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item Doraiswamy, Paul
item Hatfield, Jerry
item Prueger, John
item Stern, Alan

Submitted to: International Geoscience and Remote Sensing Symposium Proceedings
Publication Type: Abstract Only
Publication Acceptance Date: 3/6/2003
Publication Date: 7/21/2003
Citation: Doraiswamy, P.C., Hatfield, J.L, Prueger, J.H., Stern, A.J., Akhmedov, B. 2003. MODIS application for mapping regional crop yields [abstract]. In: Proceeding of IEEE International Geoscience and Remote Sensing Symposium, July 21-25, 2003, Toulouse, France. 2003 CDROM.

Interpretive Summary:

Technical Abstract: Landcover and landuse classification are important information for studying the seasonal vegetation dynamics of agricultural crops. The processing and classification of temporal imagery is the first step to develop accurate and consistent vegetation products. NASA-MODIS products of Landcover at Global scales can be very useful in assessment of regional information for ecosystems programs. Products of Landcover and leaf area index (LAI) have very specific applications in monitoring agricultural crop growth and production. Landsat is the first choice for monitoring and retrieval of crop information, however, limited temporal and spatial data coverage during critical periods by Landsat is unsuitable for regional operational programs in agriculture. MODIS imagery offers an opportunity for daily coverage required in operational applications. The objective of this research is to investigate the applicability of the MODIS products in the operational programs at the U.S. Department of Agriculture. A field study was conducted in the predominantly corn and soybean area of Iowa in the U.S. Midwest. This study site was a 100 x 50 km area where a NASA-funded soil moisture study was also conducted. Ground measurements of crop growth, development and canopy reflectance were monitored. Landsat data was used to develop the landuse classification. Canopy parameters were derived from the NASA-MODIS 8-day composite and daily surface reflectance products at 250m resolutions. Landsat classification was applied to the MODIS imagery and LAI derived from surface reflectance data using a radiative transfer model. The model parameters for corn and soybean crop were measured and compared with LAI simulation results. LAI derived from MODIS imagery during the crop season was integrated in a crop yield simulation model. Daily soil moisture simulations were assessed and used in crop yield assessment. LAI, soil moisture and crop yields were simulated at 250 m resolution and results were mapped for the study area.