Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Proceedings
Publication Acceptance Date: 6/15/2004
Publication Date: 9/20/2004
Citation: Liang, S., Fang, H., Hoogenboom, G., Teasdale, J.R., Cavigelli, M.A. 2004. Estimation of crop yield at the regional scale from modis observations. Proceedings of International Geoscience and Remote Sensing Symposium. 3:1625-1628. Interpretive Summary: Crop yields are influenced by climate, genetics, crop management, and the physical and chemical properties of soils, and may vary considerably both spatially and temporally. Accurate, objective, reliable and timely predictions of crop yields over large areas are critical for national food security through policy making on import/export plans and prices. Remote sensing data have been widely used for crop yield estimation. This method is eventually a statistical model and cannot predict the time-dependent processes of growth and field formation that are critical for real-time yield forecasting. The most promising method is the combination of remote sensing and crop growth modeling. In this study, we have developed a procedure using the data assimilation method to estimate crop yield at the regional scale from remote sensing MODIS (Medium resolution imaging spectroradiometer) data. The crop models that encompass the Decision Support System for Agrotechnology Transfer (DSSAT) and a copy radiative transfer model are coupled, and the MODIS EVI data at 250m resolution and LAI product at 1 km resolution were assimilated into the coupled model to adjust the key parameters in the crop growth model. The results compared favorably with the USDA/NASS agricultural statistics data at the county level and are very encouraging. This approach will be useful to scientists and policy makers interested in estimating crop yields at a regional scale.
Technical Abstract: This study presents some preliminary results on estimating crop yield at the regional scale from MODIS (Medium resolution imaging spectroradiometer) data using the data assimilation method. MODIS data products include leaf area index (LAI) and enhanced vegetation index (EVI). The crop growth models of DSSAT were used in this study, which are driven by weather, soil and crop management data. Some of the variables of the models were adjusted through data assimilation algorithms for accurate prediction of crop yields.