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Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors

Author

Listed:
  • Zhu, Shidan
  • Cui, Ningbo
  • Jin, Huaan
  • Jin, Xiuliang
  • Guo, Li
  • Jiang, Shouzheng
  • Wu, Zongjun
  • Lv, Min
  • Chen, Fei
  • Liu, Quanshan
  • Wang, Mingjun

Abstract

Accurately estimating of soil moisture content (SMC) is essential for effective irrigation water management and optimizing plant water productivity. Recent advancements in multi-sensor platforms and ensemble learning (EL) algorithms such as the use of UAV-based multispectral (MS) and thermal infrared (TIR) images, have enabled precise mapping of SMC at the field level. The purpose of this study is to optimize the multi-dimensional (n-D) index set and EL model to achieve accurate inversion mapping of SMC in the 0–60 cm root zone at a kiwifruit orchard during the fruit growth period (May-Sep 2022). The n-D index set was computed using kiwifruit canopy MS and TIR data from UAV along with in-field temperature data (air temperature [Ta], kiwifruit canopy temperature [Tc], soil temperature [Ts], and Ta-Tc, Ta-Ts, Tc-Ts). The primary findings of this study are as follows: (1) The Three-Dimensional Drought Index (TDDI), Temperature Vegetation Drought Index (TVDI), and Crop Water Stress Index (CWSI) demonstrated superior performance in capturing temporal variations of root zone SMC compared to Normalized Difference Vegetation Index (NDVI) and Enhance Vegetation Index (EVI). (2) The optimal Categorical Boosting (Catboost) model with two optimal TDDIs (TDDI22: (Tc-Ts)-EVI10,6,2-CWSI space and TDDI7: Tc-NDVI7,3-CWSI space), exhibited exceptional performance for SMC estimation, with determination coefficient (R2) and root-mean-square error (RMSE) of 0.966 ± 0.010 and 0.046 ± 0.003%, respectively. (3) The planted-by-planted mapping performed well in estimating root zone SMC at different growth stages and under different irrigation treatments, with a correlation coefficient (R) of 0.936 ± 0.074 (p < 0.001). This study demonstrated that the integration of multi-sensor indicators and EL models can improve the accuracy of SMC estimation due to its robust adaptability to various field conditions. Furthermore, the planted-grid-based SMC mapping framework can be considered as a valuable approach for monitoring dynamic SMC, facilitating informed irrigation decisions at the individual planting grid.

Suggested Citation

  • Zhu, Shidan & Cui, Ningbo & Jin, Huaan & Jin, Xiuliang & Guo, Li & Jiang, Shouzheng & Wu, Zongjun & Lv, Min & Chen, Fei & Liu, Quanshan & Wang, Mingjun, 2024. "Optimization of multi-dimensional indices for kiwifruit orchard soil moisture content estimation using UAV and ground multi-sensors," Agricultural Water Management, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:agiwat:v:294:y:2024:i:c:s0378377424000404
    DOI: 10.1016/j.agwat.2024.108705
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    References listed on IDEAS

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    1. Cheng, Minghan & Sun, Chengming & Nie, Chenwei & Liu, Shuaibing & Yu, Xun & Bai, Yi & Liu, Yadong & Meng, Lin & Jia, Xiao & Liu, Yuan & Zhou, Lili & Nan, Fei & Cui, Tengyu & Jin, Xiuliang, 2023. "Evaluation of UAV-based drought indices for crop water conditions monitoring: A case study of summer maize," Agricultural Water Management, Elsevier, vol. 287(C).
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    1. Liu, Qi & Hu, Xiaolong & Zhang, Yiqiang & Shi, Liangsheng & Yang, Wei & Yang, Yixuan & Zhang, Ruxin & Zhang, Dongliang & Miao, Ze & Wang, Yifan & Qu, Zhongyi, 2025. "Improving maize water stress diagnosis accuracy by integrating multimodal UAVs data and leaf area index inversion model," Agricultural Water Management, Elsevier, vol. 312(C).
    2. Liu, Quanshan & Wu, Zongjun & Cui, Ningbo & Zheng, Shunsheng & Zhu, Shidan & Jiang, Shouzheng & Wang, Zhihui & Gong, Daozhi & Wang, Yaosheng & Zhao, Lu, 2024. "Soil moisture content estimation of drip-irrigated citrus orchard based on UAV images and machine learning algorithm in Southwest China," Agricultural Water Management, Elsevier, vol. 303(C).
    3. Li, Zhenxiao & Cheng, Qian & Chen, Zhen & Xiang, Youzhen & Hu, Xiaotao & Lazarovitch, Naftali & Zhen, Jingbo, 2025. "Estimating soil water content of cotton fields using UAV-based multi-source remote sensing data fusion," Agricultural Water Management, Elsevier, vol. 322(C).
    4. Parra-López, Carlos & Ben Abdallah, Saker & Garcia-Garcia, Guillermo & Hassoun, Abdo & Trollman, Hana & Jagtap, Sandeep & Gupta, Sumit & Aït-Kaddour, Abderrahmane & Makmuang, Sureerat & Carmona-Torres, 2025. "Digital technologies for water use and management in agriculture: Recent applications and future outlook," Agricultural Water Management, Elsevier, vol. 309(C).
    5. Li, Xinshuai & Chen, Zili & He, Jingyuan & Liu, Qingyuan & Niu, Zhen & Gao, Zhilong & Jia, Zefeng & Niu, Zijie & Zhang, Dongyan & Zhou, Mingu, 2026. "Research on inversion and prediction of root region soil water content in kiwifruit based on hyperparameter tuning by transformer-DsaGRU," Agricultural Water Management, Elsevier, vol. 324(C).
    6. 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).
    7. Liu, Qi & Hu, Xiaolong & Zhang, Yiqiang & Shi, Liangsheng & Wang, Liping & Yang, Yixuan & Shen, Jiawen & Zhu, Jiong & Zhang, Dongliang & Qu, Zhongyi, 2025. "Assimilating UAV observations and crop model simulations for dynamic estimation of crop water stress," Agricultural Water Management, Elsevier, vol. 318(C).
    8. Zoratipour, Elahe & Veysi, Shadman & Mohammadi, Amir Soltani & Nasab, Saeed Boroomand & Naseri, Abd Ali, 2025. "Bias correction of satellite based crop water stress index using machine learning methods," Agricultural Water Management, Elsevier, vol. 320(C).

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