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Remote Sensing for Natural Resources    2021, Vol. 33 Issue (3) : 121-129     DOI: 10.6046/zrzyyg.2020333
Hyperspectral inversion of macro element content in loess based on the profile of Zaoshugou Village, Mangshan Mountain, Zhengzhou City
LI Shuangquan1(), MA Yufeng1(), LIU Xun2, LI Changchun2, DU Jun1
1. Institute of geography, Henan Academy of Sciences, Zhengzhou 450052, China
2. Henan Polytechnic University, Jiaozuo 454000, China
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The occurrence and development themselves of loess have recorded abundant historical information, and the macro element content of loess can accurately reflect the environmental evolution. Hyperspectral remote sensing technology enjoys the advantages of being multi-band, continuous, and high-resolution. Therefore, it can be used to detect subtle differences in soil attributes and thus provide technical support for the fast and effective acquisition of basic loess information. In this paper, the loess profile of Zaoshugou Village, Zhengzhou City is studied. Combining the hyperspectral technology, the correlation between the spectral data and the macro elements of the loess was analyzed according to smoothed original spectra, first-order differential (FD), second-order differential (SD), de-envelope (CR), and reciprocal logarithm (Log(1/R). A partial least square regression (PLSR) model was established using the wave band with a larger correlation coefficient R as the characteristic band. The main conclusions are as follows. The variations in Ga, Fe, and Mg elements in the loess profile indicate that the study area has experienced a cold dry - warm wet - cold dry climate cycle since the Middle Holocene about 5400 aBP. The reflectance spectra of the loess in different stratigraphic units show the characteristics with similar trends. However, their spectral reflectance is in the order of L0-2>L0-1>Lt>S0-1>TS. According to the method of partial least squares, the optimal inversion models of Fe2O3, CaO, and CaO/MgO are the PLSR model with FD spectral transformation as the independent variable, while the best inversion model of MgO is the PLSR model with CR spectral transformation as the independent variable. The optimal inversion model of Fe2O3, CaO, and CaO/MgO can effectively distinguish different climate zones and indicate palaeoclimate cycle changes in the region where the study area falls. The optimal inversion model of MgO can better indicate the palaeoclimate evolution law of the region where the study area falls and thus has a certain reference value.

Keywords loess      hyper-spectral      macro element      partial least squares method     
ZTFLH:  TP79  
Corresponding Authors: MA Yufeng     E-mail:;
Issue Date: 24 September 2021
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Shuangquan LI
Yufeng MA
Changchun LI
Jun DU
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Shuangquan LI,Yufeng MA,Xun LIU, et al. Hyperspectral inversion of macro element content in loess based on the profile of Zaoshugou Village, Mangshan Mountain, Zhengzhou City[J]. Remote Sensing for Natural Resources, 2021, 33(3): 121-129.
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Fig.1  Zaoshugou loess profile in Mangshan of Zhengzhou
等级 建模R2 验证R2 RPD 模型预测能力描述
A ≥型预测能 ≥型预测能 ≥型 最佳 模型精度极好,可以进行准确估算
B 0.67~0.89 0.63~0.79 ≥.63 较好 模型精度较好,可以达到估算要求
C 0.50~0.66 0.50~0.62 ≥.50 一般 模型精度一般,具备粗略估算能力
D <0.5 <0.5 <1.4 较差 模型精度较差,不具备估算能力
Tab.1  Model overall accuracy evaluation level standard
地层单元 样品
Fe2O3/% CaO/% MgO/% CaO/MgO
表土层Ts 5 4.14 5.56 2.14 2.59
黄土层L0-1 6 3.62 5.24 2.06 2.54
古土壤层S0-1 20 4.39 2.23 1.92 1.16
过渡层Lt 4 4.21 3.2 2.02 1.58
黄土层L0-2 12 3.59 3.54 2.11 1.67
Tab.2  Distribution of constant element in the Zaoshugou Holocene loess-soil profile of Zhengzhou
Fig.2  Constant element variation curve of the Zaoshugou loess-soil profile in Zhengzhou in Holocene
Fig.3  Original spectral reflectance curve after smoothing of different stratigraphic units
光谱变换 原始光谱 一阶微分 二阶微分 去包络线 倒数对数
Fe2O3 82 296 219 639 57
CaO 1 188 540 172 432 1574
MgO 349 182 288 310
CaO/MgO 1 187 535 179 417 1 440
Fe2O3 -0.47 0.67 -0.54 0.57 0.47
CaO -0.44 -0.75 -0.57 -0.53 0.45
MgO -0.52 0.55 -0.45 0.29
CaO/MgO -0.43 -0.74 0.51 -0.54 0.44
Fe2O3 400 766 766 879 400
CaO 883 661 875 894 907
CaO/MgO 883 661 1759 894 916
Tab.3  Maximum values and corresponding bands of correlation coefficients between macro elements of profile and different transform spectral reflectances
光谱变换 建模精度 验证精度 精度
原始光谱 0.47 0.30 0.46 0.34 1.25 D
FD 0.79 0.19 0.68 0.27 1.62 B
SD 0.40 0.28 0.47 0.41 0.90 D
CR 0.51 0.34 0.35 0.42 1.04 D
LOG(1/R) 0.44 0.32 0.54 0.32 1.37 D
Tab.4  Calibration and validation of Fe2O3 by PLSR model
Fig.4  Scatter diagram of Fe2O3 measured and prediction values by PLSR model
常量元素 光谱
建模精度 验证精度 精度
CaO FD 0.68 0.75 0.68 0.84 1. 61 B
SD 0.44 0.91 0.38 1.19 1.04 D
MgO FD 0.48 0.08 0.52 0.09 1.19 D
SD 0.43 0.08 0.38 0.11 1.06 D
CR 0.67 0.06 0.63 0.07 1.8 B
CaO/MgO FD 0.66 0.33 0.57 0.41 1.41 C
Tab.5  Calibration and validation of CaO,MgO and CaO/MgO by PLSR model
Fig.5  Scatter diagram of CaO,MgO and CaO/MgO measured and prediction values
Fig.6  The variation trend of Fe2O3,CaO,MgO and CaO/MgO measured and prediction values measured and predicted values in Zaoshugou profile
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