首页|基于国产资源一号02D卫星和机器学习算法的钦州湾滨海土壤盐分反演

基于国产资源一号02D卫星和机器学习算法的钦州湾滨海土壤盐分反演

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以钦州湾滨海地区作为研究区,基于国产资源一号02D(ZY1-02D)多光谱卫星提取的相关特征参数,在AdaBooost、LightGBM、XGBoost、RFR以及CatBoost五种不同机器学习算法的支持下,设置了 5种不同的输入变量组合,并基于决定系数(R2)和均方根误差(RMSE)对不同模型的性能进行了评价.结果显示:研究区实测土壤盐分含量范围为0.740～10.352 g/kg,均值为1.739 g/kg;CatBoost相较于AdaBooost、LightGBM、XGBoost、RFR有更好的预测性能,CatBoost结合全变量组在预测阶段取得了最高精度(R2=0.8317,RMSE=0.3957 g/kg);在全变量组中,纹理特征中的均值对土壤盐分含量最为敏感,贡献度最高;研究区土壤盐分含量预测值为0～8.784g/kg,均值为2.478g/kg,轻度盐渍土分布广泛,主要集中分布在研究区西部,在东部地区分布较零散.国产资源卫星遥感数据结合CatBoost模型在钦州湾滨海土壤盐分反演中表现出较好的性能,可为大规模估算土壤盐分含量提供一种新的方法和思路.
Inversion of coastal soil salinity in Qinzhou Bay based on domestic ZY1-02D satellite and machine learning algorithm
The relevant feature parameters extracted from the domestic ZY1-02D multispectral satellite were used to characterize the soil salinity over the coastal area of Qinzhou Bay with the support of Ada Boot,LightGBM,XGBoost,RFR,and CatBoost machine leaming algorithms.The performance of each model was evaluated with the coefficient of determination(R2)and root mean square error(RMSE).The results show that the soil total salt content in the research area was measured to range from 0.740 to 10.352g/kg with an average of 1.739g/kg.Model simulation results demonstrate that CatBoost had the best predictive performance over AdaBoost,LightGBM,XGBoost,and RFR,and combined CatBoost with the highest accuracy(R2=0.8317,RMSE=0.396g/kg);and of all variables in a group,the mean of texture features was most sensitive to soil salinity and made the highest contribution;The soil salt content was simulated to range from 0to 8.784g/kg,with an average of 2.478g/kg,in which mild salinity mainly occurred in the western part of the study area and scattered in the eastem part.The combination of domestic resource satellite remote sensing data and CatBoost model has shown good performance in retrieving soil salinity in the coastal area of Qinzhou Bay,providing a new approach to characterizing coastal soil salinity at a large-scale.

soil salinityremote sensing quantitative inversionmachine learningZY-limageryQinzhou bay

田义超、郑丹琳、张强、卢芳、黄友菊、陶进、张亚丽、林俊良、姚贵钊、姚媛元

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北部湾大学资源与环境学院,北部湾海洋发展研究中心,广西钦州 535000

北部湾大学,广西北部湾海洋环境变化与灾害研究重点实验室,海洋地理信息资源开发利用重点实验室,广西钦州 535000

广西壮族自治区自然资源遥感院,广西南宁 530028

土壤盐分 遥感定量反演 机器学习 资源一号 钦州湾

国家自然科学基金资助项目广西高校人文社会科学重点研究基地"北部湾海洋发展研究中心"项目资助广西高校人文社会科学重点研究基地"北部湾海洋发展研究中心"项目资助广西林业科技推广示范项目广西基地和人才项目广西自治区大学生创新创业训练项目

42261024JDZD202214BHZKY2022桂林科研[2022]第4号2019AC20088202011607185

2024

中国环境科学
中国环境科学学会

中国环境科学

CSTPCDCHSSCD北大核心
影响因子：2.174
ISSN：1000-6923
年,卷(期)：2024.44(1)
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