College of Computer Science and Technology, Excellent Research Center of Space Information and Earth Big Data, Qingdao University, Shandong, 266071, China.
College of Business, Qingdao University, Shandong, 266071, China.
Sci Total Environ. 2020 Apr 1;711:134585. doi: 10.1016/j.scitotenv.2019.134585. Epub 2019 Nov 22.
Drought is one of the most frequent disasters occurring in North China and has a great influence on agriculture, ecology and economy. To monitor drought of typical dry areas in North China, Shandong Province, this paper proposed composite drought indices using multivariable linear regression (MCDIs) to integrate Tropical Rainfall Measuring Mission (TRMM) derived precipitation, Global Land Data Assimilation System Version 2.1 (GLDAS-2.1) derived soil moisture, Moderate Resolution Imaging Spectroradiometer (MODIS) derived land surface temperature (LST) and normalized difference vegetation index (NDVI) from 2013 to 2017 (March to September). Pearson correlation analyses were performed between single remote sensing drought indices and in-situ drought indices, standardized precipitation evapotranspiration index (SPEI), in different time scales to assess the capability of single indices over Shandong Province. The multivariable linear regression method was used to established MCDIs, and mediator and moderator variables were introduced to optimize the model. The correlation coefficients (r) between MCDIs and SPEIs was higher than that between each single index and SPEIs. Additionally, when we investigate the correlations of different MCDIs with both standardized precipitation index (SPI) and moisture index (MI), the highest r values with both 1-month SPI and MI were acquired by the MCDI based on 1-month SPEI (MCDI-1). This suggested MCDI-1 was suitable to monitor meteorological drought. Also, the comparison between MCDI based on 9-month SPEI (MCDI-9) and soil moisture showed MCDI-9 was a good indicator for agricultural drought. Therefore, multivariable linear regression and MCDIs were recommended to be an effective method and indices for monitoring drought across Shandong Province and similar areas.
干旱是华北地区最常见的灾害之一，对农业、生态和经济有很大的影响。为了监测华北典型干旱地区的干旱情况，本研究提出了利用多元线性回归（MCDI）综合热带降雨测量任务（TRMM）衍生降水、全球陆面数据同化系统版本 2.1（GLDAS-2.1）衍生土壤湿度、中分辨率成像光谱仪（MODIS）衍生陆地表面温度（LST）和归一化植被指数（NDVI）的综合干旱指数，这些数据来自 2013 年至 2017 年（3 月至 9 月）。在不同的时间尺度上，对单种遥感干旱指数与实地干旱指数、标准化降水蒸散指数（SPEI）之间进行了皮尔逊相关分析，以评估单种指数在山东省的性能。采用多元线性回归方法建立 MCDI，并引入中介和调节变量对模型进行优化。MCDI 与 SPEI 之间的相关系数（r）高于每个单种指数与 SPEI 之间的相关系数。此外，当我们研究不同的 MCDI 与标准化降水指数（SPI）和湿度指数（MI）的相关性时，基于 1 个月 SPEI 的 MCDI（MCDI-1）与 1 个月 SPI 和 MI 均具有最高的 r 值。这表明 MCDI-1 适合监测气象干旱。同时，MCDI-9 与土壤湿度的比较表明，MCDI-9 是农业干旱的一个良好指标。因此，多元线性回归和 MCDI 被推荐为监测山东省及类似地区干旱的有效方法和指标。
