Abstract：Chromium (Cr) is one of the main target elements in the evaluation of soil heavy metal pollution in the black soil area of Northeast China. With the introduction of aerial hyperspectral technology, the hyperspectral inversion of Cr content has the data basis for a wide range of applications, among which the accuracy and application range of the hyperspectral model are the key factors affecting the quality of the investigation. The commonly used modeling method is to extract spectral features and build models by various statistical means. The limitations are that the model parameters are difficult to be explained, the modeling results are greatly influenced by sample selection, and the generalization ability is poor. In this study, based on the occurrence law of Cr in soil, a new indirect inversion model based on the influencing factors and spectral characteristics of Cr was designed to improve the applicability of the model in different regions. Two research areas of Jiansanjiang and Hailun in Heilongjiang Province were selected. The hyperspectral data came from the CASI/SASI aerial hyperspectral imaging system. The band range was 380～2 450 nm. The number of ground samples in Jiansanjiang and Hailun were 225 and 121 respectively. The physical and chemical parameters of Cr, SOM, N, P, K2O, SiO2, Al2O3, Fe2O3, CaO, MgO, Na2O and pH were obtained by chemical analysis. The partial least square method was used in modeling. The analysis results of the occurrence rule of Cr show that Cr in both research areas shows a very significant positive correlation with Al2O3, Fe2O3, MgO, K2O and pH, and a very significant negative correlation with SiO2, Na2O and SOM. This feature provides a foundation for the establishment of indirect inversion models. The analysis results of the spectral characteristics of Cr in the two regions together show that the spectral reflectance has the most obvious correlation with the Cr content after standard normalized variable (SNV) transformation, and the characteristic bands are 1 520, 2 195, 2 210 and 2 225 nm. The characteristic band after SNV transformation was taken as the independent variable of the pure spectral model. The SNV characteristic band and the above-mentioned soil components closely related to Cr were taken as the independent variable of the indirect inversion model. The modeling results show that, compared with the pure spectral model, the indirect inversion model significantly improved the inversion accuracy of Cr. In the Jiansanjiang area, the modeling R2 has been improved from 0.643 to 0.751, and the verification R2 has been improved from 0.571 to 0.687. In the Hailun area, the modeling R2 has been improved from 0.537 to 0.676, and the verification R2 has been improved from 0.471 to 0.643. The root mean square error (RMSE) of the indirect model is also reduced. The experimental results of model migration between the two study areas show that the migration ability of the pure spectral model is poor, and the regression R2 of measured and predicted values after model migration is close to 0, while the migration ability of the indirect model in the two study areas is significantly improved. When indirect inversion model of Hailun was applied to Jiansanjiang, the regression R2 of measured and predicted values reaches 0.597 5, while the indirect inversion model of Jiansanjiang was applied to Hailun, the regression R2 is 0.577 3. The results can provide a new method for large-scale inversion mapping of Cr in the soil in different areas.
Key words：Chromium; Aerial hyperspectral; Northeast black soil; Partial least square method; Inversion
赵宁博，秦 凯，赵英俊，杨越超. 土壤铬的航空高光谱间接反演模型及可迁移性研究[J]. 光谱学与光谱分析, 2021, 41(05): 1617-1624.
ZHAO Ning-bo, QIN Kai, ZHAO Ying-jun, YANG Yue-chao. Study on Indirect Inversion Model and Migration Ability of Chromium in Soil by Aerial Hyperspectral Method. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(05): 1617-1624.
[1] WU Ming-zhu, LI Xiao-mei, SHA Jin-ming(吴明珠, 李小梅, 沙晋明). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2014, 34(6): 1660.
[2] ZHANG Ming-yue, ZHANG Qi-li, WANG Lu, et al(张明月, 张奇栎, 王 璐, 等). Remote Sensing Technology and Application(遥感技术与应用), 2019, 34(2): 313.
[3] WANG Jing-zhe, TASHPOLAT·Tiyip, ZHANG Dong(王敬哲, 塔西甫拉提·特依拜, 张 东). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2017, 48(5): 152.
[4] LU Jie-hui, LI Xi-can, WANG Feng-hua(路杰晖, 李西灿, 王凤华). Journal of Geomatics Science and Technology(测绘科学技术学报), 2018, 35(5): 508.
[5] YANG Jin-song, YAO Rong-jiang(杨劲松, 姚荣江). . Scientia Geographica Sinica(地理科学), 2007, 27(3): 348.
[6] CHEN Ying-xu, LUO Yong-ming(陈英旭, 骆永明). Acta Pedologica Sinica(土壤学报), 1994, 31(1): 77.
[7] GUI Xin-an, YANG Hai-zhen, WANG Shao-ping, et al(桂新安, 杨海真, 王少平, 等). Chinese Journal of Soil Science(土壤通报), 2007, 38(5): 177.
[8] Vega F A, Covelo E F, Andrade M L. Journal of Colloid & Interface Science, 2006, 298(2): 582.
[9] ZHENG Shun-an, ZHENG Xiang-qun, LI Xiao-chen, et al(郑顺安, 郑向群, 李晓辰, 等). Environmental Science(环境科学), 2013, 34(2): 698.
[10] WANG Xue-feng, SHANG Fei, MA Xin, et al(王学锋, 尚 菲, 马 鑫, 等). Journal of Henan Normal University·Natural Science Edition(河南师范大学学报·自然科学版), 2013，41(5): 101.
