/m3) in non-forested regions. The SMAP-enhanced gamma SWE was evaluated with satellite-based SWE (R = 0.57, ubRMSD = 34 mm) from the Special Sensor Microwave Imager Sounder (SSMIS) and in-situ SWE (R = 0.710.96) from the Soil Climate Analysis Network and United States Army Corps of Engineer (USACE) St. Paul District, which had better agreement than the operational gamma SWE (R = 0.48, ubRMSD = 36 mm for SSMIS and R = 0.650.75 for in-situ SWE). The results contribute to improving snowmelt flood predictions as well as the accuracy of the NOAA SNOw Data Assimilation System.

Research areas: Cryospheric Indicators, Validation
Investigating decadal changes of multiple hydrological products and land-cover changes in the Mediterranean Region for 2009–2018 (2021)

Land-cover change is a critical concern due to its climatic, ecological, and socioeconomic consequences. In this study, we used multiple variables including precipitation, vegetation index, surface soil moisture, and evapotranspiration obtained from different satellite sources to study their association with land-cover changes in the Mediterranean region. Both observational and modeling data were used for climatology and correlation analysis. Famine Early Warning Systems Network (FEWS NET) Land Data Assimilation System (FLDAS) and Global Land Data Assimilation System (GLDAS) were used to extract surface soil moisture and evapotranspiration data. Intercomparing the results of FLDAS and GLDAS suggested that FLDAS data had better accuracy compared to GLDAS for its better coherence with observational data. Climate Hazards Group Infra-Red Precipitation with Station Data (version 2.0 final) (CHIRPS Pentad) were used to extract precipitation data while Moderate Resolution Imaging Spectroradiometer (MODIS) products were used to extract the vegetation indices used in this study. The land-cover change detection was demonstrated during the 20092018 period using MODIS Land-Cover data. Some of the barren and crop lands in Euphrates-Tigris and Algeria have converted to low-vegetated shrublands over the time, while shrublands and barren areas in Egypts southwestern Delta region became grasslands. These observations were well explained by changing trends of hydrological variables which showed that precipitation and soil moisture had higher values in the countries located to the east of the Mediterranean region compared to the ones on the west. For evapotranspiration, the countries in the north had lower values except for countries in Europe such as Bosnia, Romania, Slovenia, and countries in Africa such as Egypt and Libya. The enhanced vegetation index appeared to be decreasing from north to south, with countries in the north such as Germany, Romania, and Czechia having higher values, while countries in the south such as Libya, Egypt, and Iraq having lower trends. Time series analysis for selected countries was also done to understand the change in hydrological parameters, including Enhanced Vegetation Index, evapotranspiration, and soil moisture, which showed alternating drop and rise as well as stagnant values for different parameters in each country.

Research areas: Land Surface/Agriculture Indicators
Projecting Future Vegetation Change for Northeast China Using CMIP6 Model (2021)

Northeast China lies in the transition zone from the humid monsoonal to the arid continental climate, with diverse ecosystems and agricultural land highly susceptible to climate change. This region has experienced significant greening in the past three decades, but future trends remain uncertain. In this study, we provide a quantitative assessment of how vegetation, indicated by the leaf area index (LAI), will change in this region in response to future climate change. Based on the output of eleven CMIP6 global climates, Northeast China is likely to get warmer and wetter in the future, corresponding to an increase in regional LAI. Under the medium emissions scenario (SSP245), the average LAI is expected to increase by 0.27 for the mid-century (20412070) and 0.39 for the late century (20712100). Under the high emissions scenario (SSP585), the increase is 0.40 for the mid-century and 0.70 for the late century, respectively. Despite the increase in the regional mean, the LAI trend shows significant spatial heterogeneity, with likely decreases for the arid northwest and some sandy fields in this region. Therefore, climate change could pose additional challenges for long-term ecological and economic sustainability. Our findings could provide useful information to local decision makers for developing effective sustainable land management strategies in Northeast China.

Research areas: Land Surface/Agriculture Indicators
Spatial Distribution of Soil Moisture in Mongolia Using SMAP and MODIS (2021)

Soil moisture is one of the essential variables of the water cycle, and plays a vital role in agriculture, water management, and land (drought) and vegetation cover change as well as climate change studies. The spatial distribution of soil moisture with high-resolution images in Mongolia has long been one of the essential issues in the remote sensing and agricultural community. In this research, we focused on the distribution of soil moisture and compared the monthly precipitation/temperature and crop yield from 2010 to 2020. In the present study, Soil Moisture Active Passive (SMAP) and Moderate Resolution Imaging Spectroradiometer (MODIS) data were used, including the MOD13A2 Normalized Difference Vegetation Index (NDVI), MOD11A2 Land Surface Temperature (LST), and precipitation/temperature monthly data from the Climate Research Unit (CRU) from 2010 to 2020 over Mongolia. Multiple linear regression methods have previously been used for soil moisture estimation, and in this study, the Autoregressive Integrated Moving Arima (ARIMA) model was used for soil moisture forecasting. The results show that the correlation was statistically significant between SM-MOD and soil moisture content (SMC) from the meteorological stations at different depths (p < 0.0001 at 020 cm and p < 0.005 at 050 cm). The correlation between SM-MOD and temperature, as represented by the correlation coefficient (r), was 0.80 and considered statistically significant (p < 0.0001). However, when SM-MOD was compared with the crop yield for each year (20102019), the correlation coefficient (r) was 0.84. The ARIMA (12, 1, 12) model was selected for the soil moisture time series analysis when predicting soil moisture from 2020 to 2025. The forecasting results are shown for the 95 percent confidence interval. The soil moisture estimation approach and model in our study can serve as a valuable tool for confident and convenient observations of agricultural drought for decision-makers and farmers in Mongolia.

Research areas: Land Surface/Agriculture Indicators
Potential Variation of Evapotranspiration Induced by Typical Vegetation Changes in Northwest China (2022)

Evapotranspiration (ET), as a key eco-hydrological parameter, plays an important role in understanding sustainable ecosystem development. Each plant category has a unique functional trait on transpiration and photosynthesis, with ET implying that water cycle and energy transformation is linked with vegetation type. Changes in surface vegetation directly alter biophysical land surface properties, hence affecting energy and ET transfer. With the rapid increase in land surface changes, there is a need to further understand and quantify the effects of vegetation change on ET, especially over the vulnerable water-cycle region in the arid and semi-arid regions of Northwest China. We adopted the GlobalLand30 land cover and MOD16A2 in 2010 and 2020 to investigate, discuss the spatio-temporal characteristics of annual and seasonal ET of cultivated land, grassland, and forests in Northwest China, and quantify the impact on vegetation changes with absolute and relative changes from different climatic subecoregions on ET. Our results show the following: (1) Forest ET was generally the highest at 688 mm, followed by cultivated land and grassland with 200400 mm in arid climatic subecoregions. (2) Returning cultivated land to forests and cultivated land expansion potentially enhances ET by 90110 mm/10a, with the relative rate of change increasing by 22.1% and 45.8%, respectively, away from unchanged vegetation within identical subecoregions. (3) The ET of most investigated areas gains the highest value in summer, followed by spring, autumn, and winter. This study provides reference for sustainable ecosystem development and the reasonable utilization of limited water resources in Northwest China.

Research areas: Land Surface/Agriculture Indicators