引用本文:周倩倩,丁建丽,唐梦迎,杨 斌.干旱区典型绿洲土壤有机质的反演及影响因素研究[J].土壤学报,2018,55(2):313-324.
ZHOU Qianqian,DING Jianli,TANG Mengying,YANG Bin.Inversion of Soil Organic Matter Content in Oasis Typical of Arid Area and Its Influencing Factors[J].Acta Pedologica Sinica,2018,55(2):313-324
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干旱区典型绿洲土壤有机质的反演及影响因素研究
周倩倩1, 丁建丽1, 唐梦迎1, 杨 斌2
1.新疆大学资源与环境科学学院智慧城市与环境建模自治区普通高校重点实验室;2.武警黄金第八支队
摘要:
为了大面积、高精度地反演土壤有机质含量,为农业可持续发展提供数据支撑。以新疆渭干河-库车河三角洲绿洲为研究区,采用波段平均法将实测高光谱窄波段拟合为Landsat 8 OLI遥感影像的宽波段,建立土壤有机质含量的估算模型,并将最优估算模型应用到经过波段校正的Landsat 8 OLI遥感影像中。结果表明:(1)反射率进行倒数、对数、平方、一阶微分等数学变换后与有机质含量的相关性显著提高;(2)土壤有机质的高光谱估算模型拟合度较高,最优估算模型的决定系数R2为0.852,采用比值法对多光谱波段反射率进行校正,校正后的遥感影像反演结果得到了较大提高,检验样本的决定系数R2从0.711提升至0.849。从反演结果来看,将高光谱估算模型应用到经过订正的多光谱影像,土壤有机质反演模型的精度得到了大幅度提高,运用此方法可以实现高精度的土壤有机质区域化反演。(3)有机质的分布受土地利用类型、土壤颗粒组成、土壤质地的影响,其中土壤质地对有机质的空间分布影响最为显著。
关键词:  高光谱  Landsat 8 OLI  土壤有机质  影响因素
DOI:10.11766/trxb201705310236
分类号:
基金项目:国家自然科学基金项目(U1303381,41261090)、自治区重点实验室专项基金(2016D03001)及自治区科技支疆项目(201591101)和教育部促进与美大地区科研合作与高层次人才培养项目
Inversion of Soil Organic Matter Content in Oasis Typical of Arid Area and Its Influencing Factors
ZHOU Qianqian1, DING Jianli1, TANG Mengying1, YANG Bin2
1.Key Laboratory of Wisdom City and Environmental Modeling Department of Education,Xinjiang University;2.Gold Geological Party of CAPF
Abstract:
【Objective】 Soil organic matter (SOM) content is an important soil index, essential to guiding usage of chemicals in agriculture, and also an important factor affecting regional carbon balance. Scholars have long been interested in the study of soil organic matter and have helped address key environmental, agricultural and social and political issues over the past ten years. It is essential to have simpler, more accurate, more rapid and more inexpensive methods for plotting soil organic matter maps, and moreover, more time-and-cost saving ones.【Method】 Remote sensing data have extensively been used in digital soil mapping, especially in assessing soil organic matter, because the use improves accuracy of the prediction of soil physical parameters to some extent. To explore feasibility of combining the narrow band of hyperspectrum and the wide band of multispectral remote sensing images to realize high-accuracy prediction of soil organic matter (SOM), field data and soil samples were collected in Weigan River Oasis of Kuche in May of 2016 for in-lab analysis of SOM content using the potassium dichromate method; hyperspectral data were in the darkroom with the aid of the ASD Fieldspec3 spectrometer; the first 7 bands of the Landsat 8 OLI remote sensing images of May 30, 2016 were selected and used for atmospheric correction, radiometric correction and fine geometric correction of the images as pretreatment. In addition, the band averaging method was used to fit the measured data of the hyperspectral narrow bands into data of the multispectral wide bands, and then to screen out sensitive spectral parameters; models for assessing soil organic matter were built up, using the partial least squares regression method for test and screening of an optimal model. In the end, spatial distribution of soil organic matter was analyzed, taking into account all soil factors.【Result】Results show that SOM content in the oasis varies in the range of 3.57 ~ 39.22g kg-1. An optimal prediction model was built up based on the 2nd, 5th and 6th bands as independent variables after being subjected to first differential transformation, with determination coefficient R2 of the model dataset being 0.852 and of the validation set being 0.897. On such a basis, the optimal model was applied to multi-spectaral data based prediction of soil organic matter using the Landsat 8 OLI satellite images. Differential transformation significantly improved the correlation of hyperspectrum with soil organic matter content. After the reflectances of the multi-spectral bands were calibrated with the ratio method, determination coefficient R2 of the validation dataset was raised from 0.711 to 0.849. Distribution of soil organic matter was less affected by land use types or soil texture than soil particle composition.【Conclusion】The inversion of SOM indicates that the remote sensing based inversion of SOM fits the actual situation of the study area, displaying good reliability and authenticity. In this study, the findings are the same as and different in places from those of other scholars, so further studies should take into account effects of soil moisture content, salinity, landform and some other factors on soil organic matter content and improve accuracy of the prediction model. All the findings of the study exploring feasibility of combining the hyper-spectral model with remote sensing inversion in predicting soil organic matter in the studied area may serve as scientific basis and technical reference for quick acquisition of SOM information in Arid and semi-arid regions.
Key words:  Hyperspectral  Landsat 8 OLI  Soil organic matter  Influencing factor