基于多源信息的作物需水预测与灌溉决策支持模型构建研究进展

Research progress on crop water requirement prediction and irrigation decision support model development based on multi-source information

摘要

为解决气候变化与水资源约束背景下作物需水预测精度不足及灌溉决策协同性不强的问题，采用文献综述方法，围绕多源信息-作物需水预测-灌溉决策支持模型构建主线，系统梳理作物需水量与蒸散发量、灌溉制度与阈值控制等理论基础，总结气象与气候数据、遥感与蒸散发产品、土壤与地下水监测、作物与管理信息以及物联网现场感知等多源数据的组成与特点，归纳了物理、机理模型、统计与经验模型、机器学习与深度学习模型在作物需水预测中的应用进展，分析灌溉制度优化、动态调度、多目标优化、灌溉决策支持系统、智慧灌溉平台及田块-灌区-流域多尺度协同调度等研究内容，并从气候情景响应、高分辨率遥感与地面观测融合、可解释与可迁移智能模型、多目标多尺度决策及数字孪生灌区等方面概括了后续研究方向。

Abstract

Water scarcity has emerged as a major constraint on agricultural production as a result of climate change, population growth, and rising food demand. Therefore, accurate prediction of crop water requirements and rational irrigation decisions are vitally important in improving water use efficiency and supporting food security. With the quick development of Internet-of-things (IoT) technologies, remote sensing, reanalysis products, and ground monitoring, multi-source information has given crop water requirement prediction and irrigation management new support. A systematic review of the literature was carried out using the "multi-source information-crop water requirement prediction-irrigation decision support" framework. The review provides an overview of the primary data sources used in previous research, such as soil and groundwater observations, crop and management data, remote sensing and evapotranspiration products, meteorological and climate data, and IoT-based sensing data. Modeling approaches are grouped into process-based models, statistical and empirical models, and data-driven models such as machine learning and deep learning. Related studies on irrigation scheduling optimization, dynamic regulation, decision support systems, smart irrigation platforms, and multi-scale coordination are also reviewed. Multi-source information has become an important foundation for improving crop water requirement prediction and guiding irrigation decisions. Research is moving from single-source data and single-model applications toward integrated frameworks that combine multiple data sources, modeling methods, and management scales. Standardized data integration, coupling process-based and data-driven models, uncertainty analysis, and the creation of digital twin irrigation districts and intelligent irrigation platforms for integrated prediction, decision-making, and regulation should all be strengthened in future work.

关键词

作物需水预测 / 多源信息 / 遥感蒸散发 / 机器学习 / 作物模型

Key words

crop water requirement prediction / multi-source information / remote sensing evapotranspiration / machine learning / crop model

参考文献

[1] Biswas A, Sarkar S, Das S, et al. Water scarcity: A global hindrance to sustainable development and agricultural production: A critical review of the impacts and adaptation strategies[J]. Cambridge Prisms: Water, 2025, 3: e4. DOI: 10.1017/wat.2024.16.

[2] Ye Zhigang, Yin Shan, Cao Yin. et al. AI-driven optimization of agricultural water management for enhanced sustainability[J]. Scientific Reports, 2024, 14(1): 25721. DOI: 10.1038/s41598-024-76915-8.

[3] Jenkins M, Block D E. A review of methods for data-driven irrigation in modern agricultural systems[J]. Agronomy, 2024, 14(7): 1355. DOI: 10.3390/agronomy14071355.

[4] Lakhiar I A, Yan Haofang, Zhang Chuan, et al. A review of precision irrigation water-saving technology under changing climate for enhancing water use efficiency, crop yield, and environmental footprints[J]. Agriculture, 2024, 14(7): 1141. DOI: 10.3390/agriculture14071141.

[5] Jaiswal N, Kumar T V, Shukla C. Smart drip irrigation systems using IoT: A review of architectures, machine learning models, and emerging trends[J]. Discover Agriculture, 2025, 3(1): 253. DOI: 10.1007/s44279-025-00430-1.

[6] 敖建毅. 基于作物需水空间分布的农田灌溉分区方法和系统研究[D]. 咸阳: 西北农林科技大学, 2022. DOI: 10.27409/d.cnki.gxbnu.2022.001047.

