A UAV-based method for root zone soil moisture modeling of different farmland scale with grain and economic crops

环境科学 含水量 水平衡 DNS根区域 土壤水分 土壤科学 降水 灌溉 植被(病理学) 水文学(农业) 精准农业 遥感 Pedotransfer函数 作物系数 水分 多光谱图像 仿真建模 干旱 用水效率 比例(比率) 蒸散量 用水 土壤图 大气科学 相关系数 作物产量 相对湿度 土壤质地
作者
Jichao Wang,Hongwei Huang,H.H.S. Ariyasena,Jian Na Zhao,Xinyue Zhang,Xuerui Gao,Xining Zhao,Yangzi Zhao
出处
期刊:Agricultural Water Management [Elsevier BV]
卷期号:321: 109932-109932 被引量:3
标识
DOI:10.1016/j.agwat.2025.109932
摘要

Rapid and accurate estimation of crop root zone soil moisture (RZSM) is critical for precision agricultural water management, especially in arid and semi-arid regions. This study integrates unmanned aerial vehicle (UAV) multispectral remote sensing with the Remote Sensing-based Water Balance Assessment Tool (RWBAT) model to estimate RZSM for four representative crop types—wheat, maize, rapeseed, and apple trees—in the Loess Plateau region of China. High-resolution vegetation indices (VIs) derived from UAV multispectral imagery and field-measured meteorological data were used to drive the RWBAT model and simulate multi-depth soil moisture dynamics throughout the crop growth period. Based on correlation analysis, NDVI, EVI, SAVI, and DVI were selected to construct crop-specific LAI estimation models, achieving R² values ranging from 0.60 to 0.87. The RWBAT model was calibrated and validated using in-situ soil moisture data from 0 to 140 cm depth, demonstrating high simulation accuracy, particularly at 120–140 cm, with R² values of 0.91 (wheat), 0.76 (apple trees), 0.78 (rapeseed), and 0.80 (maize). Sensitivity analysis revealed that increases in relative humidity and precipitation enhance soil moisture across all crops, with precipitation having a greater influence at deeper soil depths. Overall, the proposed UAV-RWBAT integrated approach demonstrates strong potential for high-resolution, crop-specific root zone soil moisture estimation, offering a promising tool for field-scale water resource management and precision irrigation planning in heterogeneous agricultural landscapes. • Remote-sensing-based water balance assessment tool (RWBAT) for field-scale simulation. • UAV-enhanced RWBAT model improves Root Zone Soil Moisture (RZSM) prediction accuracy. • RWBAT model generates high-resolution multi-temporal soil moisture maps (0–140 cm). • Sensitivity analysis identifies Precipitation and Temperature as key RZSM drivers.
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