Capability of Remotely Sensed Drought Indices for Representing the Spatio–Temporal Variations of the Meteorological Droughts in the Yellow River Basin

Zongmin Wang,Jiapeng Wu,Haibo Yang,Yong Zhao,Zhenhong Li,Fei Wang

doi:10.3390/rs10111834

Abstract

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.

Highlights

Drought is a complex and recurring natural disaster that occurs throughout the world and often has negative impacts on many sectors of society [1,2]
The original Normalized Difference Vegetation Index (NDVI) and land surface temperature (LST) of the sudden drop were attributed to cloud and snow pixels, which resulted in sudden descent points that were inconsistent with the overall trend
The capability of remotely sensed drought indices (RSDIs) should be fully considered under different spatio–temporal patterns, which can improve the accuracy of drought monitoring in the Yellow River basin (YRB)

Summary

Introduction

Drought is a complex and recurring natural disaster that occurs throughout the world and often has negative impacts on many sectors of society [1,2]. Traditional drought monitoring is based on data from meteorological stations. In 2015, 17 sustainable development goals (SDGs) were formally adopted at the UN Sustainable Development Summit, which clearly indicates that remote sensing technology has become an important way to reduce the risk of loss from drought disaster and achieve the goal of sustainable development [10,11]. Remote sensing technology makes up for the shortage of meteorological stations thanks to its advantages of objective, its timely, economic, and wide coverage, its continuous data, and its ability to extend traditional “point” measurements to information about the entire areas. Remote sensing has proved to be the most promising technology in drought monitoring, and is widely used in drought prevention, response, recovery, and mitigation [12,13]

Methods

Results

Conclusion