Agricultural drought is one of the main disasters that cause large losses to society-economy development. Agriculture droughtmonitoring and assessment are challenges due to its complex causes in the world. Agricultural drought has characteristics of temporal-spatial inhomogeneity, wide affected areas, periodic happening. Satellite is one of the important technologies to support agriculture drought monitoring for large coverage, frequent observation, repeatable observation, reliable information source and low cost. However, single satellite can't meet the needs of agriculture drought monitoring because of the complexity of agricultural drought. It is the best choice to apply multiple satellites, such as MODIS, TM, ETM+, and HJ, to monitor agriculture drought which can make the results be more accurate and timely In this paper, taking MODIS, TM, ETM+ and HJ as data source, the application ability of agricultural drought monitoring models, including VSWI, TVDI and VCI, has been analyzed. Then, the agricultural drought integrated monitor method using aforementioned three models has been developed based on the comprehensive application of multiple satellites. Lastly, taking Southwest drought occurred from 2009 to 2010 as the study case, the Crops Affected Areas (CAA) and the Crops Failure Areas (CFA) have been assessed using the proposed methods. Results show that five regions in southwest of China, including Sichuan province, Chongqing, Guizhou province, Yunnan province, and Guangxi province, had suffered different degrees agricultural drought. The hardest-hit areas of southwest drought were Guizhou province and Yunnan province. Moreover, results also show that the comprehensive application of MODIS, TM, ETM+ and HJ is a practical method to monitoring agriculture drought.

In the paper, the parameters of hyperspectral data of the environment disaster reduction satellites have been introduced, firstly. Then the pro-processing methods for hyperspectral data have been elaborated according to the characteristics of the hyperspectral sensor of the environment disaster reduction satellites. After analysis the problems existing in the pre-processing of hyperspectral data, the hyperspectral data have been employed to classify the land features. The experimental evaluation shows that the performance of classifying the hyperspectral data of environment disaster reduction satellites is excellent.

Open access peer-reviewed chapter