Shurui Lin , Qing Zhu , Beini Yin , Guishan Yang , Kaihua Liao , Xiaoming Lai , Changqiang Guo
{"title":"基于土壤深度函数和校正方法的长三角三维土壤有机碳密度数据集","authors":"Shurui Lin , Qing Zhu , Beini Yin , Guishan Yang , Kaihua Liao , Xiaoming Lai , Changqiang Guo","doi":"10.1016/j.envsoft.2025.106582","DOIUrl":null,"url":null,"abstract":"<div><div>Accurate, high resolution, and depth continuous soil organic carbon density (SOCD) dataset is crucial for various research and management practices. Based on 593 soil samples, we tested different soil depth functions and correction methods for generating the 0–1.0 m three-dimensional SOCD dataset with the spatial resolution of 90-m in the Yangtze River Delta region. Depth functions (power function, exponential decay function, logarithmic function and inverse function) were fitted for different samples in the training set, and their obtained parameters were mapped by random forest based on ancillary variables. Then three correction methods, including coefficient scaling, data fusion and residual correction, were applied in the validation set to correct the predictions of depth functions. After correcting, the prediction accuracies have been significantly improved at all depths. Our dataset can generate accurate SOCD maps at any specific depth interval by constructing the vertical continuous distribution of the corrected coefficients.</div></div>","PeriodicalId":310,"journal":{"name":"Environmental Modelling & Software","volume":"192 ","pages":"Article 106582"},"PeriodicalIF":4.8000,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Generating three-dimensional soil organic carbon density dataset by soil depth function and correction methods in Yangtze River Delta, China\",\"authors\":\"Shurui Lin , Qing Zhu , Beini Yin , Guishan Yang , Kaihua Liao , Xiaoming Lai , Changqiang Guo\",\"doi\":\"10.1016/j.envsoft.2025.106582\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>Accurate, high resolution, and depth continuous soil organic carbon density (SOCD) dataset is crucial for various research and management practices. Based on 593 soil samples, we tested different soil depth functions and correction methods for generating the 0–1.0 m three-dimensional SOCD dataset with the spatial resolution of 90-m in the Yangtze River Delta region. Depth functions (power function, exponential decay function, logarithmic function and inverse function) were fitted for different samples in the training set, and their obtained parameters were mapped by random forest based on ancillary variables. Then three correction methods, including coefficient scaling, data fusion and residual correction, were applied in the validation set to correct the predictions of depth functions. After correcting, the prediction accuracies have been significantly improved at all depths. Our dataset can generate accurate SOCD maps at any specific depth interval by constructing the vertical continuous distribution of the corrected coefficients.</div></div>\",\"PeriodicalId\":310,\"journal\":{\"name\":\"Environmental Modelling & Software\",\"volume\":\"192 \",\"pages\":\"Article 106582\"},\"PeriodicalIF\":4.8000,\"publicationDate\":\"2025-06-17\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Environmental Modelling & Software\",\"FirstCategoryId\":\"93\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S136481522500266X\",\"RegionNum\":2,\"RegionCategory\":\"环境科学与生态学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental Modelling & Software","FirstCategoryId":"93","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S136481522500266X","RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Generating three-dimensional soil organic carbon density dataset by soil depth function and correction methods in Yangtze River Delta, China
Accurate, high resolution, and depth continuous soil organic carbon density (SOCD) dataset is crucial for various research and management practices. Based on 593 soil samples, we tested different soil depth functions and correction methods for generating the 0–1.0 m three-dimensional SOCD dataset with the spatial resolution of 90-m in the Yangtze River Delta region. Depth functions (power function, exponential decay function, logarithmic function and inverse function) were fitted for different samples in the training set, and their obtained parameters were mapped by random forest based on ancillary variables. Then three correction methods, including coefficient scaling, data fusion and residual correction, were applied in the validation set to correct the predictions of depth functions. After correcting, the prediction accuracies have been significantly improved at all depths. Our dataset can generate accurate SOCD maps at any specific depth interval by constructing the vertical continuous distribution of the corrected coefficients.
期刊介绍:
Environmental Modelling & Software publishes contributions, in the form of research articles, reviews and short communications, on recent advances in environmental modelling and/or software. The aim is to improve our capacity to represent, understand, predict or manage the behaviour of environmental systems at all practical scales, and to communicate those improvements to a wide scientific and professional audience.