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China","award":["422QN349"],"award-info":[{"award-number":["422QN349"]}]},{"name":"National Key R&amp;D Program of China","award":["ZDYF2021GXJS038"],"award-info":[{"award-number":["ZDYF2021GXJS038"]}]},{"name":"National Key R&amp;D Program of China","award":["2019RC363"],"award-info":[{"award-number":["2019RC363"]}]},{"name":"National Natural Science Foundation of China","award":["2021YFB3900501"],"award-info":[{"award-number":["2021YFB3900501"]}]},{"name":"National Natural Science Foundation of China","award":["42201365"],"award-info":[{"award-number":["42201365"]}]},{"name":"National Natural Science Foundation of China","award":["42071330"],"award-info":[{"award-number":["42071330"]}]},{"name":"National Natural Science Foundation of China","award":["XDA28100500"],"award-info":[{"award-number":["XDA28100500"]}]},{"name":"National Natural Science Foundation of China","award":["422QN349"],"award-info":[{"award-number":["422QN349"]}]},{"name":"National Natural Science Foundation of 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Fund","award":["ZDYF2021GXJS038"],"award-info":[{"award-number":["ZDYF2021GXJS038"]}]},{"name":"Hainan Province Science and Technology Special Fund","award":["2019RC363"],"award-info":[{"award-number":["2019RC363"]}]},{"name":"Hainan Provincial High-Level Talent Program of Basic and Applied Basic Research Plan in 2019 of China","award":["2021YFB3900501"],"award-info":[{"award-number":["2021YFB3900501"]}]},{"name":"Hainan Provincial High-Level Talent Program of Basic and Applied Basic Research Plan in 2019 of China","award":["42201365"],"award-info":[{"award-number":["42201365"]}]},{"name":"Hainan Provincial High-Level Talent Program of Basic and Applied Basic Research Plan in 2019 of China","award":["42071330"],"award-info":[{"award-number":["42071330"]}]},{"name":"Hainan Provincial High-Level Talent Program of Basic and Applied Basic Research Plan in 2019 of China","award":["XDA28100500"],"award-info":[{"award-number":["XDA28100500"]}]},{"name":"Hainan Provincial High-Level Talent Program of Basic and Applied Basic Research Plan in 2019 of China","award":["422QN349"],"award-info":[{"award-number":["422QN349"]}]},{"name":"Hainan Provincial High-Level Talent Program of Basic and Applied Basic Research Plan in 2019 of China","award":["ZDYF2021GXJS038"],"award-info":[{"award-number":["ZDYF2021GXJS038"]}]},{"name":"Hainan Provincial High-Level Talent Program of Basic and Applied Basic Research Plan in 2019 of China","award":["2019RC363"],"award-info":[{"award-number":["2019RC363"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate estimation of the leaf or canopy chlorophyll content is crucial for monitoring crop growth conditions. Remote sensing monitoring of crop chlorophyll is a non-destructive, large-area, and real-time method that requires reliable retrieval models and satellite data. High-resolution satellite imagery generally has better object recognition capabilities. However, the influence of the spectral and spatial resolution of medium- and high-spatial-resolution satellite imagery on chlorophyll retrieval is currently unexplored, especially in conjunction with radiative transfer models (RTMs). This has important implications for the accurate quantification of crop chlorophyll over large areas. Therefore, the objectives of this study were to establish an RTM for the retrieval of maize chlorophyll and to compare the chlorophyll retrieval capability of the model using medium- and high-spatial-resolution satellite images. We constructed a hybrid model consisting of the PROSAIL model and the Gaussian process regression (GPR) algorithm to retrieve maize leaf and canopy chlorophyll contents (LCC and CCC). In addition, an active learning (AL) strategy was incorporated into the hybrid model to enhance the model\u2019s accuracy and efficiency. Sentinel-2 imagery with a spatial resolution of 10 m and 3 m-resolution Planet imagery were utilized for the LCC and CCC retrieval, respectively, using the hybrid model. The accuracy of the model was verified using field-measured maize chlorophyll data obtained in Dajianchang Town, Wuqing District, Tianjin City, in 2018. The results showed that the AL strategy increased the accuracy of the chlorophyll retrieval. The hybrid model for LCC retrieval with 10-band Sentinel-2 without AL had an R2 of 0.567 and an RMSE of 5.598, and the model with AL had an R2 of 0.743 and an RMSE of 3.964. Incorporating the AL strategy improved the model performance (R2 = 0.743 and RMSE = 3.964). The Planet imagery provided better results for chlorophyll retrieval than 4-band Sentinel-2 imagery but worse performance than 10-band Sentinel-2 imagery. Additionally, we tested the model using maize chlorophyll data obtained from Youyi Farm in Heilongjiang Province in 2021 to evaluate the model\u2019s robustness and scalability. The test results showed that the hybrid model used with 10-band Sentinel-2 images achieved good accuracy in the Youyi Farm area (LCC: R2 = 0.792, RMSE = 2.8; CCC: R2 = 0.726, RMSE = 0.152). The optimal hybrid model was applied to images from distinct periods to map the spatiotemporal distribution of the chlorophyll content. The uncertainties in the chlorophyll content retrieval results from different periods were relatively low, demonstrating that the model had good temporal scalability. Our research results can provide support for the precise management of maize growth.<\/jats:p>","DOI":"10.3390\/rs15071784","type":"journal-article","created":{"date-parts":[[2023,3,27]],"date-time":"2023-03-27T06:46:19Z","timestamp":1679899579000},"page":"1784","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":14,"title":["Evaluation of Hybrid Models for Maize Chlorophyll Retrieval Using Medium- and High-Spatial-Resolution Satellite Images"],"prefix":"10.3390","volume":"15","author":[{"given":"Anting","family":"Guo","sequence":"first","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation of Hainan, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huichun","family":"Ye","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation of Hainan, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guoqing","family":"Li","sequence":"additional","affiliation":[{"name":"National Earth Observation Data Center, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bing","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1710-8301","authenticated-orcid":false,"given":"Wenjiang","family":"Huang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"Key Laboratory of Earth Observation of Hainan, Aerospace Information Research Institute, Chinese Academy of Sciences, Sanya 572029, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0129-9513","authenticated-orcid":false,"given":"Quanjun","family":"Jiao","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Binxiang","family":"Qian","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"},{"name":"University of Chinese Academy of Sciences, Beijing 100049, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1460-3366","authenticated-orcid":false,"given":"Peilei","family":"Luo","sequence":"additional","affiliation":[{"name":"Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China"},{"name":"International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"106775","DOI":"10.1016\/j.compag.2022.106775","article-title":"UAV-based chlorophyll content estimation by evaluating vegetation index responses under different crop coverages","volume":"196","author":"Qiao","year":"2022","journal-title":"Comput. 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