A5101903936- Soil Moisture and Remote Sensing
- Hydrology and Watershed Management Studies
- Precipitation Measurement and Analysis
- Cryospheric studies and observations
- Hydrocarbon exploration and reservoir analysis
- Climate variability and models
- Soil and Unsaturated Flow
- Remote Sensing and Land Use
- Meteorological Phenomena and Simulations
- Climate change and permafrost
- Geological and Geophysical Studies
- Plant Water Relations and Carbon Dynamics
- Geophysics and Gravity Measurements
- Geological Studies and Exploration
- Environmental and Agricultural Sciences
- Methane Hydrates and Related Phenomena
- Environmental Changes in China
- Geological Modeling and Analysis
- Tree-ring climate responses
- Forest, Soil, and Plant Ecology in China
- Atmospheric aerosols and clouds
- Flood Risk Assessment and Management
- Remote-Sensing Image Classification
- Effects of Vibration on Health
- Museums and Cultural Heritage
Rajamangala University of Technology
2024
Goddard Space Flight Center
2015-2024
Science Systems and Applications (United States)
2015-2024
GFZ Helmholtz Centre for Geosciences
2021
Hefei University of Technology
2006-2020
Guangdong Academy of Agricultural Sciences
2020
Chengdu Institute of Biology
2020
Chinese Academy of Sciences
2020
Science Applications International Corporation (United States)
2010-2019
Tianshui Normal University
2012-2017
Abstract The Modern-Era Retrospective Analysis for Research and Applications (MERRA) is a state-of-the-art reanalysis that provides, in addition to atmospheric fields, global estimates of soil moisture, latent heat flux, snow, runoff 1979–present. This study introduces supplemental improved set land surface hydrological fields (“MERRA-Land”) generated by rerunning revised version the component MERRA system. Specifically, MERRA-Land benefit from corrections precipitation forcing with Global...
Abstract The Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), features several major advances from the original MERRA reanalysis, including use, outside of high latitudes, observations-based precipitation data products to correct falling on land surface in MERRA-2 system. method merging observed into has been refined that (land-only) MERRA-Land reanalysis. This paper describes evaluates precipitation. Compared monthly GPCPv2.2 observations, corrected...
The MERRA-2 atmospheric reanalysis product provides global, 1-hourly estimates of land surface conditions for 1980–present at ~50-km resolution. uses observations-based precipitation to force the (unlike its predecessor, MERRA). This paper evaluates and MERRA hydrology estimates, along with those land-only MERRA-Land ERA-Interim/Land products, which also use precipitation. Overall, are better than MERRA. A comparison against GRACE satellite observations terrestrial water storage demonstrates...
Abstract The Soil Moisture Active Passive (SMAP) mission Level-4 Surface and Root-Zone (L4_SM) data product is generated by assimilating SMAP L-band brightness temperature observations into the NASA Catchment land surface model. L4_SM available from 31 March 2015 to present (within 3 days real time) provides 3-hourly, global, 9-km resolution estimates of (0–5 cm) root-zone (0–100 soil moisture conditions. This study presents an overview algorithm, validation approach, assessment versus in...
Near‐surface soil moisture observations from the active microwave ASCAT and passive AMSR‐E satellite instruments are assimilated, both separately together, into NASA Catchment land surface model over 3.5 years using an ensemble Kalman filter. The impact of each assimilation is evaluated in situ 85 sites US Australia, terms anomaly time series correlation‐coefficient, R. skill gained by assimilating either or was very similar, even when separated cover type. Over all sites, mean root‐zone R...
Abstract The NASA Soil Moisture Active Passive (SMAP) mission Level‐4 (L.4_SM) product provides global, 3‐hourly, 9‐km resolution estimates of surface (0–5 cm) and root zone (0–100 soil moisture with a mean latency ~2.5 days. underlying L4_SM algorithm assimilates SMAP radiometer brightness temperature (Tb) observations into the Catchment land model using spatially distributed ensemble Kalman filter. In Version 4 modeling system upward recharge from below under nonequilibrium conditions was...
The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, defined a set criteria for core validation sites (CVS) that enable testing key SM accuracy requirement (unbiased root-mean-square error <0.04 m<sup>3</sup>/m<sup>3</sup>). approach also includes other (“sparse network”) <i>in situ</i> measurements,...
Abstract The contributions of precipitation and soil moisture observations to skill in a land data assimilation system are assessed. Relative baseline estimates from the Modern Era Retrospective-analysis for Research Applications (MERRA), study investigates derived (i) model forcing corrections based on large-scale, gauge- satellite-based (ii) surface retrievals Advanced Microwave Scanning Radiometer Earth Observing System (AMSR-E). Soil (defined as anomaly time series correlation...