[7] Mansoor S, Iqbal S, Popescu S M, et al. Integration of smart sensors and IoT in precision agriculture: Trends, challenges and future prospectives[J]. Frontiers in Plant Science, 2025, 16: 1587869. DOI: 10.3389/fpls.2025.1587869.

[8] Abdelmoneim A A, Kimaita H N, Al Kalaany C M, et al. IoT sensing for advanced irrigation management: A systematic review of trends, challenges, and future prospects[J]. Sensors (Basel, Switzerland), 2025, 25(7): 2291. DOI: 10.3390/s25072291/s1.

[9] Li Shenglin, Zhu Pengyuan, Song Ni, et al. Regional soil moisture estimation leveraging multi-source data fusion and automated machine learning[J]. Remote Sensing, 2025, 17(5): 837. DOI: 10.3390/rs17050837.

[10] Roccatello E, Pagano A, Levorato N, et al. State of the art in internet of things standards and protocols for precision agriculture with an approach to semantic interoperability[J]. Network, 2025, 5(2): 14. DOI: 10.3390/network5020014.

[11] Huang Dongfu, Liu Liqun. A heterogeneous image registration model for an apple orchard[J]. Agronomy, 2025, 15(4): 889. DOI: 10.3390/agronomy15040889.

[12] 赵泽欢, 范兴科. 土壤含水量对夏玉米蒸发蒸腾量和光合性能的影响[J]. 干旱地区农业研究, 2022, 40(5): 126-133. DOI: 10.7606/j.issn.1000-7601.2022.05.14.

[13] 郝林如, 郭向红, 雷涛, 等. 气象要素缺失条件下不同机器学习模型计算参考作物蒸散量比较[J]. 节水灌溉, 2022(7): 102-108.

[14] 李尧, 刘江舟, 刘轩昂, 等. 考虑日光诱导叶绿素荧光的冬小麦蒸散量模拟[J]. 农业机械学报, 2025, 56(5): 534-542. DOI: 10.6041/j.issn.1000-1298.2025.05.051.

[15] Koç D L. Assessment of pan coefficient models for the estimation of the reference evapotranspiration in a Mediterranean environment in Turkey[J]. PeerJ, 2022, 10: e13554. DOI: 10.7717/peerj.13554.

[16] 党晓宏, 徐立杰, 高永, 等. 采煤沉陷对地表土壤蒸发和植被蒸腾的影响[J]. 应用基础与工程科学学报, 2022, 30(6): 1389-1401. DOI: 10.16058/j.issn.1005-0930.2022.06.006.

[17] Zhao Yin, Li Guoan, Li Sien, et al. A review on the optimization of irrigation schedules for farmlands based on a simulation-optimization model[J]. Water, 2024, 16(17): 2545. DOI: 10.3390/w16172545.

[18] 葛冰洋, 于砚宁, 孙佑涛, 等. 基于Sentinel-1 SAR数据的冬小麦灌溉事件识别与频次估算[J]. 农业工程学报, 2025, 41(5): 116-125. DOI: 10.11975/j.issn.1002-6819.202407147.

[19] 李茉, 徐敏, 王璐晨, 等. 基于数据融合的高时空分辨率作物蒸散发反演与高效精细化灌溉决策[J]. 农业机械学报, 2025, 56(8): 62-73. DOI: 10.6041/j.issn.1000-1298.2025.08.006.

[20] 张益, 吕明阳, 韩一军. 推进我国小麦生产节水增效: 作用机制、关键问题与路径优化[J]. 江苏农业科学, 2025, 53(11): 16-23. DOI: 10.15889/j.issn.1002-1302.2025.11.003.

[21] 苑蒙飞, 徐利岗, 窦家晅, 等. 基于土壤水分下限的设施番茄适宜灌溉控制指标试验[J]. 南水北调与水利科技（中英文）, 2024, 22(6): 1239-1248. DOI: 10.13476/j.cnki.nsbdqk.2024.0123.

[22] 王科. 温室春茬茄子蒸散量估算方法及适宜灌溉量研究[D]. 呼和浩特: 内蒙古农业大学, 2023. DOI: 10.27229/d.cnki.gnmnu.2023.000957.

