Capability of Remotely Sensed Drought Indices for Representing the Spatio-Temporal Variations of the Meteorological Droughts in the Yellow River Basin
Capability of Remotely Sensed Drought Indices for Representing the Spatio-Temporal Variations of the Meteorological Droughts in the Yellow River Basin
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DOI：
10.3390/rs10111834
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发表时间：
2018-11
期刊：
影响因子：
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通讯作者：
Fei Wang;Zongmin Wang;Haibo Yang;Yong Zhao;Zhenhong Li;Jiapeng Wu
中科院分区：
文献类型：
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作者：
Fei Wang;Zongmin Wang;Haibo Yang;Yong Zhao;Zhenhong Li;Jiapeng Wu
由于遥感数据具有覆盖面广、连续性强等优点，被广泛用于大范围干旱监测，弥补气象数据的不足和不连续性。然而，很少有研究集中在各种遥感干旱指数（RSDI）的能力，以代表气象干旱的时空变化。在这项研究中，五个RSDI，即植被状况指数（VCI），温度状况指数（TCI），植被健康指数（VHI），修正的温度植被干燥指数（MTHRR），和归一化植被供水指数（NVSWI），计算使用每月归一化差异植被指数（NDVI）和地表温度（LST）从中分辨率成像光谱仪（MODIS）。采用Savitzky-Golay（S-G）滤波方法对月NDVI和LST数据进行滤波。一个基于气象站的干旱指数代表的标准化降水蒸散指数（SPEI）进行了比较与RSDI。此外，采用无量纲的技能得分（SS）方法，确定了反映黄河流域2000 - 2015年气象干旱的时空最优RSDI。结果表明：（2）春、夏、秋、冬四季的最优RSDI分别为VHI、TCI、MTHI和VCI，RSDI与SPEI的平均相关系数为0.577（3）不同的相对干旱指数与气象干旱指数相比存在0 ~ 3个月的时滞。
Due to the advantages of wide coverage and continuity, remotely sensed data are widely used for large-scale drought monitoring to compensate for the deficiency and discontinuity of meteorological data. However, few studies have focused on the capability of various remotely sensed drought indices (RSDIs) to represent the spatio–temporal variations of meteorological droughts. In this study, five RSDIs, namely the Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Vegetation Health Index (VHI), Modified Temperature Vegetation Dryness Index (MTVDI), and Normalized Vegetation Supply Water Index (NVSWI), were calculated using monthly Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) from the Moderate Resolution Imaging Spectroradiometer (MODIS). The monthly NDVI and LST data were filtered by the Savitzky–Golay (S-G) filtering method. A meteorological station-based drought index represented by the Standardized Precipitation Evapotranspiration Index (SPEI) was compared with the RSDIs. Additionally, the dimensionless Skill Score (SS) method was adopted to identify the spatiotemporally optimal RSDIs for presenting meteorological droughts in the Yellow River basin (YRB) from 2000 to 2015. The results indicated that: (1) RSDIs revealed a decreasing drought trend in the overall YRB consistent with the SPEI except for in winter, and different variations of seasonal trends spatially; (2) the optimal RSDIs in spring, summer, autumn, and winter were VHI, TCI, MTVDI, and VCI, respectively, and the average correlation coefficient between the RSDIs and the SPEI was 0.577 (α = 0.05); and (3) different RSDIs have time lags of zero–three months compared with the meteorological drought index.