基于无人机遥感的盐渍化土壤有机质反演方法研究  

Study on Retrieval Method of Soil Organic Matter in Salinity Soil Using Unmanned Aerial Vehicle Remote Sensing

作　　者：王玮莹 彭金榜 朱婉雪 杨斌 刘振[1,3] 公华锐 王俊栋 杨婷 娄金勇 孙志刚[1,2,3] WANG Weiying;PENG Jinbang;ZHU Wanxue;YANG Bin;LIU Zhen;GONG Huarui;WANG Jundong;YANG Ting;LOU Jinyong;SUN Zhigang(Institute of Geographic Sciences and Natural Resources Research,Chinese Academy of Sciences,Beijing 100101,China;College of Resources and Environment,University of Chinese Academy of Sciences,Beijing 100049,China;Shandong Dongying Institute of Geographic Sciences,Dongying 257000,China;Yusense Information Technology and Equipment(Qingdao)Co.,Ltd.,Qingdao 266000,China)

机构地区：[1]中国科学院地理科学与资源研究所,北京100101 [2]中国科学院大学资源与环境学院,北京100049 [3]中科山东东营地理研究院,东营257000 [4]长光禹辰信息技术与装备(青岛)有限公司,青岛266000

出　　处：《地球信息科学学报》2024年第3期736-752,共17页Journal of Geo-information Science

基　　金：国家重点研发计划项目(2021YFD1900902);中国科学院A类战略性先导科技专项(XDA23050102);国家自然科学基金面上项目(42271349);国家自然科学基金创新研究群体科学基金(72221002);青岛产业领军人才工程资助;泰山产业领军人才工程资助。

摘　　要：土壤有机质含量是衡量土壤肥力的重要指标,对粮食生产具有重要影响。我国滨海盐碱地分布广阔,土壤有机质呈现显著的空间异质性。传统的“实地采集-实验测量”的方法费时费力,难以快速描绘盐碱地区土壤有机质含量的空间分布特征。因此,采用遥感技术对土壤有机质含量进行高效且精准地反演,可以快速了解土壤肥力,从而调整与优化农业生产与管理。本研究以黄河三角洲典型滨海盐碱地裸土(0~10 cm土层)为研究对象,通过提取土壤的无人机遥感光谱与空间纹理信息,构建并筛选遥感指数。分别采用多元线性逐步回归、偏最小二乘以及随机森林模型对土壤有机质含量进行反演,并对比分析耕作与未耕作处理对反演的影响,同时探究融入对土壤盐分含量敏感的遥感信息是否可提高盐碱地区土壤有机质的反演精度。研究结果表明:(1)在土壤有机质含量反演中,随机森林模型的反演精度(R2为0.83~0.95)显著高于多元线性逐步回归(R2为0.26~0.69)和偏最小二乘模型(R2为0.37~0.72);(2)与未耕作土壤(R2为0.26~0.95)相比,耕作处理下的土壤有机质的反演精度较高(R2为0.54~0.94),即土壤的耕作处理能提高光谱指数对于土壤有机质含量的响应,从而提升土壤有机质含量的反演精度,为更加精准地监测土壤有机质提供新思路;(3)融入遥感纹理信息或土壤盐分含量信息(盐分实测值和盐分敏感指数)可显著提高土壤有机质反演精度。本研究将为提升田块尺度滨海盐渍化农田的土壤有机质含量反演提供理论与技术支撑,从而促进滨海盐渍化地区现代化农业的发展。Soil Organic Matter(SOM)content is an important indicator for measuring soil fertility and has a significant impact on food production.China's coastal saline-alkali lands are vast,and SOM exhibits significant spatial heterogeneity.The traditional method of“field collection and experimental measurement”is time-consuming and labor-intensive,making it difficult to quickly depict the spatial distribution characteristics of SOM content in saline-alkali areas.Therefore,efficiently and accurately using remote sensing technology to invert SOM content can provide insights into soil fertility,so as to help adjust and optimize agricultural production and management.This study focused on the bare soil(0~10 cm soil layer)in a typical coastal saline-alkali land of the Yellow River Delta and constructed remote sensing indices from the Unmanned Aerial Vehicle(UAV)remote sensing spectral and spatial texture information.The multi-linear stepwise regression model,partial least squares model,and random forest model were employed to quantitatively estimate SOM.We compared and analyzed the impact of cultivated and uncultivated soil on SOM content retrieval through UAV remote sensing methods,and also explored whether the integration of remote sensing information sensitive to soil salt content aids in SOM retrieval in saline-alkali areas.The results show that:(1)In the inversion of SOM content,the accuracy of random forest model(R2 ranging from 0.83 to 0.95)was significantly higher than that of multiple linear stepwise regression(R2 ranging from 0.26 to 0.69)and partial least squares model(R2 ranging from 0.37 to 0.72);(2)Compared to uncultivated soil(R2 ranging from 0.26 to 0.95),the inversion accuracy of organic matter in cultivated soil(R2 ranging from 0.54 to 0.94)was significantly higher.That is to say,cultivation treatment enhanced the response of spectral indices to SOM content,thereby increasing the inversion accuracy of SOM content,which provides new insights for precise monitoring of SOM;(3)Integrating remote sensing textu

关 键 词：土壤有机质反演 无人机多光谱遥感 土壤盐分 光谱指数 盐碱地 裸土 耕作 未耕作 

分 类 号：S153.621[农业科学—土壤学] TP79[农业科学—农业基础科学]