Abstract
Cotton is a significant cash crop of China. Timely and accurate cotton area and yield estimation is useful for management decisions related to the cotton procurement and sales. This study is a first research on cotton area and yield estimation based on remote sensing at Zhanhua County which is one of the high-quality cotton production demonstration bases of China. After normalization of Enhanced Vegetation Index (EVI) time series derived from Huanjin 1 A/B satellite (HJ-1 A/B), decision tree classifier was used to identify the cotton, and then K-Means classifier was applied to estimate cotton yield. The results indicated an overall accuracy of 95% for the cotton area estimation and 91% for the cotton yield classification. With further validation, it suggests that this method can be used to timely achieve the cotton area and growth information of this region.
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