乐胖代购免代理版

{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,8,17]],"date-time":"2024-08-17T00:22:59Z","timestamp":1723854179959},"reference-count":84,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T00:00:00Z","timestamp":1723766400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"open bidding for selecting the best candidates of Fuzhou City","award":["2022JDA07"]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41501575","42330707","42171338"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Project of Chongqing Science and Technology Bureau","award":["cstc2021jcyj-msxmX0384"]},{"name":"Sichuan Science and Technology Program","award":["2023NSFSC1916"]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"Soil organic carbon (SOC) plays a vital role in the global carbon cycle and soil quality assessment. The Qinghai\u2013Tibet Plateau is one of the largest plateaus in the world. Therefore, in this region, SOC density and the spatial distribution of SOC are highly sensitive to climate change and human intervention. Given the insufficient understanding of the spatial distribution of SOC density in the Qinghai\u2013Tibet Plateau, this study utilized machine learning (ML) algorithms to estimate the density and distribution pattern of SOC density in the region. In this study, we first collected multisource data, such as optical remote sensing data, synthetic aperture radar) (SAR) data, and other environmental variables, including socioeconomic factors, topographic factors, climate factors, and soil properties. Then, we used ML algorithms, namely random forest (RF), extreme gradient boosting (XGBoost), and light gradient boosting machine (LightGBM), to estimate the topsoil SOC density and spatial distribution patterns of SOC density. We also aimed to investigate any driving factors. The results are as follows: (1) The average SOC density is 5.30 kg\/m2. (2) Among the three ML algorithms used, LightGBM showed the highest validation accuracy (R2 = 0.7537, RMSE = 2.4928 kgC\/m2, MAE = 1.7195). (3) The normalized difference vegetation index (NDVI), valley depth (VD), and temperature are crucial in predicting the spatial distribution of topsoil SOC density. Feature importance analyses conducted using the three ML models all showed these factors to be among the top three in importance, with contribution rates of 14.08%, 12.29%, and 14.06%; 17.32%, 20.73%, and 24.62%; and 16.72%, 11.96%, and 20.03%. (4) Spatially, the southeastern part of the Qinghai\u2013Tibet Plateau has the highest topsoil SOC density, with recorded values ranging from 8.41 kg\/m2 to 13.2 kg\/m2, while the northwestern part has the lowest density, with recorded values ranging from 0.85 kg\/m2 to 2.88 kg\/m2. Different land cover types showed varying SOC density values, with forests and grasslands having higher SOC densities compared to urban and bare land areas. The findings of this study provide a scientific basis for future soil resource management and improved carbon sequestration accounting in the Qinghai\u2013Tibet Plateau.<\/jats:p>","DOI":"10.3390\/rs16163006","type":"journal-article","created":{"date-parts":[[2024,8,16]],"date-time":"2024-08-16T08:29:57Z","timestamp":1723796997000},"page":"3006","source":"Crossref","is-referenced-by-count":0,"title":["Estimation of Soil Organic Carbon Density on the Qinghai\u2013Tibet Plateau Using a Machine Learning Model Driven by Multisource Remote Sensing"],"prefix":"10.3390","volume":"16","author":[{"given":"Qi","family":"Chen","sequence":"first","affiliation":[{"name":"College of Resources and Environment, Shandong Agricultural University, Taian 271018, China"}]},{"given":"Wei","family":"Zhou","sequence":"additional","affiliation":[{"name":"Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China"},{"name":"Key Laboratory of Agricultural Remote Sensing, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0001-6199-9733","authenticated-orcid":false,"given":"Wenjiao","family":"Shi","sequence":"additional","affiliation":[{"name":"Key Laboratory of Agricultural Remote Sensing, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,8,16]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"107654","DOI":"10.1016\/j.agrformet.2019.107654","article-title":"Soil organic carbon in Italian forests and agroecosystems: Estimating current stock and future changes with a spatial modelling approach","volume":"278","author":"Caddeo","year":"2019","journal-title":"Agric. 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