[23] Pan Junrui, Yu Long, Zhou Bo, et al. A daily reference crop evapotranspiration forecasting model based on improved informer[J]. Agriculture, 2025, 15(9): 933. DOI: 10.3390/agriculture15090933.

[24] 程雪, 孙爽, 张方亮, 等. 我国北方地区苹果干旱时空分布特征[J]. 应用气象学报, 2020, 31(1): 63-73. DOI: 10.11898/1001-7313.20200106.

[25] 张瑞, 王迎春, 张亦洲, 等. 基于最大熵增模型对京津冀地表通量的估算与评估[J]. 气象学报, 2023, 81(3): 492-505. DOI: 10.11676/qxxb2023.20220116.

[26] Ren Yiting, Li Qiangzi, Du Xin, et al. Analysis of corn yield prediction potential at various growth phases using a process-based model and deep learning[J]. Plants, 2023, 12(3): 446. DOI: 10.3390/plants12030446.

[27] Bijlwan A, Ranjan R, Singh M, et al. Predicting crop disease severity using real time weather variability through machine learning algorithms[J]. Scientific Reports, 2025, 15(1): 34767. DOI: 10.1038/s41598-025-18613-7.

[28] 岩腊, 龙笛, 白亮亮, 等. 基于多源信息的水资源立体监测研究综述[J]. 遥感学报, 2020, 24(7): 787-803.

[29] Djaman K, Mohammed A T, Koudahe K. Accuracy of estimated crop evapotranspiration using locally developed crop coefficients against satellite-derived crop evapotranspiration in a semiarid climate[J]. Agronomy, 2023, 13(7): 1937. DOI: 10.3390/agronomy13071937.

[30] 金楷仑, 郝璐. 基于遥感数据与SEBAL模型的江浙沪地区地表蒸散反演[J]. 国土资源遥感, 2020, 32(2): 204-212. DOI: 10.6046/gtzyyg.2020.02.26.

[31] 王桐, 唐荣林, 李召良, 等. 遥感反演蒸散发的日尺度扩展方法研究进展[J]. 遥感学报, 2019, 23(5): 813-830.

[32] 唐荣林, 王晟力, 姜亚珍, 等. 基于地表温度-植被指数三角/梯形特征空间的地表蒸散发遥感反演综述[J]. 遥感学报, 2021, 25(1): 65-82.

[33] Torres-Quezada E, Fuentes-Peñailillo F, Gutter K, et al. Remote sensing and soil moisture sensors for irrigation management in avocado orchards: A practical approach for water stress assessment in remote agricultural areas[J]. Remote Sensing, 2025, 17(4): 708. DOI: 10.3390/rs17040708.

[34] 王儒敬. 农业传感器: 研究进展、挑战与展望[J]. 智慧农业（中英文）, 2024, 6(1): 1-17. DOI: 10.12133/j.smartag.SA202401017.

[35] 陈泠羽. 陆-气耦合对中国东部夏季高温事件影响的区域性差异及其可能机制[D]. 南京: 南京信息工程大学, 2024. DOI: 10.27248/d.cnki.gnjqc.2024.000115.

[36] 云文丽. 河套灌区向日葵节水灌溉预报模型研究[J]. 江苏农业科学, 2021, 49(3): 187-191. DOI: 10.15889/j.issn.1002-1302.2021.03.033.

[37] Guo Hui, Li Sien, Kang Shaozhong, et al. Crop coefficient for spring maize under plastic mulch based on 12-year eddy covariance observation in the arid region of northwest China[J]. Journal of Hydrology, 2020, 588: 125108. DOI: 10.1016/j.jhydrol.2020.125108.

[38] Ali A, Hussain T, Zahid A. Smart irrigation technologies and prospects for enhancing water use efficiency for sustainable agriculture[J]. AgriEngineering, 2025, 7(4): 106. DOI: 10.3390/agriengineering7040106.

[39] Ndulue E, Ranjan R S. Performance of the FAO Penman-Monteith equation under limiting conditions and fourteen reference evapotranspiration models in southern Manitoba[J]. Theoretical & Applied Climatology, 2021, 143(3): 1285-1298. DOI: 10.1007/s00704-020-03505-9.

