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

Author

Listed:
  • Wang, Jichao
  • Huang, Hongwei
  • Ariyasena, H.H.S.
  • Zhao, Jian
  • Zhang, Xinyue
  • Gao, Xuerui
  • Zhao, Xining
  • Zhao, Yangzi

Abstract

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.

Suggested Citation

  • Wang, Jichao & Huang, Hongwei & Ariyasena, H.H.S. & Zhao, Jian & Zhang, Xinyue & Gao, Xuerui & Zhao, Xining & Zhao, Yangzi, 2025. "A UAV-based method for root zone soil moisture modeling of different farmland scale with grain and economic crops," Agricultural Water Management, Elsevier, vol. 321(C).
  • Handle: RePEc:eee:agiwat:v:321:y:2025:i:c:s0378377425006468
    DOI: 10.1016/j.agwat.2025.109932
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    References listed on IDEAS

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    1. Cheng, Minghan & Jiao, Xiyun & Liu, Yadong & Shao, Mingchao & Yu, Xun & Bai, Yi & Wang, Zixu & Wang, Siyu & Tuohuti, Nuremanguli & Liu, Shuaibing & Shi, Lei & Yin, Dameng & Huang, Xiao & Nie, Chenwei , 2022. "Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning," Agricultural Water Management, Elsevier, vol. 264(C).
    2. Mustafa, S.M.T. & Vanuytrecht, E. & Huysmans, M., 2017. "Combined deficit irrigation and soil fertility management on different soil textures to improve wheat yield in drought-prone Bangladesh," Agricultural Water Management, Elsevier, vol. 191(C), pages 124-137.
    3. Wu, Zongjun & Cui, Ningbo & Zhang, Wenjiang & Yang, Yenan & Gong, Daozhi & Liu, Quanshan & Zhao, Lu & Xing, Liwen & He, Qingyan & Zhu, Shidan & Zheng, Shunsheng & Wen, Shenglin & Zhu, Bin, 2024. "Estimation of soil moisture in drip-irrigated citrus orchards using multi-modal UAV remote sensing," Agricultural Water Management, Elsevier, vol. 302(C).
    4. Wantong Li & Mirco Migliavacca & Matthias Forkel & Jasper M. C. Denissen & Markus Reichstein & Hui Yang & Gregory Duveiller & Ulrich Weber & Rene Orth, 2022. "Widespread increasing vegetation sensitivity to soil moisture," Nature Communications, Nature, vol. 13(1), pages 1-9, December.
    5. Yu, Jingxin & Zhang, Xin & Xu, Linlin & Dong, Jing & Zhangzhong, Lili, 2021. "A hybrid CNN-GRU model for predicting soil moisture in maize root zone," Agricultural Water Management, Elsevier, vol. 245(C).
    6. Ming Li & Yueguan Yan, 2024. "Comparative Analysis of Machine-Learning Models for Soil Moisture Estimation Using High-Resolution Remote-Sensing Data," Land, MDPI, vol. 13(8), pages 1-24, August.
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