{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,6,27]],"date-time":"2024-06-27T05:55:32Z","timestamp":1719467732465},"reference-count":65,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2023,4,11]],"date-time":"2023-04-11T00:00:00Z","timestamp":1681171200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities","doi-asserted-by":"publisher","award":["BLX202165"],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["42001107"],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"To overcome spatial, spectral and temporal constraints of different remote sensing products, data fusion is a good technique to improve the prediction capability of soil prediction models. However, few studies have analyzed the effects of image fusion on digital soil mapping (DSM) models. This research fused multispectral (MS) and panchromatic Landsat 8 (L8) bands, and MS Sentinel 2 (S2) and panchromatic L8 bands using the Brovey, Intensity\u2013Hue\u2013Saturation and Gram\u2013Schmidt methods in an agricultural area in Yellow River Basin, China. To analyze the effects of image fusion on DSM models, various SOC prediction models derived from remote sensing image datasets were established by the random forest method. Soil salinity indices and spectral reflectance from all the remote sensing data had relatively strong negative correlations with SOC, and vegetation indices and water indices from all the remote sensing data had relatively strong positive correlations with SOC. Soil moisture and vegetation were the main controlling factors of the SOC spatial pattern in the study area. More spectral indices derived from pansharpened L8 and fused S2\u2013L8 images by all three image fusion methods had stronger relationships with SOC compared with those from MS L8 and MS S2, respectively. All the SOC models established by pansharpened L8 and fused S2\u2013L8 images had higher prediction accuracy than those established by MS L8 and MS S2, respectively. The fusion between S2 and L8 bands had stronger effects on enhancing the prediction accuracy of SOC models compared with the fusion between panchromatic and MS L8 bands. It is concluded that digital soil mapping and image fusion can be utilized to increase the prediction performance of SOC spatial prediction models.<\/jats:p>","DOI":"10.3390\/rs15082017","type":"journal-article","created":{"date-parts":[[2023,4,12]],"date-time":"2023-04-12T05:35:08Z","timestamp":1681277708000},"page":"2017","source":"Crossref","is-referenced-by-count":3,"title":["Incorporation of Fused Remote Sensing Imagery to Enhance Soil Organic Carbon Spatial Prediction in an Agricultural Area in Yellow River Basin, China"],"prefix":"10.3390","volume":"15","author":[{"given":"Yiming","family":"Xu","sequence":"first","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-3258-0666","authenticated-orcid":false,"given":"Youquan","family":"Tan","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"ORCID":"http:\/\/orcid.org\/0000-0002-6182-4017","authenticated-orcid":false,"given":"Amr","family":"Abd-Elrahman","sequence":"additional","affiliation":[{"name":"School of Forest Resources and Conservation\u2014Geomatics Program, University of Florida, 301 Reed Lab, Gainesville, FL 32611, USA"},{"name":"Gulf Coast REC\/School of Forest Resources and Conservation\u2014Geomatics Program, University of Florida, 1200 N. Park Road, Plant City, FL 33563, USA"}]},{"given":"Tengfei","family":"Fan","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]},{"given":"Qingpu","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Grassland Science, Beijing Forestry University, Beijing 100083, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,4,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"125736","DOI":"10.1016\/j.jclepro.2020.125736","article-title":"Soil Organic Carbon Sequestration Rates in Vineyard Agroecosystems under Different Soil Management Practices: A Meta-Analysis","volume":"290","author":"Payen","year":"2021","journal-title":"J. Clean. 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