Retrieval of Organic Matter Content in Black Soil Based on Airborne Hyperspectral Remote Sensing Data: Taking Jiansanjiang District in Heilongjiang Province as an Example
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摘要: 掌握黑土地有机质含量对黑土资源利用与保护具有重要意义，而高光谱卫星影像的缺乏制约了区域尺度土壤有机质反演研究的开展.以黑龙江省建三江黑土区为例，采用CASI/SASI航空高光谱数据、ASD（analytical spectral devices）地面光谱数据和土壤样品有机质含量数据，基于有机质含量与光谱反射率的相关性和定量关系，构建最优的回归模型并开展研究区土壤有机质含量遥感反演.结果表明：偏最小二乘法回归模型比多元逐步回归模型更稳定（判定系数分别为0.885和0.653），且精度更高（均方根误差分别为0.424和0.744）；采用偏最小二乘模型反演的结果与地面化探结果基本一致.Abstract: The retrieval of organic matter content in black soil is of great significance for the utilization and conservation of black soil resources. However, the lack of hyperspectral satellite images has restricted the development of soil organic matter retrieval at the regional scale. In this paper, a typical black soil area of Heilongjiang Province was selected as the study area. Then, the CASI/SASI airborne hyperspectral data, ASD(analytical spectral devices) surface spectral data, and the soil organic matter content data are used to establish the optimal regression model based on the correlation and quantitative relationship between organic matter content and spectral reflectance values of soil samples. Finally, the retrieval of soil organic matter content of the study area is carried out. The results show that the partial least square regression model is more stable than the multivariate stepwise regression model (with determination coefficients of 0.885 and 0.653, respectively), and the accuracy is higher (with root mean square error of 0.424 and 0.744, respectively). The retrieval result derived from partial least square regression is basically consistent with the result of geochemical exploration on the ground.
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Key words:
- remote sensing /
- soil organic matter /
- hyperspectral /
- black soil /
- regression model
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图 4 大气校正前后土壤光谱曲线对比
Fig. 4. The comparison of soil spectral curves before and after atmospheric correction
图 7 偏最小二乘法主成分数量与模型精度对比
Fig. 7. Comparison of partial least square method for principal component number and model accuracy
图 8 验证样本实测与预测值对比
a.偏最小二乘法; b.多元逐步回归法
Fig. 8. Comparison diagrams of tested and predicted values
图 9 土壤有机质高光谱反演与地面化探对比
a.有机质高光谱反演结果；b.地面化探结果
Fig. 9. Comparison of hyperspectral retrieval of soil organic matter with geochemical exploration
表 1 CASI/SASI系列机载成像光谱仪参数
Table 1. Parameters of the airborne imaging spectrometers of the CASI/SASI series
参数 CASI-1500 SASI-600 光谱范围 380~1 050 nm 950~2 450 nm 每行像元数 1 470 640 连续光谱通道数 288 100 光谱带宽 2.3 nm 15 nm 帧频(全波段) 14 fps 100 fps 总视场角 40° 40° 瞬时视场角 0.028° 0.07° 信噪比(峰值) ＞1 100 ＞1 100 绝对辐射精度 ＜2% ＜2% 
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表 2 土壤有机质统计特征
Table 2. Statistical characteristics of soil organic matter
样品 最小值 最大值 均值 方差 标准差 偏度 峰度 P 有机质 7.651 143.985 40.207 400.017 20.000 2.881 12.263 0.313 注：单位为g/kg；P为K-S正态检验概率值.
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表 3 土壤反射率光谱及各种变换光谱与有机质含量相关系数绝对值最大的波段
Table 3. The band of soil reflectance spectrum and the absolute value of the correlation coefficient between various transformation spectra and organic matter contents
R R' R" (1/R)' (1/R)" ln(R) (ln(R))' 波段 r 波段 r 波段 r 波段 r 波段 r 波段 r 波段 r 352 -0.54 1 746 0.66 993 0.50 1 773 -0.71 509 0.67 352 -0.56 1 585 0.73 802 -0.50 1 747 0.66 603 0.49 1 252 -0.70 354 -0.65 793 -0.54 1 568 0.73 801 -0.50 1 744 0.65 489 -0.48 1 251 -0.70 770 -0.64 794 -0.54 1 249 0.73
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表 4 多元逐步回归与偏最小二乘法反演模型对比
Table 4. Comparison between multiple stepwise regression and partial least squares inversion model
样品 偏最小二乘法 多元逐步回归法 RMSE R2 RMSE R2 有机质 0.424 0.885 0.744 0.653
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