[40] Pereira L S, Paredes P, Hunsaker D J, et al. Standard single and basal crop coefficients for field crops: Updates and advances to the FAO-56 crop water requirements method[J]. Agricultural Water Management, 2021, 243: 106466. DOI: 10.1016/j.agwat.2020.106466.

[41] Steduto P, Hsiao T C, Raes D, et al. AquaCrop-the FAO crop model to simulate yield response to water: Ⅰ. Concepts and underlying principles[J]. Agronomy Journal, 2009, 101(3): 426-437. DOI: 10.2134/agronj2008.0139s.

[42] Hsiao T C, Heng L, Steduto P, et al. AquaCrop-the FAO crop model to simulate yield response to water: Ⅲ. Parameterization and testing for maize[J]. Agronomy Journal, 2009, 101(3): 448-459. DOI: 10.2134/agronj2008.0218s.

[43] Foster T, Brozović N, Butler A P, et al. AquaCrop-OS: An open source version of FAO's crop water productivity model[J]. Agricultural Water Management, 2017, 181: 18-22. DOI: 10.1016/j.agwat.2016.11.015.

[44] Jones J W, Hoogenboom G, Porter C H, et al. The DSSAT cropping system model[J]. European Journal of Agronomy, 2003, 18(3-4): 235-265. DOI: 10.1016/S1161-0301(02)00107-7.

[45] Ines A V M, Honda K, Gupta A D, et al. Combining remote sensing-simulation modeling and genetic algorithm optimization to explore water management options in irrigated agriculture[J]. Agricultural Water Management, 2006, 83(3): 221-232. DOI: 10.1016/j.agwat.2005.12.006.

[46] 裴鼎, 余航, 潘梦楠, 等. 基于水量平衡的澳洲坚果灌溉制度研究[J]. 江西农业学报, 2025, 37(1): 84-91. DOI: 10.19386/j.cnki.jxnyxb.2025.01.011.

[47] Li Jing, Liu Deyao, Zhang Yitao, et al. Irrigation optimization via crop water use in saline coastal areas: A field data analysis in China's Yellow River Delta[J]. Plants, 2023, 12(10): 1990. DOI: 10.3390/plants12101990.

[48] Khazaeiathar M, Schmalz B. Addressing volatility and nonlinearity in discharge modeling: ARIMA-iGARCH for short-term hydrological time series simulation[J]. Hydrology, 2025, 12(8): 197. DOI: 10.3390/hydrology12080197.

[49] Yang Yong, Sun Huaiwei, Xue Jie, et al. Estimating evapotranspiration by coupling Bayesian model averaging methods with machine learning algorithms[J]. Environmental Monitoring and Assessment, 2021, 193(3): 156. DOI: 10.1007/s10661-021-08934-1.

[50] Amani S, Shafizadeh-Moghadam H. A review of machine learning models and influential factors for estimating evapotranspiration using remote sensing and ground-based data[J]. Agricultural Water Management, 2023, 284: 108324. DOI: 10.1016/j.agwat.2023.108324.

[51] Yong S L S, Ng J L, Huang Y F, et al. Enhanced daily reference evapotranspiration estimation using optimized hybrid support vector regression models[J]. Water Resources Management, 2024, 38(11): 4213-4241. DOI: 10.1007/s11269-024-03860-6.

[52] Bellido-Jiménez J A, Estévez J, García-Marín A P. New machine learning approaches to improve reference evapotranspiration estimates using intra-daily temperature-based variables in a semi-arid region of Spain[J]. Agricultural Water Management, 2021, 245: 106558. DOI: 10.1016/j.agwat.2020.106558.

[53] 邢立文, 崔宁博, 董娟. 基于LSTM深度学习模型的华北地区参考作物蒸散量预测研究[J]. 水利水电技术, 2019, 50(4): 64-72. DOI: 10.13928/j.cnki.wrahe.2019.04.009.

[54] Acharki S, Raza A, Vishwakarma D K, et al. Comparative assessment of empirical and hybrid machine learning models for estimating daily reference evapotranspiration in sub-humid and semi-arid climates[J]. Scientific Reports, 2025, 15(1): 2542. DOI: 10.1038/s41598-024-83859-6.

[55] Granata F. Evapotranspiration evaluation models based on machine learning algorithms: A comparative study[J]. Agricultural Water Management, 2019, 217: 303-315. DOI: 10.1016/j.agwat.2019.03.015.

