土壤2023，Vol.60Issue(5)：1106-1113,8.DOI:10.13758/j.cnki.tr.2023.05.021
基于双时相卫星遥感光谱指数估算土壤有机质含量
Estimation of Soil Organic Matter Content Based on Dual-temporal Satellite Remote-sensing Spectral Index
摘要
Abstract
The performance of the dual-temporal spectral index(the band combination of two images)in predicting soil organic matter(SOM)was investigated over a double-cropping agricultural region(Fengqiu County)in the Huang-Huai-Hai Plain,where the bare soil period is often short for remote sensing of soils.In the study,a total of 117 soil samples were collected and dual-temporal Landsat 8 satellite images during the bare soil period(Acquisition date:October 6,2014 and October 30,2017)were selected for establishing four types of spectral indices:ratio spectral index,difference spectral index,normalized spectral index and optimized spectral index.Then,these indices were used as the input in SVM(Support Vector Machine)models of SOM after being selected by the variable selection method of LASSO(Least Absolute Shrinkage and Selection Operator).The results of leave-one-out cross-validation showed that,compared with image bands or spectral indices built by single images(single-temporal spectral index),the dual-temporal spectral index could make better use of temporal information of images and its prediction accuracy was higher for SOM(R2=0.53,RMSE=2.01g/kg).Moreover,the spatial distribution pattern of SOM predicted by the dual-temporal spectral index was consistent with the real condition.Thus,the proposed method of using the dual-temporal spectral index for SOM prediction in the study could promote prediction and mapping of soil properties in areas with short bare soil periods.关键词
土壤有机质/土壤遥感/双时相光谱指数/黄淮海平原Key words
Soil organic matter/Remote sensing of soils/Dual-temporal spectral index/Huang-Huai-Hai Plain分类
农业科技引用本文复制引用
王欣怡,王昌昆,马海艺,刘杰,袁自然,姚成硕,潘贤章..基于双时相卫星遥感光谱指数估算土壤有机质含量[J].土壤,2023,60(5):1106-1113,8.基金项目
国家自然科学基金项目(41971050)、福建省自然科学基金项目(2020J05027)、遥感科学国家重点实验室开放基金项目(OFSLRSS202112)和教育部人文社科青年基金项目(21YJC630090)共同资助 Supported by the National Natural Science Foundation of China(No.41971050),the National Natural Science Foundation of Fujian Province,China(No.2019J01660),Open Fund of Key Laboratory of Remote Sensing Science(No.OFSLRSS202112)and Young Foundation of Ministry of Education,Humanities and Social Science(No.21YJC630090) （41971050）
