An Efficient Method for Retrieving Citrus Orchard Evapotranspiration Based on Multi-Source Remote Sensing Data Fusion from Unmanned Aerial Vehicles

Zhiwei Zhang, Weiqi Zhang, Chenfei Duan (), Shijiang Zhu () and Hu Li
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Zhiwei Zhang: College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China
Weiqi Zhang: College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China
Chenfei Duan: Hebei Key Laboratory of Smart Water Conservancy, Hebei University of Engineering, Handan 056038, China
Shijiang Zhu: College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China
Hu Li: College of Hydraulic & Environmental Engineering, China Three Gorges University, Yichang 443002, China

Agriculture, 2025, vol. 15, issue 19, 1-27

Abstract: Severe water scarcity has become a critical constraint to global agricultural development. Enhancing both the timeliness and accuracy of crop evapotranspiration ( ET c ) retrieval is essential for optimizing irrigation scheduling. Addressing the limitations of conventional ground-based point-source measurements in rapidly acquiring two-dimensional ET c information at the field scale, this study employed unmanned aerial vehicle (UAV) remote sensing equipped with multispectral and thermal infrared sensors to obtain high spatiotemporal resolution imagery of a representative citrus orchard (Citrus reticulata Blanco cv. ‘Yichangmiju’) in western Hubei at different phenological stages. In conjunction with meteorological data (air temperature, daily net radiation, etc.), ET c was retrieved using two established approaches: the Seguin-Itier (S-I) model, which relates canopy–air temperature differences to ET c , and the multispectral-driven single crop coefficient method, which estimates ET c by combining vegetation indices with reference evapotranspiration. The thermal-infrared-driven S-I model, which relates canopy–air temperature differences to ET c , and the multispectral-driven single crop coefficient method, which estimates ET c by combining vegetation indices with reference evapotranspiration. The findings indicate that: (1) both the S-I model and the single crop coefficient method achieved satisfactory ET c estimation accuracy, with the latter performing slightly better (accuracy of 80% and 85%, respectively); (2) the proposed multi-source fusion model consistently demonstrated high accuracy and stability across all phenological stages (R 2 = 0.9104, 0.9851, and 0.9313 for the fruit-setting, fruit-enlargement, and coloration–sugar-accumulation stages, respectively; all significant at p < 0.01), significantly enhancing the precision and timeliness of ET c retrieval; and (3) the model was successfully applied to ET c retrieval during the main growth stages in the Cangwubang citrus-producing area of Yichang, providing practical support for irrigation scheduling and water resource management at the regional scale. This multi-source fusion approach offers effective technical support for precision irrigation control in agriculture and holds broad application prospects.

Keywords: UAV thermal infrared sensing; crop water use; orchard phenological stages; evapotranspiration inversion; fusion modeling (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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