{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,8]],"date-time":"2026-04-08T04:51:48Z","timestamp":1775623908870,"version":"3.50.1"},"reference-count":71,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T00:00:00Z","timestamp":1724716800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Key R&amp;D Program of China","award":["2022YFD190050403"],"award-info":[{"award-number":["2022YFD190050403"]}]},{"name":"the National Key R&amp;D Program of China","award":["52179049"],"award-info":[{"award-number":["52179049"]}]},{"name":"the National Key R&amp;D Program of China","award":["xbzg-zdsys-202103"],"award-info":[{"award-number":["xbzg-zdsys-202103"]}]},{"name":"the National Natural Science Foundation of China","award":["2022YFD190050403"],"award-info":[{"award-number":["2022YFD190050403"]}]},{"name":"the National Natural Science Foundation of China","award":["52179049"],"award-info":[{"award-number":["52179049"]}]},{"name":"the National Natural Science Foundation of China","award":["xbzg-zdsys-202103"],"award-info":[{"award-number":["xbzg-zdsys-202103"]}]},{"name":"the Western Light-Key Laboratory Cooperative Research Cross-Team Project of Chinese Academy of Sciences","award":["2022YFD190050403"],"award-info":[{"award-number":["2022YFD190050403"]}]},{"name":"the Western Light-Key Laboratory Cooperative Research Cross-Team Project of Chinese Academy of Sciences","award":["52179049"],"award-info":[{"award-number":["52179049"]}]},{"name":"the Western Light-Key Laboratory Cooperative Research Cross-Team Project of Chinese Academy of Sciences","award":["xbzg-zdsys-202103"],"award-info":[{"award-number":["xbzg-zdsys-202103"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Accurate estimation of soil moisture content (SMC) in the field is a critical aspect of precise irrigation management. The development of unmanned aerial vehicle (UAV) platforms has provided an economically efficient means for field-scale SMC measurements. However, previous studies have mostly focused on single-sensor estimates of SMC. Additionally, the lack of differentiation between various crops and their growth stages has resulted in an unclear understanding of how crop types and growth stages affect the accuracy of SMC estimation at different soil depths. Therefore, the purpose of this paper was to use UAV multimodal remote sensing and a machine learning algorithm to estimate the SMC in agricultural fields and investigate estimation\u2019s effectiveness under different scenarios. The results indicated the following: (1) The multispectral remote sensing method provided higher accuracy in SMC estimation compared to thermal infrared remote sensing. Moreover, the integration of multimodal data improved the accuracy of SMC estimation, enhancing the coefficient of determination (R2) by approximately 14% over that achieved through the use of multispectral data alone and 39% over that of thermal infrared data alone. (2) Across the entire growth period, the optimal soil depths of SMC estimation for soybean were 10 cm and 20 cm (average R2 were 0.81 and 0.82, respectively), while for corn, they were 10 cm, 20 cm, and 40 cm (average R2 were 0.59, 0.60, and 0.55, respectively). (3) The SMC estimation model performed better for both crops during the first three growth stages, with accuracy declining in the maturity stage. These results demonstrate that this approach can provide relatively accurate root zone SMC estimates for different crops throughout their main growth periods. Thus, it can be employed for SMC monitoring and precision irrigation system design.<\/jats:p>","DOI":"10.3390\/rs16173166","type":"journal-article","created":{"date-parts":[[2024,8,27]],"date-time":"2024-08-27T09:26:51Z","timestamp":1724750811000},"page":"3166","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Study on Soil Moisture Status of Soybean and Corn across the Whole Growth Period Based on UAV Multimodal Remote Sensing"],"prefix":"10.3390","volume":"16","author":[{"given":"Yaling","family":"Zhang","sequence":"first","affiliation":[{"name":"Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China"},{"name":"National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture in Wuwei of Gansu Province, Wuwei 733000, China"}]},{"given":"Xueyi","family":"Yang","sequence":"additional","affiliation":[{"name":"Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China"},{"name":"National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture in Wuwei of Gansu Province, Wuwei 733000, China"}]},{"given":"Fei","family":"Tian","sequence":"additional","affiliation":[{"name":"Center for Agricultural Water Research in China, China Agricultural University, Beijing 100083, China"},{"name":"National Field Scientific Observation and Research Station on Efficient Water Use of Oasis Agriculture in Wuwei of Gansu Province, Wuwei 733000, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,27]]},"reference":[{"key":"ref_1","first-page":"116","article-title":"Situation and problem analysis of water resource security in China","volume":"18","author":"Zhang","year":"2009","journal-title":"Resour. 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