Evaluation and Modifying of Multispectral Drought Severity Index
LIU Jun1, 2, 3, LIANG Shao-qing3, LI Yan-rong3, QIN Rong-rong3, ZHANG Tao-ran3, YANG Qiang3, DU Ling-tong4
1. Postdoctoral Scientific Research Workstation of Safety Technology and Engineering, Taiyuan University of Technology, Taiyuan 030024, China
2. Postdoctoral Scientific Research Workstation of Shanxi Transportation Technology Research & Development Co., Ltd., Taiyuan 030006, China
3. College of Mining Engineering, Taiyuan University of Technology, Taiyuan 030024, China
4. Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwest China, Yinchuan 750021, China
Abstract：Drought is a very destructive natural disaster. In recent years, affected by climate change, the frequency of drought is increasing all over the world, causing serious economic losses. Accurate monitoring of drought information is the basis of disaster prevention and reduction. Drought Severity Index (DSI) is a kind of index that can effectively capture regional drought information. It has been proved that it has great potential in global drought monitoring, but its classification of drought grade is obviously regional. In order to test the applicability of DSI on the provincial scale and correct the classification differences, DSIMODIS and DSIAVHRR from 2001 to 2014 were obtained based on the data sets of MODIS ET/PET, NDVI and AVHRR NDVI in Shanxi Province. Meanwhile, combined with SPEI, the spatial and temporal distribution of drought in these three indexes was compared and analyzed. To verify the reliability of SPEI, long time series SPEIs were compared with historical drought records. In order to investigate the errors of DSI in the region, DSIMODIS, DSIAVHRR and SPEI were compared in time frequency and spatial distribution. Finally, as SPEI a reference and based on the original DSI classification standard, the new drought classification standard was obtained by adjusting the threshold of drought grade of DSIMODIS with 0.1 as the step size. In addition, DSIMM was used to monitor the provincial seasonal scale from 2001 to 2014, to capture the typical drought events in 2001 and 2002, and to verify the applicability and robustness of DSIMM in drought monitoring in Shanxi. Studies show that there is a high correlation between DSIMODIS and DSIAVHRR, with a correlation coefficient of 0.75. Both of them have consistent performance in spatial distribution and time frequency of drought, indicating that DSI can be extended to AVHRR data set in the absence of data to make up for the deficiency of MODIS data in long time series monitoring. D1 was underestimated by DSIMODIS, while D2, D3 and D4 were overestimated in Shanxi province. In the four grades of mild drought D1, moderate drought D2, severe drought D3 and extreme drought D4, DSIMM and SPEI showed a high degree of consistency in the occurrence frequency and spatial ratio of drought. The time frequency of D3 was completely consistent, and the spatial distribution consistency of D4 was 0.98. The drought classification standard of DSIMM can monitor the drought information at different temporal and spatial scale accurately, which can be applied to the whole governor time series drought monitoring. The results can be used for reference in regional scale drought monitoring using DSI, and a simple calculation method with high accuracy is obtained for Shanxi Province drought information monitoring.
[1] Wilhite D A. Drought: A Global Assessment, 2000, (1): 3.
[2] Cook B I, SmerdonJ E, Seager R, et al. Climate Dynamics, 2014, 43(9/10): 2607.
[3] Nasrollahi N, Aghakouchak A, Cheng L, et al. Water Resources Research, 2015, 51(4): 2847.
[4] Xu Ying, Ge Junzhu, Tian Shaoyang, et al. Science of The Total Environment，2015, 505: 1043.
[5] Venteris E R, Tagestad J D, Downs J L, et al. International Journal of Applied Earth Observation and Geoinformation, 2015, 39: 160.
[6] Palmer W C. Meteorological Droughts. U. S. Department of Commerce, 1965, 45: 58.
[7] Vicente-Serrano S M, Begueria S, Lope-Moreno J I. Journal of Geophysical Research: Atmospheres, 2011, 116(D19): 1.
[8] Mu Q, Zhao M, Kimball J S，et al. Meteorol. Soc., 2013, 94(1): 83.
[9] Sandholt I, Rasmussen K, Andersen J. Remote Sensing of Environment, 2002, 79(2-3): 213.
[10] Du Lingtong, Song Naiping, Liu Ke, et al. Remote Sensing, 2017, 9(2): 177.
[11] Du L, Tian Q, Yu T, et al. International Journal of Applied Earth Observation and Geoinformation, 2013，23: 245.
[12] Hou J, Du L, Liu K, et al. Theoretical and Applied Climatology, 2019，136(3-4): 915.
[13] SHEN Guo-qiang, ZHENG Hai-feng, LEI Zhen-feng(沈国强, 郑海峰, 雷振锋). Acta Ecologicasinica(生态学报), 2017, 37(11): 3787.
[14] Vicente-Serrano S M, Beguería S, López-Moreno J. International Journal of Climate, 2010, 23: 1696.
[15] HUANG Jian-xi, ZHANG Jie, LIU Jun-ming, et al(黄健熙, 张 洁, 刘峻明, 等). Transactions of The Chinese Society of Agricultural Machinery（农业机械学报）2015, 46(3): 166.
[16] LI Na, HUO Zhi-guo, QIAN Jin-xia (李 娜, 霍治国，钱锦霞). Chinese Journal of Ecology(生态学杂志), 2019, 38(7): 2249.
[17] Tan C, Yang J, Li M. China Atmosphere, 2015, (6): 1399.
