Abstract
The U.S. Department of Agriculture (USDA) International Production Assessment Division (IPAD) is responsible for providing monthly global crop estimates that heavily influence global commodity market access. These estimates are derived from a merging of many data sources including satellite and ground observations, and more than 20 years of climatology and crop behavior data over key agricultural areas. The goal of IPAD is to provide timely and accurate estimates of global crop conditions for use in up-to-date commodity intelligence reports. A crucial requirement of these global crop yield forecasts is the regional characterization of surface and sub-surface soil moisture. However, due to the spatial heterogeneity and dynamic nature of precipitation events and soil wetness, accurate estimation of regional land surface-atmosphere interactions based sparse ground measurements is difficult. Temporal resolution is particularly important for predicting adequate surface wetting and drying between precipitation events and is closely integrated with CADRE. We attempt to improve upon the existing system by applying an Ensemble Kalman filter (EnKF) data assimilation system to integrate surface soil moisture retrievals from the NASA Advanced Microwave Scanning Radiometer (AMSR-E) into the USDA soil moisture model. The improved temporal resolution and spatial coverage of the satellite-based EOS Advanced Microwave Scanning Radiometer (AMSR-E) is envisaged to provide a better characterization of root zone soil moisture at the regional scale and enable more accurate crop monitoring in key agricultural areas This work aims at evaluating the utility of merging satellite-retrieved soil moisture estimates with the IPAD two-layer soil moisture model used within the DBMS. We present a quantitative analysis of the assimilated soil moisture product over West Africa (9?N-20?N; 20?W-20?E). This region contains many key agricultural areas and has a high agro-meteorological gradient from desert and semi-arid vegetation in the North, to grassland, trees and crops in the South, thus providing an ideal location for evaluating the assimilated soil moisture product over multiple land cover types and conditions. A data denial experimental approach is utilized to isolate the added utility of integrating remotely-sensed soil moisture by comparing assimilated soil moisture results obtained using (relatively) low-quality precipitation products obtained from real-time satellite imagery to baseline model runs forced with higher quality rainfall. An analysis of root-zone anomalies for each model simulation suggests that the assimilation of AMSR-E surface soil moisture retrievals can add significant value to USDA root-zone predictions derived from real-time satellite precipitation products.
Published Version
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call