Downscaled Soil Moisture Active Passive (SMAP) Product @ 1-km Resolution using Machine Learning

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Research Data Assimilation and Remote Sensing Drought Modeling Probabilistic Drought Monitoring and Forecasting Interactive Drought Type Maps Interactive Probability Maps Static Maps Drought Recovery and Water Use Efficiency Drought Vulnerability, Risk and Mortality Analysis Extreme Events and Flood Modeling Climate Variability and Change Hydrologic Modeling Bayesian Multimodeling Meteorological and Hydrological Postprocessing

Data Assimilation and Remote Sensing

Drought Modeling Probabilistic Drought Monitoring and Forecasting Interactive Drought Type Maps Interactive Probability Maps Static Maps Drought Recovery and Water Use Efficiency Drought Vulnerability, Risk and Mortality Analysis

Probabilistic Drought Monitoring and Forecasting Interactive Drought Type Maps Interactive Probability Maps Static Maps

Interactive Drought Type Maps

Interactive Probability Maps

Static Maps

Drought Recovery and Water Use Efficiency

Drought Vulnerability, Risk and Mortality Analysis

Extreme Events and Flood Modeling

Climate Variability and Change

Hydrologic Modeling Bayesian Multimodeling Meteorological and Hydrological Postprocessing

Bayesian Multimodeling

Meteorological and Hydrological Postprocessing

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*Data SMAP SMAP Visualization

SMAP

SMAP Visualization

Remotely sensed soil moisture satellite data provide an unprecedented information about the soil moisture across spatial and temporal scales, which is logistically unachievable from in situ observation networks. SMAP (Soil Moisture Active Passive) is one of those satellites that provides soil moisture data at top 5 cm soil layer using brightness temperature on the 36‐km grid cell from both ascending (6:00 pm) and descending (6:00 am) passes. Although the SMAP soil moisture observation has a decent spatial resolution for global and continental scale applications, it cannot be used directly for regional or local studies, such as agriculture and drought monitoring, irrigation management and planning, flood forecasting, crop production and water resources management.

Here, we developed a framework based on machine learning and a suit of remotely sensed data (i.e., MODIS NDVI and land surface temperature and precipitation) and in situ data sets, including soil texture and topography data, to downscale the level 3 daily composite version (L3_SM_P) of SMAP radiometer soil moisture over the Continental United States (CONUS) to 1‐km spatial resolution, pivotal for a variety of applications. The downscaled soil moisture product (SMAP_L3_SM_P_D) has been validated against two core validation sites and over 300 sparse in situ soil moisture sensors evenly distributed over the CONUS. For more information please see Abbaszadeh et al., 2019, WRR