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

郑曼迪; 熊黑钢; 乔娟峰; 刘靖朝

doi:doi:10.3788/lop55.072801

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： 735次

Remote Sensing Inversion of Soil Organic Matter Based on Broad Band and Narrow Band Comprehensive Spectral Index

论文大纲

郑曼迪 ¹熊黑钢 ^2,*乔娟峰 ¹刘靖朝 ¹

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

遥感反演宽波段窄波段综合光谱指数 remote sensing inversion broad range narrow band comprehensive spectral index

AI 词云图 AI一句话精读 AI短摘要

注：本部分内容由 AI 自动生成，请您知悉。

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.

参考文献

[1] 方少文, 杨梅花, 赵小敏, 等. 红壤区土壤有机质光谱特征与定量估算——以江西省吉安县为例［J］. 土壤学报, 2014, 51(5): 1003-1010.

Fang S W, Yang M H, Zhao X M, et al. Spectral characteristics and quantitative estimation of SOM in red soil typical of Ji’an Country, Jiangxi Province［J］. Acta Pedologica Sinica, 2014, 51(5): 1003-1010.

[2] 吴一全, 周杨, 龙云淋. 基于自适应参数支持向量机的高光谱遥感图像小目标检测［J］. 光学学报, 2015, 35(9): 0928001.

Wu Y Q, Zhou Y, Long Y L. Small target detection in hyperspectral remote sensing image based on adaptive parameter SVM［J］. Acta Optica Sinica, 2015, 35(9): 0928001.

[3] Pisek J, Chen J M. Comparison and validation of MODIS and VEGETATION global LAI products over four BigFoot sites in North America［J］. Remote Sensing of Environment, 2007, 109(1): 81-94.

[4] Sims D A, Gamon J A. Relationships between leaf pigment content and spectral reflectance across a wide range of species, leaf structures and developmental stages［J］. Remote Sensing of Environment, 2002, 81(2): 337-354.

[5] 张娟娟, 田永超, 姚霞, 等. 同时估测土壤全氮、有机质和速效氮含量的光谱指数研究［J］. 土壤学报, 2012, 49(1): 50-59.

Zhang J J, Tian Y C, Yao X, et al. The spectral index for estimating soil OM, TN and AN content simultaneously using near-infrared spectroscopy［J］. Acta Pedologica Sinica, 2012, 49(1): 50-59.

[6] 王飞, 丁建丽, 魏阳, 等. 基于Landsat系列数据的盐分指数和植被指数对土壤盐度变异性的响应分析—以新疆天山南北典型绿洲为例［J］. 生态学报, 2017, 37(15): 5007-5022.

Wang F, Ding J L, Wei Y, et al. Sensitivity analysis of soil salinity and vegetation indices to detect soil salinity variation by using Landsat series images: applications in different oases in Xinjiang, China［J］. Acta Ecologica Sinica, 2017, 37(15): 5007-5022.

[7] 张林静, 岳明, 张远东, 等. 新疆阜康绿洲荒漠过渡带植物群落物种多样性特征［J］. 地理科学, 2003, 23(3): 329-334.

Zhang L J, Yue M, Zhang Y D, et al. Characteristics of plant community species diversity of oasis desert ecotone in Fukang, Xinjiang［J］. Scientia Geographica Sinica, 2003, 23(3): 329-334.

[8] 姚峰, 古丽·加帕尔, 包安明, 等. 基于遥感技术的干旱荒漠区露天煤矿植被群落受损评估［J］. 中国环境科学, 2013, 33(4): 707-713.

Yao F, Guli·Jiapaer, Bao A M, et al. Damage assessment of the vegetable types based on remote sensing in the open coalmine of arid desert area［J］. China Environmental Science, 2013, 33(4): 707-713.

[9] 赵芳, 彭彦昆. 区间偏最小二乘法结合拉曼光谱测定猪肉皮下脂肪的碘值［J］. 中国激光, 2017, 44(11): 1111001.

Zhao F, Peng Y K. Measurement of iodine value of pork’s subcutaneous adipose tissue by interval partial least square and Raman spectroscopy［J］. Chinese Journal of Lasers, 2017, 44(11): 1111001.

[10] Fabio C, Angelo P, Federico S, et al. Evaluation of the potential of the current and forthcoming multispectral and hyperspectral imagers to estimate soil texture and organic carbon［J］. Remote Sensing of Environment, 2016, 179: 54-65.

[11] 解宪丽, 孙波, 郝红涛. 土壤可见光-近红外反射光谱与重金属含量之间的相关性［J］. 土壤学报, 2007, 44(6): 982-993.

Xie X L, Sun B, Hao H T. Relationship between visible-near infrared reflectance spectroscopy and heavy metal of soil concentration［J］. Acta Pedologica Sinica, 2007, 44(6): 982-993.

[12] 陈祯. 不同土壤含水量、体积质量及光谱反射率的关系模型［J］. 农业工程学报, 2012, 28(4): 76-81.

