侯艳军, 塔西甫拉提·特依拜, 买买提·沙吾提, 张飞. 荒漠土壤有机质含量高光谱估算模型[J]. 农业工程学报, 2014, 30(16): 113-120. DOI: doi:10.3969/j.issn.1002-6819.2014.16.016
    引用本文: 侯艳军, 塔西甫拉提·特依拜, 买买提·沙吾提, 张飞. 荒漠土壤有机质含量高光谱估算模型[J]. 农业工程学报, 2014, 30(16): 113-120. DOI: doi:10.3969/j.issn.1002-6819.2014.16.016
    Hou Yanjun, Tashpolat·Tiyip, Mamat·Sawut, Zhang Fei. Estimation model of desert soil organic matter content using hyperspectral data[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2014, 30(16): 113-120. DOI: doi:10.3969/j.issn.1002-6819.2014.16.016
    Citation: Hou Yanjun, Tashpolat·Tiyip, Mamat·Sawut, Zhang Fei. Estimation model of desert soil organic matter content using hyperspectral data[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2014, 30(16): 113-120. DOI: doi:10.3969/j.issn.1002-6819.2014.16.016

    荒漠土壤有机质含量高光谱估算模型

    Estimation model of desert soil organic matter content using hyperspectral data

    • 摘要: 为解决荒漠土壤有机质含量高光谱估算存在的困难,提高土壤有机质含量估算的精准性,该文对准噶尔盆地东部荒漠土壤进行采样、化验分析和光谱测量、处理,分析土壤光谱与有机质含量的相关性,确定敏感光谱波段,建立荒漠土壤有机质含量多种高光谱估算模型,旨在通过模型精度的比较,确定最优模型。结果表明:反射率、倒数对数光谱与荒漠土壤有机质含量相关性低,而经过一阶微分、二阶微分变换后,相关系数有所提高,部分波段的相关系数通过0.01显著水平的检验,可以用来荒漠土壤有机质含量的估算;一元线性回归建立的估算模型的精度低,不适用荒漠土壤有机质含量高光谱的估算。荒漠土壤有机质多元逐步回归模型的二阶微分、倒数对数二阶微分修正决定系数得到了较大提高,分别提高了0.22和0.31,均方根误差下降了0.66和0.80,建模精度高于一元线性回归模型。荒漠土壤有机质一阶微分、二阶微分光谱的最小偏二乘回归模型的决定系数比其多元逐步回归模型提高了0.07、0.04,一阶微分、二阶微分均方根误差都下降了0.11,二阶微分偏最小二乘法回归模型是该研究所建12个模型的最优估算模型。在多元逐步、偏最小二乘回归模型中,最优估算模型是二阶微分模型,因而用偏最小二乘法回归估算荒漠土壤有机质含量是个可行的方法。该研究的成果为荒漠土壤有机质高光谱遥感分析提供了支撑,实现荒漠土壤有机质监测的时效性、准确性,为区域生态环境的修复提供依据。

       

      Abstract: Abstract: Little reports focus on estimating soils with organic matter content smaller than 2% using hyperspectral data. However, it is important for desert soils where most soil organic matter is low. Due to low organic matter content (<2%) in deserts soil, it is difficult to identify spectral features of soil organic matter and to determine sensitive bands. Therefore, this study aimed to establish a method for organic matter content of desert soils using Hyperspectral data. Thirty-two soil samples were collected from the Eastern Junggar Basin, China. The soil organic matter of these samples was determined. Meanwhile, the reflectance of samples was measured. The correlation between soil spectrum and its organic matter content was analyzed. Twenty-four of 32 samples was used for establishing hyperspectral models of estimating deserts soil organic matter content (SOMC) and the other 8 samples was for model verification. The results showed all the soil samples had the organic matter content <2%, in agreement with the study requirement here. Through comparing with response bands of soil organic matter with its content of 2% above, we found that spectral sensitive region 640-790 nm for deserts soil organic matter <2% was most sensitive. The correlations between reflectance spectra, inverse-log spectra (log (1/A)) and SOMC were low. Their correlations were improved after conducting the first-order differential and second-order differential transformation on soil spectra data. The correlation of partial spectrum bands by F test was significant (p<0.01). Thus it could be used to estimate deserts SOMC. The accuracy of linear regression model was rather low (R2<0.60) and not suitable to estimate deserts SOMC. Compared to the linear regression model, the R2 of second-order differential model and inverse-log second-order differential model based on multiple stepwise regressions was increased by 0.22 and 0.21, respectively, and their RMSE were decreased by 0.66 and 0.88, respectively. As such, the estimation accuracy of multiple stepwise regression models was higher than that from the linear regression model. Compared with the multiple stepwise regression models, the R2 of first-order differential model and second-order differential model based on partial least square regression was increased by 0.07 and 0.04 but the RMSE was reduced by 0.11. Therefore, the second-order differential model based on partial least square regression was the optimal model among the 12 estimation models established in this paper. For both multiple stepwise regressions and partial least square regression, the second-order differential estimation model was best in all the estimation models established. Therefore, the estimation of deserts SOMC using multiple stepwise regressions was feasible. The results here provide valuable information for hyperspectral remote sensing analysis on deserts soil organic matter, realize timeliness and accuracy of monitoring deserts soil organic matter, and helps with regional ecological environment restoration.

       

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