Soil moisture prediction based on retrievals from satellite sensing and a regional climate model

Huailiang Chen; Xiangde Xu; Yujie Liu; Yusheng Li; Shitao Wang

doi:10.1117/12.616557

1 September 2005 Soil moisture prediction based on retrievals from satellite sensing and a regional climate model

Huailiang Chen, Xiangde Xu, Yujie Liu, Yusheng Li, Shitao Wang

Proceedings Volume 5884, Remote Sensing and Modeling of Ecosystems for Sustainability II; 58840E (2005) https://doi.org/10.1117/12.616557
Event: Optics and Photonics 2005, 2005, San Diego, California, United States

Abstract

Soil moisture is an inegligible physical variable in agrometeorology, climatology, hydrology, ecology and crop cultivation and predicted normally by use of the Penman formula for meteorological records from a single or a few stations and weather forecasts. This method, however, allows to make the prediction only for a limited number of stations rather than regional gridded predictions. For this reason, we developed a scheme of satellite sensings retrieval, the regional climate model (RegCM2) and a soil water predicting model in combination for moisture in fields of staple crops over the Huang-Huai Plains, by which to establish a drought warning system, of which 1) the soil water predicting model makes use of the soil moisture balance equation applicable to fields of winter wheat and summer corn in the Plains, whose central component is the Penman formula revised by FAO; 2) the needed NWP products are offered by NCAR RegCM2 and 3) the initial field of soil moisture comes from the retrieval of polar-orbiting meteorological satellite data that are corrected through vegetation cover correction and a variational technique. Results show that the proposed scheme is able to improve the precision of the prediction and to better monitor and predict changes in the moisture and the distribution of drought-hit crop areas over the study plains.

Citation Download Citation

Huailiang Chen, Xiangde Xu, Yujie Liu, Yusheng Li, and Shitao Wang "Soil moisture prediction based on retrievals from satellite sensing and a regional climate model", Proc. SPIE 5884, Remote Sensing and Modeling of Ecosystems for Sustainability II, 58840E (1 September 2005); https://doi.org/10.1117/12.616557

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KEYWORDS

Soil science

Data modeling

Atmospheric modeling

Environmental sensing

Vegetation

Satellites

Climatology

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