[56] Qian Haiyang, Wang Weiguang, Chen Gang. Assessing forecast performance of daily reference evapotranspiration: A comparison of equations, machine and deep learning using weather forecasts[J]. Journal of Hydrology, 2024, 644: 132101. DOI: 10.1016/j.jhydrol.2024.132101.

[57] Tikhamarine Y, Malik A, Kumar A, et al. Estimation of monthly reference evapotranspiration using novel hybrid machine learning approaches[J]. Hydrological Sciences Journal, 2019, 64(15): 1824-1842. DOI: 10.1080/02626667.2019.1678750.

[58] Zhao Yuxin, Dong Heng, Huang Wenbing, et al. Seamless terrestrial evapotranspiration estimation by machine learning models across the contiguous United States[J]. Ecological Indicators, 2024, 165: 112203. DOI: 10.1016/j.ecolind.2024.112203.

[59] Sun Huaiwei, Yang Yong, Wu Ruiying, et al. Improving estimation of cropland evapotranspiration by the Bayesian model averaging method with surface energy balance models[J]. Atmosphere, 2019, 10(4): 188. DOI: 10.3390/atmos10040188.

[60] Li Xiaoxiao, Sun Huaiwei, Yang Yong, et al. Different vegetation covers leading to the uncertainty and consistency of ET estimation: A case study assessment with extended triple collocation[J]. Remote Sensing, 2024, 16(13): 2484. DOI: 10.3390/rs16132484.

[61] Domínguez A, Martínez-López J A, Amami H, et al. Adaptation of a scientific decision support system to the productive sector: A case study: MOPECO irrigation scheduling model for annual crops[J]. Water, 2023, 15(9): 1691. DOI: 10.3390/w15091691.

[62] Yue Qiong, Zhang Fan, Zhang Chenlong, et al. A full fuzzy-interval credibility-constrained nonlinear programming approach for irrigation water allocation under uncertainty[J]. Agricultural Water Management, 2020, 230: 105961. DOI: 10.1016/j.agwat.2019.105961.

[63] 时荣超, 郭文忠. 农业灌溉水资源优化配置研究进展[J]. 农业工程学报, 2024, 40(4): 1-13. DOI: 10.11975/j.issn.1002-6819.202307207.

[64] 丰尔蔓, 李援农, 胡战峰, 等. 基于水量平衡下的灌区用水计划编制方法综述[J]. 排灌机械工程学报, 2022, 40(3): 294-301. DOI: 10.3969/j.issn.1674-8530.20.0012.

[65] Liu Jiamei, Chang Fangle, Wang Xiujuan, et al. Smart irrigation scheduling for crop production vs-ing a crop model and improved deep reinforcement learning[J]. Agriculture, 2025, 15(24): 2569. DOI: 10.3390/agriculture15242569.

[66] Liu Ao, Zhao Dongbo, Wei Yichang. Model predictive control of adaptive irrigation decisions incorporating rainfall intensity and soil properties[J]. Agriculture, 2025, 15(5): 527. DOI: 10.3390/agriculture15050527.

[67] Li Jiang, Song Jian, Li Mo, et al. Optimization of irrigation scheduling for spring wheat based on simulation-optimization model under uncertainty[J]. Agricultural Water Management, 2018, 208: 245-260. DOI: 10.1016/j.agwat.2018.06.029.

[68] Zhang Shenlong, Yang Gaiqiang, Wang Chaozi, et al. Linking agricultural water-food-environment nexus with crop area planning: A fuzzy credibility-based multi-objective linear fractional programming approach[J]. Agricultural Water Management, 2023, 277: 108135. DOI: 10.1016/j.agwat.2022.108135.

[69] Li Mo, Fu Qiang, Singh V P, et al. An interval multi-objective programming model for irrigation water allocation under uncertainty[J]. Agricultural Water Management, 2018, 196: 24-36. DOI: 10.1016/j.agwat.2017.10.016.

[70] Liu Xinying, Zhao Zhihua, Rezaeipanah A. Intelligent and automatic irrigation system based on internet of things using fuzzy control technology[J]. Scientific Reports, 2025, 15(1): 14577. DOI: 10.1038/s41598-025-98137-2.

