A combined drought monitoring index based on multi-sensor remote sensing data and machine learning

Abstract

The occurrence of drought is related to complicated interactions between many factors, such as precipitation, temperature, evapotranspiration and vegetation. In this study, the relationships between drought and precipitation, temperature, vegetation and evapotranspiration were investigated with a random forest (RF), and a new combined drought monitoring index (CDMI) was constructed. The effectiveness of the CDMI in monitoring drought in Shaanxi Province was verified by the in situ 1 ∼ 12-month standardized precipitation index (SPI); relative soil moisture (RSM) and four other commonly used remote sensing drought monitoring indices. The results show that CDMI is more correlated with the SPI and RSM than the four indices. Moreover, the spatial distributions of drought for the CDMI and RSM are similar. Therefore, the CDMI can be used to monitor droughts in Shaanxi Province, and machine learning can explore the relationships between various factors and establish a drought index without knowledge of the causal mechanisms of these factors.

KEYWORDS:

• Drought monitoring
• MODIS
• TRMM
• random forest

Acknowledgement

We are grateful to the anonymous reviewers for their valuable comments and recommendations, which greatly help us to improve the original version of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [41171310, 41501093] and the Natural Science Basic Research Program of Shaanxi grant number [2016JM4016].