Agricultural models, such as the Decision Support System for Agrotechnology Transfer – Cropping Systems Model (DSSAT-CSM), have been developed for predicting crop yield at field and regional scales and to provide useful information for water resources management. A potentially valuable input to agricultural models is soil moisture. Presently, no observations of soil moisture exist covering the entire U.S. at adequate time (daily) and space (~10 km or less) resolutions desired for crop yield assessments. Data products from NASA’s upcoming Soil Moisture Active Passive (SMAP) mission will fill the gap. The objective of this study is to demonstrate the usefulness of the SMAP soil moisture data in modeling and forecasting crop yields and irrigation amount. A simple, efficient data assimilation algorithm is presented in which the agricultural crop model DSSAT-CSM is constrained to produce modeled crop yield and irrigation amounts that are consistent with SMAP-type data. Numerical experiments demonstrate that incorporating the SMAP data into the agricultural model provides an added benefit of reducing the uncertainty of modeled crop yields when the weather input data to the crop model are subject to large uncertainty.
El Sharif, H., J. Wang, and A.P. Georgakakos (2015): Modeling regional crop yield and irrigation demand using SMAP type of soil moisture data. Journal of Hydrometeorology 16 (2): 904-916. DOI: 10.1175/JHM-D-14-0034.1
This Paper/Book acknowledges NSF CZO grant support.