Thomas J. Jackson

Assessment of the SMAP Passive Soil Moisture Product

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

WindSat Global Soil Moisture Retrieval and Validation

A physically based six-channel land algorithm is developed to simultaneously retrieve global soil moisture (SM), vegetation water content (VWC), and land surface temperature. The algorithm is based on maximum-likelihood estimation and uses dual-polarization WindSat passive microwave data at 10, 18.7, and 37 GHz. The global retrievals are validated at multispatial and multitemporal scales against SM climatologies, in situ network data, precipitation patterns, and Advanced Very High Resolution Radiometer (AVHRR) vegetation data. In situ SM observations from the U.S., France, and Mongolia for diverse land/vegetation cover were used to validate the results. The performance of the estimated volumetric SM was within the requirements for most science and operational applications (standard error of 0.04 m3/m3, bias of 0.004 m3/m3, and correlation coefficient of 0.89). The retrieved SM and VWC distributions are very consistent with global climatology and mesoscale precipitation patterns. The comparisons between the WindSat vegetation retrievals and the AVHRR Green Vegetation Fraction data also reveal the consistency of these two independent data sets in terms of spatial and temporal variations.

Assimilation of a Satellite-Based SoilMoisture Product into a Two-Layer Water Balance Model for a Global Crop Production Decision Support System

Timely and accurate monitoring of global weather anomalies and drought conditions is essential for assessing global crop conditions. Soil moisture observations are particularly important for crop yield fluctuation forecasts provided by the US Department of Agriculture’s (USDA) International Production Assessment Division (IPAD) of the Office of Global Analysis (OGA) within the Foreign Agricultural Service (FAS). The current system utilized by IPAD estimates soil moisture from a 2-layer water balance model based on precipitation and temperature data from World Meteorological Organization (WMO) and US Air Force Weather Agency (AFWA). The accuracy of this system is highly dependent on the data sources used; particularly the accuracy, consistency, and spatial and temporal coverage of the land and climatic data input into the models. However, many regions of the globe lack observations at the temporal and spatial resolutions required by IPAD. This study incorporates NASA’s soil moisture remote sensing product provided by the EOS Advanced Microwave Scanning Radiometer (AMSR-E) to the U.S. Department of Agriculture Crop Assessment and Data Retrieval (CADRE) decision support system. A quasi-global-scale operational data assimilation system has been designed and implemented to provide CADRE with a daily soil moisture analysis product obtained via the assimilation of AMSR-E surface soil moisture retrievals into the IPAD two-layer soil moisture model. This chapter presents a methodology of data assimilation system design and a brief evaluation of the system performance over the Conterminous United States (CONUS).