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Crop yield estimation is of great importance to food security. NDVI, as an effective crop monitoring tool, is extensively used in crop yield estimation. However there are few studies conducted in the regions where mixed crops are grown. In this study, a statistical approach for crop area identification is proposed and applied to wheat in Jianshui County in the Nanpan River Basin, Yunnan Province of China. Based on the correlation analysis between MODIS NDVI data and crop yield, the planting areas are identified, as well as the best periods for a reliable estimation. Regression models are presented to predict the crop yield with the retrieved NDVI from the corresponding crop planting-areas. Besides, the crop yield is also strongly influenced by meteorological factors, such as precipitation, temperature and potential evapotranspiration data. Therefore, new regression model by adding those factors is presented and compared with the former one. This study has proposed a simple and convenient method on crop yield estimation using meteorological factors and NDVI data in small regions where crop type is unknown exactly.


Session R47, Remote Sensing and LiDAR Data: Water Quality, Vegetation, and Bathymetry



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