Remote Sensing Inversion of Soil Manganese in Nanchuan District, Chongqing
XU Tian1, 2, LI Jing1, 2, LIU Zhen-hua1, 2*
1. College of Natural Resources and Environment, South China Agricultural University，Guangzhou 510000, China
2. Guangdong Province Engineering Research Center for Land Information Technology，Guangzhou 510000, China
Abstract：Manganese in soil plays an important role in plant growth, and high or low levels of soil manganese will have adverse effects on plants, so it is especially important to monitor soil manganese content quickly. At present, the studies related to the monitoring of soil Mn content by remote sensing technology mainly focus on the estimation of soil Mn content using soil spectra. At the same time, it is difficult to obtain soil spectra from satellite images in the southern region where vegetation covers all year round. Therefore, this paper introduces vegetation spectra to explore the rapid monitoring method of soil Mn elements in vegetation-covered areas. Firstly, 11 vegetation spectral indicators were extracted from Landsat 8 images, and the best vegetation spectral indicators were selected by Pearson correlation coefficient combined with Variance Inflation Factor (VIF); based on this, Partial Least Squares (PLS) regression was used. (PLSR), Multiple Stepwise Regression (MSR) and BP Neural Network (BPNN) algorithms were used to construct the best vegetation spectral indicators. Finally, spatial mapping of soil Mn content was carried out based on the best inversion model. Taking the Nanchuan District of Chongqing City as an example, the results showed that three vegetation spectral indicators (specific vegetation index, normalized vegetation index and visible atmospheric impedance vegetation index) were identified as the best spectral response indicators of soil Mn. The mapping accuracy of soil Mn content (R2 was 0.69, RMSE was 567.64, and RPD was 1.30). The results showed that the inversion of soil Mn content by vegetation spectral indicators is feasible, and this study opens up new ideas for monitoring soil Mn content at the regional scale.
Key words：Vegetation spectral index;Soil manganese;BPNN;Spectral response index screening;Optimal inversion model
[1] TENG Xiao-hua, LIU Yu-hao, LI Ke-fei, et al(滕小华, 刘宇昊, 李克非, 等). Journal of Northeast Agricultural University(东北农业大学学报), 2021, 52(1): 90.
[2] PAN Yong, LIU Yan-shu(潘 勇, 刘彦姝). Laser and Infrared(激光与红外), 2012, 42(4): 426.
[3] Pandit Caaminee M, Filippelli Gabriel M, Li Lin. International Journal of Remote Sensing, 2010, 31(15): 4111.
[4] ZHANG Yu-xiu, LI Lin-feng, CHAI Tuan-yao, et al(张玉秀, 李林峰, 柴团耀, 等). Journal of Botany(植物学报), 2010, 45(4): 506.
[5] YANG Ling-yu, GAO Xiao-hong, ZHANG Wei, et al(杨灵玉, 高小红, 张 威, 等). Journal of Ecology(生态学杂志), 2016, 27(6): 1775.
[6] LI Cong-hui, LAI Zhuang-jie, LIU Jian, et al(李聪慧, 赖壮杰, 刘 健, 等). Journal of Northwest Forestry Academy(西北林学院学报), 2021, 36(6): 195.
[7] Main Russell, Cho Moses Azong, Mathieu Renaud, et al. ISPRS Journal of Photogrammetry and Remote Sensing, 2011, 66(6): 751.
[8] Turner Ii B L, Meyer W B. International Social Science Journal, 1991, 43(129): 669.
[9] Ahamed T, Tian L，Zhang Y, et al. Biomass & Bioenergy, 2011, 35(7): 2455.
[10] WANG Fu-min, HUANG Jing-feng, TANG Yan-lin, et al(王福民, 黄敬峰, 唐延林, 等). China Rice Science(中国水稻科学), 2007, 21(2): 159.
[11] BAI Hui, YANG Feng-bao(白 慧, 杨风暴). Advances in Laser and Optoelectronics(激光与光电子学进展), 2021, 58(5): 280.
[12] Gitelson A A, Keydan G P, Merzlyak M N. Geophysical Research Letters, 2006, 33(11): 431.
[13] Liu Jinbao，Zhang Yang，Wang Huanyuan, et al. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 2018, 199: 43.
[14] LIU Yang, ZHANG Han, FENG Hai-kuan, et al(刘 杨，张 涵，冯海宽，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2021, 41(9): 2657.
[15] JU Yi-lin, JI Yong-jie, HUANG Ji-mao, et al(巨一琳, 姬永杰, 黄继茂, 等). Journal of Nanjing Forestry University(南京林业大学学报), 2022, 46(1): 58.
[16] ZHOU Rong, ZHAO Tian-zhong, WU Fa-yun(周 蓉, 赵天忠, 吴发云). Journal of Northwest Forestry Academy(西北林学院学报), 2022, 37(2): 186.
[17] CHEN Rui-hua, SHANG Tian-hao, ZHANG Jun-hua, et al(陈睿华, 尚天浩, 张俊华, 等). Journal of Applied Ecology(应用生态学报), 2022, 33(4): 922.
[18] ZHOU Yi-an, ZHOU Hao, LAO Cong-cong(周益安, 周 昊, 劳聪聪). Water Saving Irrigation(节水灌溉), 2021，(12): 100.
