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Study on the Relationship Between Black Soil Emissivity Spectrum and Total Potassium Content Based on TASI Thermal Infrared Data |
LI Ming, QIN Kai*, ZHAO Ning-bo, TIAN Feng, ZHAO Ying-jun |
National Key Laboratory of Remote Sensing Information and Image Analysis, Beijing Research Institute of Uranium Geology, Beijing 100029, China |
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Abstract Potassium content in soil is one of the important indicators for evaluating soil nutrient levels. There are few studies using thermal infrared emissivity data to invert potassium, and the model accuracy is low. In this paper, the Thermal Airborne Hyperspectral Imager (TASI) data collected in the Hailun region of Northeast China is used to investigate the relationship between soil emissivity and potassium content in black soil after pretreatment and separation of temperature and emissivity. Compared with the constant multiple stepwise regression and partial least-square regression model, a new stepwise regression method- quadratic multiple stepwise regression is innovatively used to enhance the model. Compared with the constant multiple stepwise regression, more parameters are introduced to establish the model, which can effectively improve the inversion accuracy. It is found that the model which uses effective special selected bands has a higher inversion accuracy to the potassium element and the selected bands are negatively correlated. The bands are 6 (8.602 μm), 11 (9.150 μm), 15 (9.588 μm), and 23 (10.464 μm)and the correlation coefficients are -0.658, -0.673, -0.645, -0.627, respectively. The quadratic multiple stepwise regression model’s RMSE of the training and testing data are 0.027 and 0.032, the decision coefficient R2 are 0.667 and 0.82. Compared to the constant multiple stepwise regression model’s RMSE of the training and testing data: 0.031 and 0.031, the decision coefficient R2: 0.569 and 0.78 and the least squares model’s RMSE: 0.033, 0.037, the judgment coefficient R2: 0.45, 0.51, the precisions of evalution indexes have been improved, it is indicated that this method effectively improved the inversion accuracy of the potassium element using the emissivity data. After using the studentized residuals to improve the model to remove the outliers, it is found that the training accuracy is significantly improved but the test accuracy is reduced. Over-fitting the training set data leads to the decline of the model generalization. Therefore, the model is not recommended to improve.
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Received: 2019-07-31
Accepted: 2019-11-09
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Corresponding Authors:
QIN Kai
E-mail: h_rs_qk@163.com
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[1] ZHANG Dong-hui, ZHAO Ying-jun, QIN Kai, et al(张东辉, 赵英俊, 秦 凯,等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2018, 34(20): 141.
[2] LIU Huan-jun, WANG Xiang, ZHANG Xiao-kang, et al(刘焕军, 王 翔, 张小康, 等). Chinese Journal of Soil Science(土壤通报), 2018, 49(1): 38.
[3] Zhou D K, Larar A M, Liu X. Journal of Applied Remote Sensing, 2018, 12(1):1.
[4] SHI Yang, WANG Ru-jing, WANG Yu-bing(史 杨, 王儒敬, 汪玉冰). Chinese Journal of Luminescence(发光学报), 2018, 39(10): 1458.
[5] XIE Wen, ZHAO Xiao-min, GUO Xi, et al(谢 文, 赵小敏, 郭 熙, 等). Scientia Silvae Sinicae(林业科学), 2018, 54(6): 16.
[6] CAO Wen-tao, WU Quan-yuan, WANG Fei, et al(曹文涛, 吴泉源, 王 菲, 等). Chinese Journal of Soil Science(土壤通报), 2016, 47(2): 265.
[7] Wu W, Al-Shafie W M, Mhaimeed A S, et al. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2014, 7(11): 4442.
[8] Rocha Neto, Odílio, Teixeira A, et al. Remote Sensing, 2017, 9(42): 1.
[9] LI Yan, WANG Rang-hui, GUAN Yan-long, et al(李 焱, 王让会, 管延龙,等). Remote Sensing Technology and Application(遥感技术与应用), 2017, 32(1): 173.
[10] WANG Xiang-feng, MENG Ji-hua(王祥峰, 蒙继华). Remote Sensing Technology and Application(遥感技术与应用), 2015, 30(6): 1033.
[11] CHEN Yuan-peng, ZHANG Shi-wen, LUO Ming, et al(陈元鹏, 张世文, 罗 明, 等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2019, 50(1): 170.
[12] ARDAK·Kelimu, TASHPOLAT·Tiyip, ZHANG Fei, et al(阿尔达克·克里木, 塔西甫拉提·特依拜, 张 飞, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(17): 115.
[13] XIA Jun, ZHANG Fei(夏 军, 张 飞). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2019, 39(4): 1063.
[14] YANG Yong-min, QIU Jian-xiu, SU Hong-bo, et al(杨永民, 邱建秀, 苏红波,等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2018, 37(4): 459.
[15] YANG Hang, ZHANG Li-fu, ZHANG Xue-wen, et al(杨 杭, 张立福, 张学文,等). Journal of Remote Sensing(遥感学报), 2011, 15(6): 1242. |
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