Regional-Scale Estimation of Maize Plant Moisture Content in Arid Regions Integrating Multi-Source Remote Sensing and Machine Learning

Abstract

Agricultural production in arid regions is strongly constrained by water stress, making timely evaluation of crop water conditions increasingly important. However, conventional measurements of plant moisture content (PMC) primarily rely on destructive oven-drying methods, which are not only labor-intensive and time-consuming but also constrained by limited sample size and spatial coverage. These shortcomings make it difficult to capture the spatial heterogeneity of crop water status across large agricultural regions, thereby restricting regional-scale water diagnosis and precision irrigation decision-making. Focusing on silage maize cultivated in the arid region of Gansu Province, China, this work develops a regional PMC estimation approach by combining multi-source remote sensing data. High-resolution unmanned aerial vehicle (UAV) observations were integrated with Sentinel-2 and Sentinel-3 imagery, while radiometric and temperature corrections were applied to improve data consistency. A set of spectral, textural, and thermal features was derived from multispectral, visible, and thermal infrared datasets. Feature selection based on Pearson correlation was then carried out, followed by the construction of three models, namely Random Forest (RF), Support Vector Machine (SVM), and Partial Least Squares Regression (PLSR). Among them, the RF model performed more reliably, achieving a validation R2 of 0.92 with relatively low prediction error. In addition, calibration using UAV data led to a clear improvement in satellite-based estimates, with R2 increasing from 0.52–0.62 to 0.71–0.74. The generated PMC maps captured both the temporal decline during the growing season and the spatial variability across the study area. Overall, the proposed approach offers a practical option for large-scale monitoring of crop water status and can support irrigation management in water-limited environments.

Plant phenotyping relevance

マルチソースリモートセンシングと機械学習により、トウモロコシの植物含水量という明示的な植物状態を地域スケールで推定・検証する手法開発が中心である。

Code and data availability

The paper's phenotype measurements (PMC ground samples), UAV imagery, and analysis code are not publicly available; the Data Availability Statement explicitly states the data are proprietary or confidential. The only URL present (Copernicus data hub) is a generic public satellite data source, not a paper-specific asset

No evidence-backed public reproduction asset is currently recorded.