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Remote Sensing

Corn and Soybean Mapping in the United States Using MODIS Time-Series Data Sets

^a Geographic Information Science Center of Excellence, South Dakota State Univ., Brookings, SD 57007
^b Dep. of Geography, Univ. of Maryland, College Park, MD 20742

^* Corresponding author (jiyul.chang{at}sdstate.edu)

Monitoring and mapping of U.S. croplands has long been a primary goal of many users of earth observation satellite data. The advantages of using low spatial and high temporal resolution data are (i) increased ability to monitor the phenological change of crop plants, and (ii) the possibility of generating consistent large area crop cover maps. This study investigates the potential of 500-m MODIS (MODerate Resolution Imaging Spectroradiometer) data in estimating corn (Zea mays L.) and soybean [Glycine max (L.) Merr.] area for the dominant production areas of the USA. To avoid cloud cover, MODIS 32-day composites for all land bands, normalized difference vegetation index (NDVI), and land surface temperature (LST), were used covering March 2002 to February 2003. These time-sequential images were further composited to produce 279 annual time-integrated metrics. Using USDA-NASS Cropland Data Layers (CDL) as subpixel training data, percentage soybean and corn cover per 500-m pixel was calculated and accuracy was assessed at national, state, and county scales using data from the 2002 NASS Census of Agriculture. When these estimates were compared with the NASS Census, r² values for corn, soybean, and combined corn and soybean areas were 0.957, 0.949, and 0.984 at the state level, respectively. At the national scale, MODIS estimates of corn and soybean cover differed by 6 and 4%, respectively. Results indicate a robust potential for using MODIS in crop type monitoring applications.

Abbreviations: CDL, cropland data layer • LST, land surface temperature • MODIS, Moderate Resolution Imaging Spectroradiometer • NASS, National Agricultural Statistics Service • NDVI, normalized difference vegetation index • RMSE, root mean square error