基于分数阶微分联合光谱指数估算银川平原土壤有机质含量  

Estimation of soil organic matter content in Yinchuan Plain based on fractional derivative combined with spectral indices

作　　者：尚天浩 陈睿华 张俊华[3] 王怡婧 SHANG Tianhao;CHEN Ruihua;ZHANG Junhua;WANG Yijing(College of Geographical Sciences and Planning,Ningxia University,Yinchuan 750021,China;Xi'an Meihang Remote Sensing Information Co.Ltd.,Xi'an 710199,China;College of Ecology and Environmental Science,Ningxia University/Breeding Base for State Key Laboratory of Land Degradation and Ecological Restoration in Northwestern China/Key Laboratory of Restoration and Reconstruction of Degraded Ecosystems in Northwestern China of Ministry of Education,Yinchuan 750021,China)

机构地区：[1]宁夏大学地理科学与规划学院,银川750021 [2]西安煤航遥感信息有限公司,西安710199 [3]宁夏大学生态环境学院/西北土壤退化与生态恢复国家重点实验室培育基地/西北退化生态系统恢复与重建教育部重点实验室,银川750021

出　　处：《应用生态学报》2023年第3期717-725,共9页Chinese Journal of Applied Ecology

基　　金：国家自然科学基金项目(42067003);国家重点研发计划项目(2021YFD1900602);清华大学-宁夏银川水联网数字治水联合研究院联合开放基金项目(SKLHSE-2022-IOW11)资助。

摘　　要：土壤有机质是评价土壤肥力的重要指标。为探讨分数阶微分联合不同光谱指数所建模型对较低土壤有机质含量的估算效果,本研究利用银川平原土壤野外高光谱反射率,结合室内实测有机质含量,对反射率原始数据进行倒数对数变换后进行0~2阶(间隔0.20)的分数阶微分处理,构建差值指数(DI)、比值指数(RI)、亮度指数(BI)、归一化指数(NDI)、再归一化指数(RDI)和广义指数(GDI),分析6个指数与土壤有机质含量间的二维相关性,筛选出最优光谱指数,分别建立主成分回归(PCR)、偏最小二乘回归(PLSR)、反向神经网络(BPNN)、支持向量机(SVM)和地理加权回归(GWR)模型估算土壤有机质含量。结果表明:DI、RI、NDI、BI、GDI、RDI与土壤有机质含量间的最大相关系数绝对值(MACC)整体呈现先上升后下降的规律,分别在1.0、0.6、1.4和1.6阶处MACC最高。基于分数阶微分变化下的0.2~2.0阶RDI可用于后续模型构建,其中,MACC值最佳组合波段主要集中在400~600和1300~1700 nm。在单一光谱指数RDI所建不同模型中,SVM模型估算精度最高,其建模决定系数、验证决定系数和相对分析误差分别达到0.86、0.87和2.32。研究结果可为低有机质含量地区的土壤有机质快速、准确估算及制图提供科学依据。Soil organic matter(SOM)is a crucial indicator of soil fertility.Field hyperspectral reflectance and laboratory SOM data of soil samples from the Yinchuan Plain were used to explore the performance of models based on fractional derivative combined with different spectral indices.Following reciprocal and logarithmic transformation,the reflectance data were processed using fractional derivative from 0 to 2 orders(interval 0.20).Then,the difference index(DI),ratio index(RI),brightness index(BI),normalized difference index(NDI),renormalized difference index(RDI),and generalized difference index(GDI)were constructed.The two-dimensional correlation between the six indices and SOM content were analyzed.The optimal spectral indices were selected to establish SOM estimation models with principal component regression(PCR),partial least square regression(PLSR),back propagation neural network(BPNN),support vector machine(SVM),and geographically weighted regression(GWR).Results showed that the maximum absolute correlation coefficient(MACC)values between DI,RI,NDI,BI,GDI,RDI,and SOM contents increased firstly and then decreased,with the highest values observed at 1.0,0.6,1.4,and 1.6 orders.The 0.2-2.0 order RDI under fractional derivative variation could be used for subsequent model construction,in which the optimal combinations of bands for MACC values were mainly concentrated at 400-600 nm and 1300-1700 nm.Among the different models based on the single spectral index RDI,the model based on SVM achieved the highest estimation accuracy,whose modeling determination coefficient,verification determination coefficient and relative percentage difference reached 0.86,0.87 and 2.32.Our results would provide a scientific reference for quick and accurate SOM assessment and mapping in areas with relatively low SOM content.

关 键 词：土壤有机质 分数阶微分 光谱指数 支持向量机 地理加权回归 

分 类 号：S153.6[农业科学—土壤学]