融合作物类型的土壤盐分遥感反演方法研究  

Study of soil salinity remote sensing inversion method integrating crop type

作　　者：张胜男 陆苗[1] 温彩运 宋英强 康璐 沈军辉 杨民志 ZHANG Shengnan;LU Miao;WEN Caiyun;SONG Yingqiang;KANG Lu;SHEN Junhui;YANG Minzhi(State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China(the Institute of Agricultural Resources and Regional Planning,Chinese Academy of Agricultural Sciences),Beijing 100081,China;School of Civil and Architectural Fengineering,Shangdong University of Technology,Zibo 255000,China;Agricultural Development Service Center of Kenli District,Dongying 257500,China)

机构地区：[1]北方干旱半干旱耕地高效利用全国重点实验室(中国农业科学院农业资源与农业区划研究所),北京100081 [2]山东理工大学建筑工程与空间信息学院,山东淄博255000 [3]东营市垦利区农业发展服务中心,山东东营257500

出　　处：《测绘通报》2024年第2期1-7,共7页Bulletin of Surveying and Mapping

基　　金：国家重点研发计划(2023YFD200140101);国家自然科学基金(42071419);中国农业科学院科技创新工程(CAAS-ZDRW202201)。

摘　　要：在沿海平原地区,土壤盐度是制约作物生长的非生物胁迫之一,也是作物种植的重要依据,作物类型能够间接反映土壤盐渍化程度,因此本文提出了一种融合作物类型信息的土壤盐分反演方法。以黄河三角洲典型滨海盐渍土地区为例,基于Sentinel-2 MSI影像,首先采用随机森林分类提取作物类型信息,并基于OneHot方式将作物类型信息编码;然后融合作物类型信息,结合环境协变量数据、地面实测盐分数据,采用自适应增强决策树模型(AB-DT)进行盐分反演;最后与其他机器学习方法,如支持向量机、随机森林、K最邻近和决策树进行盐分反演精度的对比。结果表明:①加入作物类型信息能够提高土壤盐分反演模型精度,所有模型中,融合作物类型变量的AB-DT反演模型精度最高,建模集R 2为0.86,测试集R 2为0.61;②加入作物类型信息能够修正误判的盐渍土级别,并使土壤盐分反演结果的地块边缘更加清晰。综上所述,加入作物类型信息,能够提高土壤盐分反演的准确性,为农田管理和农业决策提供更可靠的依据。In coastal plain regions,soil salinity serves as one of the abiotic stressors limiting crop growth.The content of soil salinity is a critical determinant for crop cultivation.The variety of crops grown can indirectly indicate the extent of soil salinization.Therefore,this paper proposes an integrated approach for the inversion of soil salinity,incorporating crop type information.Based on Sentinel-2 MSI imagery in a typical coastal saline soil area in the Yellow River Delta.Firstly,crop type information was extracted using random forest classification and coded based on the OneHot method.Then,by integrating crop type information,environmental covariate data,and ground-measured salinity data,the adaptive boosting decision tree(AB-DT)model is applied for soil salinity estimation.Finally,the accuracy of salinity estimation is compared with other machine learning methods,including support vector machines,random forests,K-nearest neighbors,and decision trees.The results indicated that①Incorporating crop type information enhances the accuracy of soil salinity estimation models.Among all models,the AB-DT model with fused crop type variables achieves the highest modeling set R 2 of 0.86 and validation set R 2of 0.61.②The inclusion of crop type information enable to correct misclassifications of salinity levels and yield sharper boundaries in soil salinity estimation results.In conclusion,the incorporation of crop type information improves the accuracy of soil salinity estimation,providing a more reliable basis for agricultural management and decision-making.

关 键 词：土壤盐渍化 多光谱遥感反演 机器学习 

分 类 号：P237[天文地球—摄影测量与遥感]