College of Geography and Environmental Science, Northwest Normal University, 967Anning East Road, Lanzhou, 730070, Gansu Province, China.
Faculty of Geomatics, Lanzhou Jiaotong University, Lanzhou, 730070, China.
Environ Sci Pollut Res Int. 2021 Oct;28(37):51556-51574. doi: 10.1007/s11356-021-14122-y. Epub 2021 May 14.
Drought is a complex natural disaster affected by multiple climate factors and underlying surface. In recent years, drought monitoring indices of remote sensing have been widely applied to monitor drought in a certain region or global. However, some remote sensing drought monitoring indices do not consider the drought-causing factors enough to reflect the comprehensive drought situation of a region fully. In this paper, a new remote sensing drought monitoring index, called Remote Sensing Drought Evaluation Index (RSDEI), was constructed by combining Vegetation Condition Index (VCI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI), and Soil Moisture Condition Index (SMCI) using the spatial principal component analysis (SPCA) method. The reasonableness of RSDEI was test and verified using Net Primary Productivity (NPP), Standardized Precipitation Evapotranspiration Index (SPEI), and unit area crop yield. The RSDEI was also applied to the drought condition monitoring of the northwest arid and semi-arid region from 2001 to 2019.The result demonstrated that the results showed that the RSDEI had a high correlation coefficient with SPEI-12 (R=0.85, p<0.01). It is concluded that the correlation coefficient between RSDEI and NPP is 0.74 at 95% confidence level, which indicates that RSDEI and NPP have a strong correlation. Then, the correlation between RSDEI and crop yield per unit area is 0.89. The results of RSDEI showed that the drought in northwest China started in May and lasted in September from 2001 to 2019. The lowest value of RSDEI appeared in May, which inflected the significant difference of drought level in different month in northwest China. The result of CV (coefficient of variation) showed that the drought variation in the study area had a stable low fluctuation condition as a whole, in the northwest and northeast of study area, which indicated that the changes of drought were different in the past 19 years. The Hurst exponent analysis showed that the area with the positive evolution of Hurst index (0.5<H<1) is 1,845,046.669 km,which accounts for 75.9% of the total area, while the area with reverse evolution characteristics (H<0.5) accounts for 24.1% of the total area. The result obtained above reflected that the drought changes in most regions are better than that in the past 19 years. The trend gradually changes from drought to humid.
干旱是一种受多种气候因素和下垫面影响的复杂自然灾害。近年来，遥感干旱监测指数已广泛应用于监测某一区域或全球的干旱情况。然而，一些遥感干旱监测指数并没有充分考虑致旱因素，无法全面反映区域的综合干旱情况。本文构建了一种新的遥感干旱监测指数，即遥感干旱评价指数（RSDEI），它是通过空间主成分分析（SPCA）方法，将植被条件指数（VCI）、温度条件指数（TCI）、降水条件指数（PCI）和土壤湿度条件指数（SMCI）相结合得到的。利用净初级生产力（NPP）、标准化降水蒸散指数（SPEI）和单位面积作物产量对 RSDEI 的合理性进行了检验和验证。还将 RSDEI 应用于 2001 年至 2019 年西北干旱半干旱地区的干旱状况监测。结果表明，RSDEI 与 SPEI-12 的相关系数较高（R=0.85，p<0.01）。结论是，RSDEI 与 NPP 的相关系数在 95%置信水平下为 0.74，表明 RSDEI 与 NPP 具有很强的相关性。然后，RSDEI 与单位面积作物产量的相关系数为 0.89。RSDEI 的结果表明，2001 年至 2019 年中国西北地区的干旱始于 5 月，持续到 9 月。RSDEI 的最低值出现在 5 月，这表明不同月份西北地区干旱程度存在显著差异。CV（变异系数）的结果表明，研究区的干旱变化整体上呈现稳定的低波动状态，在研究区的西北部和东北部，表明过去 19 年干旱的变化是不同的。Hurst 指数分析表明，正演化 Hurst 指数（0.5<H<1）的区域面积为 1,845,046.669 km²，占总面积的 75.9%，而反演化特征（H<0.5）的区域面积占总面积的 24.1%。以上结果反映了大部分地区的干旱变化好于过去 19 年，干旱逐渐向湿润变化。
