激光与光电子学进展, 2018, 55 (7): 072801, 网络出版: 2018-07-20   

基于宽波段与窄波段综合光谱指数的土壤有机质遥感反演 下载: 734次

Remote Sensing Inversion of Soil Organic Matter Based on Broad Band and Narrow Band Comprehensive Spectral Index
作者单位
1 新疆大学资源与环境科学学院绿洲生态重点实验室, 新疆 乌鲁木齐 830046
2 北京联合大学应用文理学院城市科学系, 北京 100083
摘要
对比基于宽波段、窄波段建立的土壤有机质(SOM)含量的预测模型以及空间格局分布的差异性,采用地面高光谱测量和土质分析验证利用卫星遥感数据监测土壤基本生态参数的可行性。以天山北麓的土壤为研究对象,运用宽波段、窄波段两种方式计算实测光谱反射率的综合光谱指数,与无人干扰区、人为干扰区的有机质进行相关性分析以及主成分分析,以相关系数和特征向量值都较优的综合光谱指数作为自变量,使用多元线性回归模型(MLR)以及偏最小二乘回归模型(PLSR)分别建立了无人干扰区及人为干扰区宽、窄波段的SOM高光谱预测模型,并进行模型验证、对比与优选,最后基于最佳模型对研究区进行SOM含量的空间格局反演和分析。结果显示,通过有机质与盐分指数、植被指数的相关性分析和主成分分析,挑选出了无人干扰区窄波段的盐分指数2(SI2)、盐分指数3(SI3)和比值植被指数(RVI)、归一化植被指数(NDVI)以及宽波段的盐分指数1(SI1)、SI2和RVI、NDVI,人为干扰区窄波段的SI1、SI3和RVI、NDVI以及宽波段的SI1、SI2、重归一化植被指数(RDVI),以此为自变量建立有机质含量的MLR以及PLSR模型。通过对比所建模型的精度可知,无论是无人干扰区还是人为干扰区,有机质预测模型精度最高的均是窄波段的PLSR模型,可决定系数、相对分析误差分别为0.753、2.01和0.819、2.14。基于以上的最佳模型对研究区的SOM含量进行空间反演与分析可知:无人干扰区的有机质质量分数集中在小于10×10-3范围内,呈现出中间低、四周高的趋势;而人为干扰区有机质的质量分数集中在10×10-3~15×10-3范围内,呈现出西南、东北低,中北部高的趋势。
Abstract
Comparing the soil organic matter (SOM) prediction model based on the broad and narrow bands and the difference in spatial pattern distribution, we validate the feasibility of using satellite remote sensing data to monitor soil basic ecological parameters by ground hyperspectral measurement and analysis of soil. Taking the soil of Tianshan as the research object, we calculate comprehensive spectral index of the broad and narrow band, respectively, using correlation analysis and principal component analysis in the organic matter of unmanned interference area, human interference area, and choosing the comprehensive spectral index with better correlation coefficient and characteristic vector value as the independent variables, using multivariate linear regression model (MLR) and partial least squares regression model (PLSR) to establish respectively the hyperspectral prediction model of SOM in broad and narrow band of unmanned interference area and human interference area. The validation, the comparison and selection the model are carried out. Finally, we analyze and inverse the spatial pattern of SOM content based on the best model of research area. Results show that, through the correlation analysis and principal component analysis of the organic matter and salinity index, vegetation index to establish the MLR and PLSR of organic matter component, we pick out the salinity index 2 (SI2), salinity index 3 (SI3) and ratio vegetation index (RVI), normalized difference vegetation index (NDVI) of narrow band, and the SI1, SI2, RVI, NDVI of broad band in unmanned interference area; we pick out SI1, SI3, RVI and NDVI of narrow band, and SI1, SI2, RVI and renormalized difference vegetation index (RDVI) of broad band. Taking these parameters as independent variables, we build MLR and PLSR models of soil organic matter. By comparing the precision of the models, we find that the PLSR model with narrow band has high precision in human or unmanned interference areas, and determinable coefficient and relative percent deviation are 0.753, 2.01 and 0.819 and 2.14, respectively. Spatial inversion and analysis of SOM in research area are carried out based on the best model above. The mass fraction of organic matter in unmanned interference area is concentrated in less than 10×10-3, and presents the trend of low in middle and high in around. The mass fraction of organic matter in human interference is 10×10-3-15×10-3, presents the trend of low in southwest and northeast, and high around middle north region.

郑曼迪, 熊黑钢, 乔娟峰, 刘靖朝. 基于宽波段与窄波段综合光谱指数的土壤有机质遥感反演[J]. 激光与光电子学进展, 2018, 55(7): 072801. Zheng Mandi, Xiong Heigang, Qiao Juanfeng, Liu Jingchao. Remote Sensing Inversion of Soil Organic Matter Based on Broad Band and Narrow Band Comprehensive Spectral Index[J]. Laser & Optoelectronics Progress, 2018, 55(7): 072801.

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