热红外光谱的干旱区土壤含盐量遥感反演

doi:10.3964/j.issn.1000-0593(2019)04-1063-07

Abstract
References
Related Citation (15)

Download: PDF (1542 KB)
Export: BibTeX | EndNote (RIS)

Abstract The soil salinization has faced a serious threat to the ecological environment in arid areas, and it is of great significance to quantitative inversion of the salt content of soil by remote sensing technology. In this paper, we gathered the farmland soil and salt crystal in Ebinur lake watershed, to prepare into soil samples with different salt content (the proportion of salt and saline soil: 0.3%~30%) in the laboratory. We measured the thermal infrared emissivity spectral of soil samples using 102F FTIR spectrometer, and through the Planck function fitting to obtain soil emissivity data, and then used the Gaussian filter method for smoothing emissivity curve to eliminate the background and noise effects. The saline soil emissivity spectral curve features were as bellow. The emissivity spectrum curve of soil with different salt content was basically consistent in shape and change tendency, and with the increase of salt content, the value of emissivity increased. Soil salinity factors had inhibitory effect on Reststrahlen absorption characteristics, which would be weakened with the increase of salt content, that presented as the depth of the asymmetric absorption valleys decreased, but the position and width changed a little. Based on the correlation analysis of emissivity and salt content, we found that: It was positively correlated between thermal infrared emissivity and salt content of soil, with the maximum correlation coefficient being 0.899, and the corresponding waveband 9.21 μm; 8.2~10.5 μm was the most sensitive wave bands for soil salinity. Using monadic linear regression, multiple stepwise regression and partial least square method to construct the prediction model, the value of R² were respectively 0.863, 0.879 and 0.958, and RMSE were respectively 3.853%, 3.334% and 1.911%. It was proved that these three kinds of methods had certain prediction ability for salt content of soil, but partial least square was the best method. The thermal infrared wave bands of ASTER, Landsat8 and HJ-1B satellite sensors were chosen for the emissivity spectrum simulation according to the spectral response function of the sensor, and the correlation analysis results showed: ASTER’s B10, B11 and B12 bands are sensitive to the salt factor with thermal infrared spectroscopy and have a high correlation with soil salinity, and their correlation coefficient are up to 0.706, 0.786 and 0.872 respectively. Furthermore, the prediction model of soil salt content based on ASTER thermal infrared wavebands was established through the multiple linear regression method, R² and RMSE of the predicted model was 0.833 and 3.895%. At last, the results showed that: it is feasible to quantitatively inverse salt content of saline soil by satellite thermal infrared remote sensing, which will provide a new way and reference for the remote sensing monitoring of soil salinization in arid areas.

Key words：Soil; Salt content; Thermal infrared spectrum; Emissivity; Waveband simulation

Received: 2018-03-05 Accepted: 2018-08-19

ZTFLH:

TP79

Cite this article:

URL:

https://www.gpxygpfx.com/EN/10.3964/j.issn.1000-0593(2019)04-1063-07 OR https://www.gpxygpfx.com/EN/Y2019/V39/I04/1063

[1] Sreenivas K, Venkataratnam L, Narasimha Rao P V. International Journal of Remote Sensing, 1995, 16(4): 641.
[2] WENG Yong-ling, GONG Peng(翁永玲, 龚鹏). Scientia Geographica Sinica(地理科学), 2006, 26(3): 369.
[3] TIAN Chang-yan, ZHOU Hong-fei, LIU Guo-qing(田长彦, 周宏飞, 刘国庆). Arid Land Geography(干旱区地理), 2000, 23(2): 178.
[4] YANG Jing-song(杨劲松). Soils(土壤), 1995, (1): 23.
[5] Dehaan R L, Taylor G R. Remote Sensing of Environment, 2002, 80: 406.
[6] CUI Bao-shan, LIU Xing-tu(崔保山, 刘兴土). Scientia Geographica Sinica(地理科学), 2001, 21(3): 250.
[7] PENG Xiang, HU Dan, ZENG Wen-zhi, et al(彭翔, 胡丹, 曾文治, 等). Transactions of Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(11): 167.
[8] ZHANG Rui, LI Zhao-fu, PAN Jian(张锐, 李兆富, 潘剑). Transactions of Chinese Society of Agricultural Engineering(农业工程学报), 2017, 33(1): 175.
[9] WEI Chang-long, ZHAO Yu-guo, LI De-cheng, et al(魏昌龙, 赵玉国, 李德成, 等). Transactions of Chinese Society of Agricultural Engineering(农业工程学报), 2014, 30(1): 81.
[10] XIAO Qing, LIU Qin-huo, LI Xiao-wen(肖青, 柳钦火, 李小文, 等). Journal of Infrared and Millimeter Waves(红外与毫米波学报), 2003, 22(5): 373.
[11] ZHANG Yao, LI Min-zan, ZHENG Li-hua, et al(张瑶, 李民赞, 郑立华, 等). Transactions of Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(9): 121.
[12] WEN Yong-ling, QI Hao-ping, FANG Hong-bin, et al(翁永玲, 戚浩平, 方洪宾, 等). Acta Pedologica Sinica(土壤学报), 2010, 47(6): 1255.
[13] CAO Lei, DING Jian-li, UMUT Halik, et al(曹雷, 丁建丽, 玉米提·哈力克, 等). Acta Pedologica Sinica(土壤学报), 2016, 53(6): 1399.
[14] LEI Lei, DING Jian-li, JIANG Hong-nan, et al(雷磊, 丁建丽, 江红南, 等). Journal of Desert Research(中国沙漠), 2013, 33(4): 1104.
[15] DAI Xi-jun, PENG Jie, ZHANG Yan-li, et al(代希君, 彭杰, 张艳丽, 等). Acta Pedologica Sinica(土壤学报), 2016, 53(4): 909.
[16] MA Chi(马驰). Journal of Arid Land Resources and Environment(干旱区资源与环境), 2017, 31(7): 85.
[17] SU Wen, DING Jian-li, YANG Ai-xia(苏雯, 丁建丽, 杨爱霞). China Rural Water and Hydropower(中国农村水利水电), 2017, (2): 9.
[18] DING Jian-li, ZENG Xiao-ji, LI Zheng-guang(丁建丽, 曾小箕, 李争光). Bulletin of Soil and Water Conservation(水土保持通报), 2014, 34(1): 321.
[19] SUN Qian, TASHPOLAT·Tiyip, HOU Yan-jun, et al(孙倩, 塔西甫拉提·特依拜, 侯艳军, 等). China Mining Magazine(中国矿业), 2015, 24(5): 137.
[20] XIA Jun, TASHPOLAT·Tiyip, MAMAT·Sawut, et al(夏军, 塔西甫拉提·特依拜, 买买提·沙吾提, 等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2012, 32(11): 2956.
[21] 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 Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(17): 115.
[22] Carl S, Craig J M. Image Exploitation and Target Recognition, Algorithms for Multispectral, Hyperspectral, and Ultraspectral Imagery Ⅶ, 2001, 4381: 172.
[23] XU Jun, JIANG Jian-jun(徐军, 蒋建军). Journal of Anhui Agricultural Sciences(安徽农业科学), 2012, 40(28): 14099.