Chen Z. Relationship model among water content, bulk density and reflectivity of different soil［J］. Transactions of the Chinese Society of Agricultural Engineering, 2012, 28(4): 76-81.

[13] 李萍. 黄河三角洲土壤含水量状况的高光谱估测与遥感反演［D］. 泰安: 山东农业大学, 2016: 26.

Li P. Hyperspectral estimation and remote sensing retrieval of soil water regime in the Yellow River Delta［D］. Taian: Shandong Agricultural University, 2016: 26.

[14] 徐涵秋, 唐菲. 新一代Landsat系列卫星: Landsat 8遥感影像新增特征及其生态环境意义［J］. 生态学报, 2013, 33(11): 3249-3257.

Xu H Q, Tang F. Analysis of new characteristics of the first Landsat 8 image and their eco-environmental significance［J］. Acta Ecologica Sinica, 2013, 33(11): 3249-3257.

[15] Khan N M, Rastoskuev V, Sato Y, et al. Assessment of hydrosaline land degradation by using a simple approach of remote sensing indicators［J］. Agricultural Water Management, 2005, 77: 96-109.

[16] Inakwu O A Odeh, Onus A. Spatial analysis of soil salinity and soil structural stability in a semiarid region of New South Wales, Australia［J］. Environmental Management, 2008, 42(2): 65-78.

[17] 陈红艳, 赵庚星, 陈敬春, 等. 基于改进植被指数的黄河口区盐渍土盐分遥感反演［J］. 农业工程学报, 2015, 31(5): 107-114.

Chen H Y, Zhao G X, Chen J C, et al. Remote sensing inversion of saline soil salinity based on modified vegetation index in estuary area of Yellow River［J］. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(5): 107-114.

[18] Baret F, Guyot G. Potentials and limits of vegetation indices for LAI and APAR assessment［J］. Remote Sensing of Environment, 1991, 35(2/3): 161-173.

[19] Schlerf M, Atzberger C, Hill J. Remote sensing of forest biophysical variables using HyMap imaging spectrometer data［J］. Remote Sensing of Environment, 2005, 95(2): 177-194.

[20] Roujean J L, Breon F M. Estimating PAR absorbed by vegetation from bidirectional reflectance measurements［J］. Remote Sensing of Environment, 1995, 51(3): 375-384.

[21] 蔡亮红, 丁建丽. 基于高光谱多尺度分解的土壤含水量反演［J］. 激光与光电子学进展, 2018, 55(1): 013001.

Cai L H, Ding J L. Inversion of soil moisture content based on hyperspectral multi-scale decomposition［J］. Laser & Optoelectronics Progress, 2018, 55(1): 013001.

[22] Viscarra R V, McGlyn R N, McBratney A B. Determining the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy［J］. Geoderma, 2006, 137(1/2): 70-82.

[23] 姜雪芹, 叶勤, 林怡, 等. 基于谐波分析和高光谱遥感的土壤含水量反演研究［J］. 光学学报, 2017, 37(10): 1028001.

Jiang X Q, Ye Q, Lin Y, et al. Inverting study on soil water content based on harmonic analysis and hyperspectral remote sensing［J］. Acta Optica Sinica, 2017, 37(10): 1028001.

[24] 李相, 丁建丽. 基于实测高光谱指数与HSI影像指数的土壤含水量监测［J］. 农业工程学报, 2015, 31(19): 68-75.

Li X, Ding J L. Soil moisture monitoring based on measured hyperspectral index and HSI image index［J］. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(19): 68-75.

[25] 梁雪, 吉海彦, 王鹏新, 等. 用MSC-ANN方法建立冬小麦叶片叶绿素与反射光谱的定量分析模型研究［J］. 光谱学与光谱分析, 2010, 30(1): 188-191.

Liang X, Ji H Y, Wang P X, et al. Study on building quantitative analysis model for chlorophyll in winter wheat with reflective spectrum using MSC-ANN algorithm［J］. Spectroscopy and Spectral Analysis, 2010, 30(1): 188-191.

[26] 唐启义. DPS数据处理系统［M］. 北京: 科学出版社, 2010: 2.

Tang Q Y. DPS data processing system［M］. Beijing: Science Press, 2010: 2.

[27] Masoud A A, Koikek K. Arid land salinization detected by remotely sensed land cover changes: a case study in the Siwa region, NW Egypt［J］. Journal of Arid Environments, 2006, 66(1): 151-167.

[28] 董志玲. 干旱荒漠区人工梭梭林土壤碳氮储量分布规律及影响因子研究［D］. 北京: 中国林业科学研究院, 2014: 36.

Dong Z L. The study of artificial Haloxylon forest distribution of soil carbon and nitrogen storage and its influence factors in arid desert area［D］. Beijing: Chinese Academy of Forestry Sciences, 2014: 36.

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.

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： 735次

关于本站 Cookie 的使用提示

全站搜索

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： 735次

相关论文

相关资讯

关于本站 Cookie 的使用提示

全站搜索

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： 735次