Decoding Agricultural Drought Resilience: A Triple-Validated Random Forest Framework Integrating Multi-Source Remote Sensing for High-Resolution Monitoring in the North China Plain

Agricultural drought poses a severe threat to food security in the North China Plain, necessitating accurate and timely monitoring approaches. This study presents a novel drought assessment framework that innovatively integrates multiple remote sensing indices through an optimized random forest algorithm, achieving unprecedented accuracy in regional drought monitoring. The framework introduces three key innovations: (1) a systematic integration of six drought-related factors including vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (PCI), land cover type (LC), aspect (ASPECT), and available water capacity (AWC); (2) an optimized random forest algorithm configuration with 100 decision trees and enhanced feature extraction capability; and (3) a robust triple-validation strategy combining standardized precipitation evapotranspiration index (SPEI), comprehensive meteorological drought index (CI), and soil moisture verification. The framework demonstrates exceptional performance with R2 values consistently above 0.80 for monthly assessments, reaching 0.86 during autumn and 0.73 during summer seasons. Particularly, it achieves 87% accuracy in mild drought (−1.0 < SPEI ≤ −0.5) and 85% in moderate drought (−1.5 < SPEI ≤ −1.0) detection. The 20-year (2000–2019) spatiotemporal analysis reveals that moderate drought events dominated the region (23.7% of total occurrences), with significant intensification during the 2010–2012 and 2014–2016 periods. Summer drought frequency peaked at 12–15 months in south-central Shandong (37°N, 117°E) and eastern Henan (34°N, 114°E). The framework’s high spatial resolution (1 km) and comprehensive validation protocol establish a reliable foundation for agricultural drought monitoring and water resource management, offering a transferable methodology for regional drought assessment worldwide.

Authors

Institutions

Publication Details

Journal
Remote Sensing
Published
2025-04-15
DOI
https://doi.org/10.3390/rs17081404
Citations
2
Primary Topic
Hydrology and Drought Analysis
Type
article
Field-Weighted Citation Impact
2.21

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Decoding Agricultural Drought Resilience: A Triple-Validated Random Forest Framework Integrating Multi-Source Remote Sensing for High-Resolution Monitoring in the North China Plain

Haolin Wang, Jianli Ding, Guoqing Wang, Jianyun Zhang et al.
2 citations
Remote Sensing
Hydrology and Drought Analysis
2.21
article

Decoding Agricultural Drought Resilience: A Triple-Validated Random Forest Framework Integrating Multi-Source Remote Sensing for High-Resolution Monitoring in the North China Plain

Haolin Wang, Jianli Ding, Guoqing Wang, Jianyun Zhang, Chengbin Chu, Song Zhang, Xianyong Meng
article en
2 citations

Abstract

Agricultural drought poses a severe threat to food security in the North China Plain, necessitating accurate and timely monitoring approaches. This study presents a novel drought assessment framework that innovatively integrates multiple remote sensing indices through an optimized random forest algorithm, achieving unprecedented accuracy in regional drought monitoring. The framework introduces three key innovations: (1) a systematic integration of six drought-related factors including vegetation condition index (VCI), temperature condition index (TCI), precipitation condition index (PCI), land cover type (LC), aspect (ASPECT), and available water capacity (AWC); (2) an optimized random forest algorithm configuration with 100 decision trees and enhanced feature extraction capability; and (3) a robust triple-validation strategy combining standardized precipitation evapotranspiration index (SPEI), comprehensive meteorological drought index (CI), and soil moisture verification. The framework demonstrates exceptional performance with R2 values consistently above 0.80 for monthly assessments, reaching 0.86 during autumn and 0.73 during summer seasons. Particularly, it achieves 87% accuracy in mild drought (−1.0 < SPEI ≤ −0.5) and 85% in moderate drought (−1.5 < SPEI ≤ −1.0) detection. The 20-year (2000–2019) spatiotemporal analysis reveals that moderate drought events dominated the region (23.7% of total occurrences), with significant intensification during the 2010–2012 and 2014–2016 periods. Summer drought frequency peaked at 12–15 months in south-central Shandong (37°N, 117°E) and eastern Henan (34°N, 114°E). The framework’s high spatial resolution (1 km) and comprehensive validation protocol establish a reliable foundation for agricultural drought monitoring and water resource management, offering a transferable methodology for regional drought assessment worldwide.

Remote SensingVol. 17(8)
UniLaSalle Amiens (ESIEE-Amiens) (FR), Chinese Academy of Engineering (CN), Xinjiang Institute of Engineering (CN), Nanjing Hydraulic Research Institute (CN), Université Gustave Eiffel (FR), Ministry of Water Resources of the People's Republic of China (CN), China Agricultural University (CN)
National Natural Science Foundation of China, Jinan University
Openalex Percentile: Top 14%
Hydrology and Drought Analysis
2.21
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.