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Regional-Scale Estimation of Maize Plant Moisture Content in Arid Regions Integrating Multi-Source Remote Sensing and
Jixuan Yan1,2, Xuchun Li1,2, Zichen Guo1,2
1State Key Laboratory of Aridland Crop Science, College of Water Conservancy and Hydropower Engineering, Gansu Agricultural University, Lanzhou 730070, China.
Plants (Basel, Switzerland)
|July 15, 2026
Summary
This study developed a remote sensing approach to estimate regional plant moisture content (PMC) for silage maize in arid regions. The Random Forest model accurately predicted PMC, improving satellite estimates and supporting precision irrigation.
Area of Science:
- Agricultural Science
- Remote Sensing
- Environmental Monitoring
Background:
- Water stress significantly impacts agricultural production in arid regions, necessitating accurate crop water status evaluation.
- Traditional plant moisture content (PMC) measurements are destructive, labor-intensive, and lack spatial coverage, hindering regional water management.
- Existing methods struggle to capture crop water status heterogeneity, limiting precision irrigation and large-scale water diagnosis.
Purpose of the Study:
- To develop a regional plant moisture content (PMC) estimation approach for silage maize in arid environments.
- To integrate multi-source remote sensing data, including UAV and satellite imagery, for improved PMC monitoring.
- To assess the performance of different machine learning models in estimating crop water status.
Main Methods:
- Combined high-resolution UAV observations with Sentinel-2 and Sentinel-3 imagery, applying radiometric and temperature corrections.
- Extracted spectral, textural, and thermal features from multispectral, visible, and thermal infrared datasets.
- Employed feature selection via Pearson correlation and constructed Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR) models.
Main Results:
- The Random Forest (RF) model demonstrated superior performance, achieving a validation R² of 0.92.
- Calibration with UAV data significantly enhanced satellite-based PMC estimates, increasing R² from 0.52-0.62 to 0.71-0.74.
- Generated PMC maps effectively depicted temporal trends and spatial variability of crop water status.
Conclusions:
- The proposed multi-source remote sensing approach provides a practical method for large-scale crop water status monitoring in arid regions.
- This technique supports effective irrigation management and enhances agricultural resilience in water-limited environments.
- Integrating UAV data with satellite imagery offers a significant advancement in regional crop water assessment.
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