Abstract SMAP (Soil Moisture Active and Passive) radiometer observations at ∼40 km resolution are routinely assimilated into the NASA Catchment Land Surface Model to generate 9 Level‐4 Soil product. This study demonstrates that adding high‐resolution radar from Sentinel‐1 assimilation can increase spatiotemporal accuracy of soil moisture estimates. Radar were either separately or simultaneously with observations. Assimilation impact was assessed by comparing 3‐hourly, surface root‐zone...
Land surface (or "skin") temperature (LST) lies at the heart of energy balance and is a key variable in weather climate models. In this research LST retrievals from International Satellite Cloud Climatology Project (ISCCP) are assimilated into Noah land model Catchment (CLSM) using an ensemble-based, offline data assimilation system. described very differently two A priori scaling dynamic bias estimation approaches applied because satellite LSTs typically exhibit different mean values...
Abstract Recent advances in remote sensing and land data assimilation purport to improve the quality of antecedent soil moisture information available for operational hydrologic forecasting. We objectively validate this claim by calculating strength relationship between storm‐scale runoff ratio (i.e., total streamflow divided rainfall accumulation depth units) prestorm surface estimates from a range products. Results demonstrate that both satellite‐based, L band microwave radiometry...
Accurate partitioning of precipitation into infiltration and runoff is a fundamental objective land surface models tasked with characterizing the water energy balance. Temporal variability in this due, part, to changes pre-storm soil moisture, which determine capacity unsaturated storage. Utilizing NASA Soil Moisture Active Passive Level-4 moisture product combination streamflow observations, we demonstrate that (LSMs) generally underestimate strength positive rank correlation between event...
The assimilation of remotely sensed soil moisture information into a land surface model has been shown in past studies to contribute accuracy the simulated hydrological variables. Remotely data, however, can also be used improve itself through calibration model's parameters, and this increase products. Here, data provided by Soil Moisture Active/Passive (SMAP) satellite mission are applied component NASA GEOS Earth system using both order quantify relative degrees which each strategy...
The Soil Moisture and Ocean Salinity (SMOS) Active Passive (SMAP) missions provide Level-1 brightness temperature (Tb) observations that are used for global soil moisture estimation. However, the nature of these Tb data differs: SMOS contain atmospheric select reflected extraterrestrial ("Sky") radiation, whereas SMAP corrected contributions, using auxiliary near-surface information. Furthermore, multiangular, is measured at 40° incidence angle only. This letter discusses how Tb, radiative...
Abstract The Soil Moisture Active Passive (SMAP) level 4 product provides enhanced soil moisture estimates by assimilating SMAP brightness temperature observations into a land surface model. Here, quantitative estimate of the relative skill Level‐4 and model‐only (vs. true moisture) is derived using only one additional noisy (but independent) product. method applied globally verified high‐quality, ground‐based measurements where available. Results demonstrate that has relatively little...
Abstract Three independent, quasi‐global, gridded data sets of precipitation (a rain gauge‐based set, the satellite‐only component NASA Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement mission (IMERG) Final Run product, and estimates derived from Soil Moisture Active Passive (SMAP) soil moisture retrievals), are objectively combined into a single pentad set at 36‐km resolution using unique approach based on extended triple collocation. The quality each four is then...
This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network (NN) and physically-based SMAP retrievals were assimilated into NASA Catchment model over contiguous United States for April 2015 March 2017. By construction, NN are consistent with global climatology moisture. Assimilating without further bias correction improved surface root zone correlations against in situ measurements...
The SMAP Level 4 soil moisture (L4_SM) product provides global estimates of surface and root zone moisture, along with other land variables their error estimates. These are obtained through assimilation brightness temperature observations into the Goddard Earth Observing System (GEOS-5) model. L4_SM is provided at 9 km spatial 3-hourly temporal resolution about 2.5 day latency. in validated against situ observations. meets required target uncertainty 0.04 m <sup...
Because runoff production is more efficient over wetter soils, and because soil moisture has an intrinsic memory, information can potentially contribute to the accuracy of streamflow predictions at seasonal leads. In this work, we use surface (0-5 cm) retrievals obtained with National Aeronautics Space Administration's Soil Moisture Active Passive satellite instrument in conjunction measurements taken within 236 intermediate-scale (2000-10,000 km2) unregulated river basins conterminous...
We propose a novel approach, called the “localized ratio fitting” (LRF), to estimating true temperature sensitivity from soil respiration measurements, task crucial modeling terrestrial carbon cycle and climate but so far hindered by inadequate conventional regression approach. LRF takes advantage of different timescales pool dynamics–induced environmental variation–induced changes in CO 2 efflux. It first transforms expression for into form suppressing influence dynamics then uses...