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In this study, we integrated Sentinel-2 multispectral imagery (MSI) with computational hyperspectral features (CHSFs) and developed a novel symbolic regression algorithm based on deep reinforcement learning and genetic programming (DRL-GP) to estimate forage P content in alpine grasslands. Using 243 field observations collected during the regreening, grass-bearing, and yellowing periods in 2023 from the Shaliu River Basin, we generated 10 CHSF images (CHSFIs) with varying spectral dispersions (1\u201310 nm). Our results demonstrated the following: (1) The DRL-GP-based symbolic regression model identified the optimal CHSF and spectral dispersion for each growing season, significantly enhancing estimation accuracy. (2) Forage P content estimations using the combined CHSF and DRL-GP-based symbolic regression algorithm significantly outperformed traditional methods. Compared to original spectral features, the R2 improved by 99.5%, 57.4%, and 86.2% during the regreening, grass-bearing, and yellowing periods, with corresponding MSE reductions of 84.8%, 41.5%, and 75.8% and MAE decreases of 70.7%, 57.5%, and 50.4%. Across these growing seasons, the R2 increased by 322.2%, 68.2%, and 639.8% compared to MLR, 128.9%, 97.4%, and 469.2% compared to RF, and 485.1%, 65.3%, and 231.3% compared to DNN. The MSE decreased by 31%, 82.9%, and 52.4% compared to MLR, 39.9%, 42.4%, and 31.4% compared to RF, and 84.5%, 73.4%, and 81.9% compared to DNN. The MAE decreased by 32.6%, 67%, and 44.2% compared to MLR, 42.6%, 47.6%, and 37.9% compared to RF, and 60.2%, 50%, and 56.3% compared to DNN. (3) Proximity to the water system notably influenced forage P variation, with the highest increases observed within 1\u20132 km of water sources. These findings provide critical insights for optimizing grassland management and improving livestock productivity.<\/jats:p>","DOI":"10.3390\/rs16214086","type":"journal-article","created":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T09:49:14Z","timestamp":1730454554000},"page":"4086","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Mapping Seasonal Spatiotemporal Dynamics of Alpine Grassland Forage Phosphorus Using Sentinel-2 MSI and a DRL-GP-Based Symbolic Regression Algorithm"],"prefix":"10.3390","volume":"16","author":[{"given":"Jiancong","family":"Shi","sequence":"first","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Space Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Center for Geographic Environment Research and Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Aiwu","family":"Zhang","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Space Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Center for Geographic Environment Research and Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Juan","family":"Wang","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Space Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Center for Geographic Environment Research and Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Xinwang","family":"Gao","sequence":"additional","affiliation":[{"name":"Key Laboratory of 3D Information Acquisition and Application, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Engineering Research Center of Space Information Technology, Ministry of Education, Capital Normal University, Beijing 100048, China"},{"name":"Center for Geographic Environment Research and Education, Capital Normal University, Beijing 100048, China"}]},{"given":"Shaoxing","family":"Hu","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering & Automation, Beihang University, Beijing 100191, China"}]},{"given":"Shatuo","family":"Chai","sequence":"additional","affiliation":[{"name":"Academy of Animal and Veterinary Sciences, Qinghai University, Xining 810016, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"113262","DOI":"10.1016\/j.rse.2022.113262","article-title":"Predicting plant biomass and species richness in temperate grasslands across regions, time, and land management with remote sensing and deep learning","volume":"282","author":"Muro","year":"2022","journal-title":"Remote Sens. 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