{"title":"Prediction and mapping of soil organic carbon in the Bosten Lake oasis based on Sentinel-2 data and environmental variables","authors":"Shaotian Li , Xinguo Li , Xiangyu Ge","doi":"10.1016/j.iswcr.2024.12.002","DOIUrl":null,"url":null,"abstract":"<div><div>Soil is the largest carbon pool on the Earth's surface. With the application of remote sensing technology, Soil Organic Carbon (SOC) estimation has become a hot topic in digital soil mapping. However, the heterogeneity of geomorphology can affect the performance of remote sensing in determining soil organic carbon. In the Bosten Lake Watershed in northwestern China, we collected 116 soil samples from farm land, uncultivated land, and woodland. To establish an SOC prediction model, we produced 16 optical remote sensing variables and 9 environmental covariates. Three types of land use were studied: farm land, uncultivated land, and woodland. Five machine learning models were used for these land use types: gradient Tree (ET), Support Vector Machine (SVM), Random Forest (RF), Adaptive gradient Boosting (AdaBoost), and extreme Gradient Boosting (XGBoost). The main driving variables for changes in organic carbon content across the entire sample area were Enhanced Vegetation Index (EVI), Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI); for farm land, it was Clay Index (CI2); for farm land and woodland, it was Color Index (CI). The results showed that in terms of prediction accuracy, RF and XGBoost outperformed SVM. In terms of simulation precision, the ET model's woodland model (R<sup>2</sup> = 0.86, RMSE = 7.72), the ET model's farm land model (R<sup>2</sup> = 0.82, RMSE = 6.66), and the uncultivated land model of the RF model (R<sup>2</sup> = 0.81, RMSE = 1.09) performed best. Compared to global modeling, establishing SOC estimation models based on different land use types yielded more ideal results in this study. These findings provide new insights into high-precision estimation of organic carbon content.</div></div>","PeriodicalId":48622,"journal":{"name":"International Soil and Water Conservation Research","volume":"13 2","pages":"Pages 436-446"},"PeriodicalIF":7.3000,"publicationDate":"2024-12-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Soil and Water Conservation Research","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2095633924000911","RegionNum":1,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENVIRONMENTAL SCIENCES","Score":null,"Total":0}
引用次数: 0
Abstract
Soil is the largest carbon pool on the Earth's surface. With the application of remote sensing technology, Soil Organic Carbon (SOC) estimation has become a hot topic in digital soil mapping. However, the heterogeneity of geomorphology can affect the performance of remote sensing in determining soil organic carbon. In the Bosten Lake Watershed in northwestern China, we collected 116 soil samples from farm land, uncultivated land, and woodland. To establish an SOC prediction model, we produced 16 optical remote sensing variables and 9 environmental covariates. Three types of land use were studied: farm land, uncultivated land, and woodland. Five machine learning models were used for these land use types: gradient Tree (ET), Support Vector Machine (SVM), Random Forest (RF), Adaptive gradient Boosting (AdaBoost), and extreme Gradient Boosting (XGBoost). The main driving variables for changes in organic carbon content across the entire sample area were Enhanced Vegetation Index (EVI), Enhanced Vegetation Index 2 (EVI2), Soil-Adjusted Vegetation Index (SAVI); for farm land, it was Clay Index (CI2); for farm land and woodland, it was Color Index (CI). The results showed that in terms of prediction accuracy, RF and XGBoost outperformed SVM. In terms of simulation precision, the ET model's woodland model (R2 = 0.86, RMSE = 7.72), the ET model's farm land model (R2 = 0.82, RMSE = 6.66), and the uncultivated land model of the RF model (R2 = 0.81, RMSE = 1.09) performed best. Compared to global modeling, establishing SOC estimation models based on different land use types yielded more ideal results in this study. These findings provide new insights into high-precision estimation of organic carbon content.
期刊介绍:
The International Soil and Water Conservation Research (ISWCR), the official journal of World Association of Soil and Water Conservation (WASWAC) http://www.waswac.org, is a multidisciplinary journal of soil and water conservation research, practice, policy, and perspectives. It aims to disseminate new knowledge and promote the practice of soil and water conservation.
The scope of International Soil and Water Conservation Research includes research, strategies, and technologies for prediction, prevention, and protection of soil and water resources. It deals with identification, characterization, and modeling; dynamic monitoring and evaluation; assessment and management of conservation practice and creation and implementation of quality standards.
Examples of appropriate topical areas include (but are not limited to):
• Conservation models, tools, and technologies
• Conservation agricultural
• Soil health resources, indicators, assessment, and management
• Land degradation
• Sustainable development
• Soil erosion and its control
• Soil erosion processes
• Water resources assessment and management
• Watershed management
• Soil erosion models
• Literature review on topics related soil and water conservation research