Main Participants: CAI Jiabing, ZHANG Baozhong, XU Di, YU Yingduo, LIU Yu, WEI Zheng, PENG Zhigong, CHEN He

The decision-making network is the core of irrigation district modernization and the precise implementation and realization of digital irrigation districts. The accurate estimation of farmland evapotranspiration and the precision irrigation management system forms the foundation of this decision-making network. Focusing on this pivotal aspect, and with the support of multiple National Natural Science Foundation projects and National Science and Technology Support Program initiatives, relevant scientific research has been conducted for nearly 20 years to explore farmland evapotranspiration mechanism and precision irrigation decision-making simulation in irrigation districts, which has yielded significant results and thus advanced agricultural water conservancy, hydrology, and water resources disciplines.

·   In response to the unclear scale effects of crop evapotranspiration, the ambiguous conversion relationship between temporal and spatial scales, the difficulties in real-time forecasting of crop water requirements based on farmland conditions and the impracticality of existing models, this research profoundly reveals the variation pattern  of farmland evapotranspiration at different spatio-temporal scales and its driving mechanisms. In addition, efficient models for simulating reference evapotranspiration and forecasting crop water requirements has been established, thereby resolving the issues of farmland data traceability, analytical methods and accuracy. These advancements provide foundational models for decision-making theory and technology of precision irrigation.

·   In response to issues such as ambiguous parameter scaling for remote sensing of evapotranspiration in irrigation districts, insufficiently detailed characterization of process parameters, and the urgent need to improve simulation accuracy, this research has innovated precision characterization methods and models for key parameters in remote sensing inversion of evapotranspiration. It has clarified the mechanisms for enhancing the accuracy of simulation models for remote sensing inversion of evapotranspiration and overcome the issue of inaccurate ET estimation caused by light saturation of vegetation indices during the mid-to-late stages of crop growth. These advancements provide scientific insights for the in-depth development of remote sensing mechanisms for evapotranspiration in irrigation districts.

·   In response to issues such as the unclear sensitivity and thresholds of decision-making indicators for farmland irrigation, the simplicity and lack of systematicness of decision-making models, and data asynchrony in precision irrigation decision-making management in irrigation districts, a decision-making theory and methodology for precision farmland irrigation has been developed. In addition, a precision irrigation simulation model and system for irrigation districts has been constructed based on multi-data fusion and assimilation, thereby addressing the multidimensional and nonlinear challenges in precision irrigation decision-making and forecasting. These advancements provide core technologies and methods for the modernization of irrigation districts and the realization of smart irrigation districts.

·   The scale effects and process mechanisms of farmland evapotranspiration and remote sensing-based evapotranspiration inversion in irrigation districts have been systematically addressed, and a method for identifying and differentiating component temperatures of heterogeneous surface pixels has been proposed, thereby overcoming the accuracy and applicability issues of the classical dual-source remote sensing evapotranspiration model caused by the inconsistency between its assumptions and the actual heterogeneous surface conditions.

·   A two-stage precision characterization method based on the red-edge chlorophyll index has been proposed for the first time to differentiate the expression forms of leaf area index for C3 and C4 crops, which significantly improves the accuracy, validation efficiency and reliability of remote sensing monitoring of evapotranspiration and advances major progress in addressing the long-standing "evapotranspiration challenge" critical to agricultural and forest meteorology, hydrology and water resources.

·   A fuzzy logic-based precision irrigation simulation model and system for intelligent multi-index integrated decision-making has been established and a regional precision irrigation methodology and system based on multi-data fusion and assimilation has been  developed. These achievements proactively align with the requirements of building modern and digital irrigation districts under the rigid constraints of water resources.

·   This work has led the way in agricultural and forest meteorology and evapotranspiration research, with some findings and conclusions being widely recognized and cited both domestically and internationally. It has advanced the development of agricultural water conservancy, hydrology and water resources disciplines, making foundational contributions to the efficient utilization of water resources. The research findings have been published in 44 high-level papers and yielded 13 national invention patents, 7 software copyrights and 2 monographs.

·   According to a search in the SCIE database (Web of Science Core Collection), 9 representative papers have been cited a total of 535 times, with non-self citations amounting to 461 times. Among these, the paper "Estimating reference evapotranspiration with the FAO Penman–Monteith equation using daily weather forecast messages" has been cited 257 times; "Dual crop coefficient modelling applied to the winter wheat–summer maize crop sequence in North China Plain: Basal crop coefficients and soil evaporation component" has been cited 108 times; and "Responses of field evapotranspiration to the changes of cropping pattern and groundwater level in large irrigation district of Yellow River basin" has been cited 65 times.

Figure 1 Main Research Content and the Scientific Challenges Addressed

Figure 2 Mechanism Revelation

Figure 3 Estimation Method Improvement

Figure 4 Precision Decision-making Simulation