Potential Utility of Thematic Happer Data in Estimating Crop Areas

This paper predicts potential improvements offered by thematic mapper data over multispectral scanner data when utilized in regression estimation of crop .areas. A study comparing LANDSAT data and simulated thematic mapper data is described. Quantitative measures of potential improvements in crop-area estimates of corn, soybeans, and dense woodlands are calculated, and the sensor characteristics causing these improvements are determined. 1. BACKGROUND Since the launch of LANDSAT I in 1972, the Economics and Statistics Service of the U.S. Department of Agriculture has investigated the utility of multispectral sensor (MSS) data in estimating crop areas. ESS's approach to utilizing MSS data has been to use it to supplement enumerator-collected ground observations available from ESS's operational surveys. These ground surveys consist of interviews with farm operators residing in randomly selected areas of land, referred to by ESS as segments. The findings of ES5's HSS studies have been that such supplementary use of remote sensing data does yield statistical improvements in crop-area estimates but possess numerous difficulties for successful operational implementation. Hore specifically, ESS has found that MSS-based crop-area estimates are measurably more precise (i.e., have smaller variance) than estimates based only on ground data. However, operational implementation is hampered by MS5 data problems of uneven quality, untimely delivery, and lost acquisitions because of clouds. This paper describes an ESS investigation to predict to what degree, if any, that the supplementary use of thematic mapper (TM) data in crop-area estimation will provide additional statistical improvements compared to the supplementary use of MSS data. The research methodology employed was to analyze simulated TM (S-TM) data acquired by the a~rcraft-borne NSOOl sensor. 2. CROP-AREA ESTIMATES FROM REMOTE-SENSING DATA .Presented at the Fifteenth International Symposium on Remote Sensing of .Environment, Ann Arbor, MI, May 1981.