[71] Jha G, Nicolas F, Schmidt R, et al. Irrigation decision support systems (idss) for California's water-nutrient-energy nexus[J]. Agronomy, 2022, 12(8): 1962. DOI: 10.3390/agronomy12081962.

[72] Abdelmoamen Ahmed A, Al Omari S, Awal R, et al. A distributed system for supporting smart irrigation using Internet of Things technology[J]. Engineering Reports, 2021, 3(7): e12352. DOI: 10.1002/eng2.12352.

[73] 朱兴宇, 粟晓玲, 胡雪雪, 等. 协同调控水-农业-生态的干旱区多水源优化配置[J]. 农业工程学报, 2024, 40(2): 239-248. DOI: 10.11975/j.issn.1002-6819.202307012.

[74] Pan Qi, Zhang Chenglong, Guo Shanshan, et al. An interval multi-objective fuzzy-interval credibility-constrained nonlinear programming model for balancing agricultural and ecological water management[J]. Journal of Contaminant Hydrology, 2022, 245: 103958. DOI: 10.1016/j.jconhyd.2022.103958.

[75] Claro A M, Fonseca A, Fraga H, et al. Future agricultural water availability in Mediterranean Countries under climate change: A systematic review[J]. Water, 2024, 16(17): 2484. DOI: 10.3390/w16172484.

[76] Yue Zhiwei, Zhuo La, Ji Xiangxiang, et al. Water-saving irrigated area expansion hardly enhances crop yield while saving water under climate scenarios in China[J]. Communications Earth & Environment, 2025, 6(1): 295. DOI: 10.1038/s43247-025-02279-0.

[77] 孙进, 鲁帆, 王康铭, 等. 气候变化对西藏自治区青稞需水影响研究[J]. 中国农村水利水电, 2024(6): 64-74. DOI: 10.12396/znsd.231606.

[78] 曹永强, 李维佳, 赵博雅. 气候变化下辽西北春玉米生育期需水量研究[J]. 资源科学, 2018, 40(1): 150-160. DOI: 10.18402/resci.2018.01.14.

[79] Ardakani M K, Rahimi H, Babaei M A. Irrigation and crop management using multi-objective optimization: A case study[J]. Asia Pacific Management Review, 2024, 29(1): 53-63. DOI: 10.1016/j.apmrv.2023.06.002.

[80] Guzinski R, Nieto H, Sandholt I, et al. Modelling high-resolution actual evapotranspiration through Sentinel-2 and Sentinel-3 data fusion[J]. Remote Sensing, 2020, 12(9): 1433. DOI: 10.3390/rs12091433.

[81] Huang Lei, Luo Yong, Chen Jingm, et al. Satellite-based near-real-time global daily terrestrial evapotranspiration estimates[J]. Earth System Science Data Discussions, 2024, 16(9): 3993-4019. DOI: 10.5194/essd-16-3993-2024.

[82] 周婕, 张珂, 杜涛. 基于卫星遥感的水库库区植被覆盖变化研究: 以三河口水库为例[J]. 水利水电技术（中英文）, 2024, 55(6): 134-147. DOI: 10.13928/j.cnki.wrahe.2024.06.011.

[83] 刘自隆. 物联网技术在现代农业节水灌溉中的应用[J]. 河北农机, 2024(24): 43-45. DOI: 10.15989/j.cnki.hbnjzzs.2024.24.042.

[84] Karniadakis G E, Kevrekidis I G, Lu Lu, et al. Physics-informed machine learning[J]. Nature Reviews Physics, 2021, 3(6): 422-440. DOI: 10.1038/s42254-021-00314-5.

[85] Read J S, Jia X, Willard J, et al. Process-guided deep learning predictions of lake water temperature[J]. Water Resources Research, 2019, 55(11): 9173-9190. DOI: 10.1029/2019WR024922.

[86] Li Xiang, Khandelwal A, Jia Xiaowei, et al. Regionalization in a global hydrologic deep learning model: From physical descriptors to random vectors[J]. Water Resources Research, 2022, 58(8): e2021WR031794. DOI: 10.1029/2021WR031794.

