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However, physical models like the SEB- and LUE-based ones can be complex and demand extensive input data. In our study, we leveraged multiple variables (vegetation growth, surface moisture, radiative energy, and other relative variables) as inputs for various regression algorithms, including Multiple Linear Regression (MLR), Random Forest Regression (RFR), and Backpropagation Neural Network (BPNN), to estimate water (ET) and carbon fluxes (NEE) in the Haihe River Basin, and compared the estimated results with the observations from six eddy covariance flux towers. We aimed to (1) assess the impacts of different input variables on the accuracy of ET and NEE estimations, (2) compare the accuracy of the three regression methods, including three machine learning algorithms and Multiple Linear Regression, and (3) evaluate the performance of ET and NEE estimation models across various regions. The key findings include: (1) Increasing the number of input variables typically improved the accuracy of ET and NEE estimations. (2) RFR proved to be the most accurate for both ET and NEE estimations among the three regression algorithms. Of these, the four types of variables used together with RFR resulted in the best accuracy for ET (R2 of 0.81 and an RMSE of 1.13 mm) and NEE (R2 of 0.83 and an RMSE of 2.83 gC\/m2) estimations. (3) Vegetation growth variables (i.e., VIs) are the most important inputs for ET and NEE estimation. (4) The proposed ET and NEE estimation models exhibited some variation in accuracy across different validation sites. Despite these variations, the accuracy levels across all six validation sites remained relatively high. Overall, this study lays the groundwork for an efficient approach to agricultural water resources and ecosystem monitoring and management.<\/jats:p>","DOI":"10.3390\/rs16173280","type":"journal-article","created":{"date-parts":[[2024,9,4]],"date-time":"2024-09-04T02:25:49Z","timestamp":1725416749000},"page":"3280","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Combination of Multiple Variables and Machine Learning for Regional Cropland Water and Carbon Fluxes Estimation: A Case Study in the Haihe River Basin"],"prefix":"10.3390","volume":"16","author":[{"given":"Minghan","family":"Cheng","sequence":"first","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou 225009, China"},{"name":"Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China"}]},{"given":"Kaihua","family":"Liu","sequence":"additional","affiliation":[{"name":"College of Agricultural Science and Engineering, Hohai University, Nanjing 210048, China"}]},{"given":"Zhangxin","family":"Liu","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou 225009, China"},{"name":"Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China"}]},{"given":"Junzeng","family":"Xu","sequence":"additional","affiliation":[{"name":"College of Agricultural Science and Engineering, Hohai University, Nanjing 210048, China"}]},{"given":"Zhengxian","family":"Zhang","sequence":"additional","affiliation":[{"name":"Co-Innovation Center of Sustainable Forestry in Southern China, Jiangsu Provincial Key Lab of Soil Erosion and Ecological Restoration, Nanjing Forestry University, Nanjing 210037, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0873-3922","authenticated-orcid":false,"given":"Chengming","family":"Sun","sequence":"additional","affiliation":[{"name":"Jiangsu Key Laboratory of Crop Genetics and Physiology\/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou 225009, China"},{"name":"Jiangsu Co-Innovation Center for Modern Production Technology of Grain Crops, Yangzhou University, Yangzhou 225009, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,9,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.rse.2019.03.040","article-title":"High spatial resolution monitoring land surface energy, water and CO2 fluxes from an Unmanned Aerial System","volume":"229","author":"Wang","year":"2019","journal-title":"Remote Sens. 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