Monitoring drought using composite drought indices based on remote sensing

Area of Science:

• Environmental Science
• Remote Sensing
• Agricultural Science

Background:

• Drought poses a significant threat to agriculture, ecology, and economy in North China, particularly in Shandong Province.
• Effective drought monitoring is crucial for mitigating its widespread impacts.

Purpose of the Study:

• To develop and validate composite drought indices (MCDIs) for monitoring drought in Shandong Province.
• To assess the capability of integrating multiple remote sensing data sources for drought assessment.

Main Methods:

• Utilized multivariable linear regression (MCDIs) to integrate data from TRMM (precipitation), GLDAS-2.1 (soil moisture), MODIS (LST), and NDVI.
• Performed Pearson correlation analyses between remote sensing indices and in-situ drought indices (SPEI, SPI, MI) across various time scales.
• Optimized the MCDI model using mediator and moderator variables.

Main Results:

• MCDIs demonstrated higher correlation coefficients (r) with SPEIs compared to individual remote sensing drought indices.
• MCDI-1, based on 1-month SPEI, proved suitable for meteorological drought monitoring.
• MCDI-9, based on 9-month SPEI, showed effectiveness in indicating agricultural drought.

Conclusions:

• Multivariable linear regression and MCDIs are effective for drought monitoring in Shandong Province and similar regions.
• The proposed MCDIs offer a robust approach to integrating diverse remote sensing data for comprehensive drought assessment.