Sun Hao, Chen Yunhao, Sun Hongquan. Comparisons and classification system of typical remote sensing indexes for agricultural drought[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(14): 147-154.
    Citation: Sun Hao, Chen Yunhao, Sun Hongquan. Comparisons and classification system of typical remote sensing indexes for agricultural drought[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2012, 28(14): 147-154.

    Comparisons and classification system of typical remote sensing indexes for agricultural drought

    • Various remote sensing indexes of agriculture drought were proposed at present, but it is hard to decide which one is the most appropriate. In this paper, an intercomparison among 13 remote sensed indexes for monitoring agricultural drought was performed using MODIS products. Then a classification system was constructed for these indexes, where the applicability of each class was demonstrated. The results indicated that these indexes were not exactly the same because different indexes used different features of land surface to represent agricultural drought. Consequently, these indexes were classified into four categories: reflecting soil moisture change, reflecting canopy temperature change, reflecting vegetation water content change, and reflecting crop form and greenness change. The first category was suitable for agricultural drought early warning and soil type agricultural drought monitoring, where the Modified Perpendicular Drought Index could effectively represent changes in the soil surface moisture, and was suitable for monitoring changes temporally. The second category was not only suitable for drought early warning, but also more suitable for drought monitoring, where the Temperature Vegetation Drought Index based on the LAI-LST feature space was recommended. The third and fourth categories were more suitable for early warning and assessment of agricultural drought disaster. The present paper provides a reference for the selection of remote sensing indexes for monitoring agricultural drought.
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