[87] Cai Kaixuan He Jinxin, Li Qingliang, et al. Meta-LSTM in hydrology: Advancing runoff predictions through model-agnostic meta-learning[J]. Journal of Hydrology, 2024, 639: 131521. DOI: 10.1016/j.jhydrol.2024.131521.

[88] Wang Huimin, Peng Xiao, He Xiaogang. Forecasting fierce floods with transferable AI in data-scarce regions[J]. The Innovation, 2024, 5(4): 100652. DOI: 10.1016/j.xinn.2024.100652.

[89] Zhang Qiong, Yang Xiaoling, Ding Cheng, et al. Optimization of sensor combinations for simplified estimation of reference crop evapotranspiration using machine learning and SHAP interpretation[J]. Agriculture, 2026, 16(1): 93. DOI: 10.3390/agriculture16010093.

[90] Wang Lingyue, Hu Ping, Zheng Hongwei, et al. A physics-constrained deep learning framework for actual evapotranspiration estimation using ground station observations and remote sensing data[J]. Environmental Modelling & Software, 2025, 186: 106585. DOI: 10.1016/j.envsoft.2025.106585.

[91] Castelletti A, Ficchì A, Cominola A, et al. Model predictive control of water resources systems: A review and research agenda[J]. Annual Reviews in Control, 2023, 55: 442-465. DOI: 10.1016/j.arcontrol.2023.03.013.

[92] Li Haiyan, Li Mo, Wang Yijia, et al. Precision irrigation framework could enhance water productivity and reduce carbon emissions in China[J]. Communications Earth & Environment, 2026, 7(1): 113. DOI: 10.1038/s43247-025-03137-9.

[93] El Hachimi C, Belaqziz S, Khabba S, et al. Physics-informed neural networks for enhanced reference evapotranspiration estimation in Morocco: Balancing semi-physical models and deep learning[J]. Chemosphere, 2025, 374: 144238. DOI: 10.1016/j.chemosphere.2025.144238.

[94] Darvishi E, Kordestani T. Multi-objective optimization of water scheduling in irrigation canal network using NSGA-Ⅱ[J]. Journal of Applied Research in Water and Wastewater, 2019, 6(2): 95-99. DOI: 10.22126/arww.2020.1358.

[95] Yang Cuiping, Liu Changhong, Qiu Zhiyaun, et al. Multi-objective irrigation strategies and production prediction for winter wheat in China in future dry years using CERES-wheat model and non-dominated sorting genetic algorithm Ⅱ[J]. Computers and Electronics in Agriculture, 2025, 230: 109888. DOI: 10.1016/j.compag.2024.109888.

[96] Ghorbani Bam P, Rezaei N, Roubanis A, et al. Digital twin applications in the water sector: A Review[J]. Water, 2025, 17(20): 2957. DOI: 10.3390/w17202957.

[97] Bautista C J C, Gallegos R K B, Lampayan R M. Machine learning and digital twins in smart irrigation: Optimising water use through agricultural data analytics[J]. Digital Twin, 2025: 2562418. DOI: 10.1080/27525783.2025.2562418.

[98] Subeesh A, Chauhan N. Agricultural digital twin for smart farming: A review[J]. Green Technologies and Sustainability, 2025: 100299. DOI: 10.1016/j.grets.2025.100299.

[99] 刘丽, 张玉翠, 张小帅, 等. 北方缺水地区农业智慧灌溉应用与发展[J]. 农业工程, 2023, 13(10): 78-83. DOI: 10.19998/j.cnki.2095-1795.2023.10.014.

[100] 向宇轩, 赵鑫, 牛攀, 等. 智慧灌溉技术推广效应评价体系研究与实证[J]. 节水灌溉, 2024(12): 72-79. DOI: 10.12396/jsgg.2024284.

[101] Alves R G, Maia R F, Lima F. Development of a digital twin for smart farming: Irrigation management system for water saving[J]. Journal of Cleaner Production, 2023, 388: 135920. DOI: 10.1016/j.jclepro.2023.135920.

[102] Ahsen R, Di Bitonto P, Novielli P, et al. Harnessing digital twins for sustainable agricultural water management: A systematic review[J]. Applied Sciences, 2025, 15(8): 4228. DOI: 10.3390/app15084228.

基金资助

国家自然科学基金项目(52469013)

宁夏重点研发计划项目(2023BCF01017)