Dr. Yuanyuan Wang Profile

Dr. Yuanyuan Wang

at National Satellite Meteorological Ctr

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Proceedings Article | 2 November 2017 Presentation + Paper

A new comprehensive index for drought monitoring with TM data

Yuanyuan Wang

Proceedings Volume 10421, 104210N (2017) https://doi.org/10.1117/12.2278166

KEYWORDS: Agriculture, Vegetation, Meteorology, Satellites, Soil science, Spatial resolution, Meteorological satellites, Temperature metrology, MODIS, Near infrared

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Drought is one of the most important and frequent natural hazards to agriculture production in North China Plain. To improve agriculture water management, accurate drought monitoring information is needed. This study proposed a method for comprehensive drought monitoring by combining a meteorological index and three satellite drought indices of TM data together. SPI (Standard Precipitation Index), the meteorological drought index, is used to measure precipitation deficiency. Three satellite drought indices (Temperature Vegetation Drought Index, Land Surface Water Index, Modified Perpendicular Drought Index) are used to evaluate agricultural drought risk by exploring data from various channels (VIS, NIR, SWIR, TIR). Considering disparities in data ranges of different drought indices, normalization is implemented before combination. First, SPI is normalized to 0 — 100 given that its normal range is -4 - +4. Then, the three satellite drought indices are normalized to 0 - 100 according to the maximum and minimum values in the image, and aggregated using weighted average method (the result is denoted as ADI, Aggregated drought index). Finally, weighed geometric mean of SPI and ADI are calculated (the result is denoted as DIcombined). A case study in North China plain using three TM images acquired during April-May 2007 show that the method proposed in this study is effective. In spatial domain, DI_combined demonstrates dramatically more details than SPI; in temporal domain, DI_combined shows more reasonable drought development trajectory than satellite indices that are derived from independent TM images.

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