{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:32:28Z","timestamp":1760146348742,"version":"build-2065373602"},"reference-count":62,"publisher":"MDPI AG","issue":"20","license":[{"start":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T00:00:00Z","timestamp":1729468800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52309058","U2243235","2023M732702"],"award-info":[{"award-number":["52309058","U2243235","2023M732702"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100002858","name":"China Postdoctoral Science Foundation","doi-asserted-by":"publisher","award":["52309058","U2243235","2023M732702"],"award-info":[{"award-number":["52309058","U2243235","2023M732702"]}],"id":[{"id":"10.13039\/501100002858","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate estimation of gross primary production (GPP) of paddy rice fields is essential for understanding cropland carbon cycles, yet remains challenging due to spatial heterogeneity. In this study, we integrated high-resolution unmanned aerial vehicle (UAV) imagery into a leaf biochemical properties-based model for improving GPP estimation. The key parameter, maximum carboxylation rate at the top of the canopy (Vcmax,025), was quantified using various spatial information representation methods, including mean (\u03bcref) and standard deviation (\u03c3ref) of reflectance, gray-level co-occurrence matrix (GLCM)-based features, local binary pattern histogram (LBPH), and convolutional neural networks (CNNs). Our models were evaluated using a two-year eddy covariance (EC) system and UAV measurements. The result shows that incorporating spatial information can vastly improve the accuracy of Vcmax,025 and GPP estimation. CNN methods achieved the best Vcmax,025 estimation, with an R of 0.94, an RMSE of 19.44 \u03bcmol m\u22122 s\u22121, and an MdAPE of 11%, and further produced highly accurate GPP estimates, with an R of 0.92, an RMSE of 6.5 \u03bcmol m\u22122 s\u22121, and an MdAPE of 23%. The \u03bcref-GLCM texture features and \u03bcref-LBPH joint-driven models also gave promising results. However, \u03c3ref contributed less to Vcmax,025 estimation. The Shapley value analysis revealed that the contribution of input features varied considerably across different models. The CNN model focused on nir and red-edge bands and paid much attention to the subregion with high spatial heterogeneity. The \u03bcref-LBPH joint-driven model mainly prioritized reflectance information. The \u03bcref-GLCM-based features joint-driven model emphasized the role of GLCM texture indices. As the first study to leverage the spatial information from high-resolution UAV imagery for GPP estimation, our work underscores the critical role of spatial information and provides new insight into monitoring the carbon cycle.<\/jats:p>","DOI":"10.3390\/rs16203906","type":"journal-article","created":{"date-parts":[[2024,10,21]],"date-time":"2024-10-21T09:58:24Z","timestamp":1729504704000},"page":"3906","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Accurate Estimation of Gross Primary Production of Paddy Rice Cropland with UAV Imagery-Driven Leaf Biochemical Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-2015-4946","authenticated-orcid":false,"given":"Xiaolong","family":"Hu","sequence":"first","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0446-0488","authenticated-orcid":false,"given":"Liangsheng","family":"Shi","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Lin","family":"Lin","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenji","family":"Li","sequence":"additional","affiliation":[{"name":"Urban Operation Management Center of Hengsha Township, Shanghai 201914, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xianzhi","family":"Deng","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jinmin","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jiang","family":"Bian","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-4388-4961","authenticated-orcid":false,"given":"Chenye","family":"Su","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuai","family":"Du","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tinghan","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6799-1726","authenticated-orcid":false,"given":"Yujie","family":"Wang","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan 430072, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhitao","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas, Ministry of Education, Northwest A&F University, Yangling 712100, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2024,10,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"nwab150","DOI":"10.1093\/nsr\/nwab150","article-title":"Worldwide impacts of atmospheric vapor pressure deficit on the interannual variability of terrestrial carbon sink","volume":"9","author":"He","year":"2022","journal-title":"Natl. 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