GF-5高光谱遥感影像的土壤有机质含量反演估算研究

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Abstract Based on the hyperspectral remote sensing images inversion, this paper estimated the soil organic matter content of the plain in the Harbin and Xing’an League boundary. Firstly, from the Gao-Fen 5(GF-5) hyperspectral remote sensing image, which has been pre-processing such as radiation and geometric correction, 100 groups of surface soil samples were collected by the five-point sampling method, and a series of physical and chemical analysis in the laboratory were performed to obtain the soil organic matter content. Then, the partial least squares regression method was applied to establish an estimation model of soil organic matter content by hyperspectral image soil desertification index, soil degradation index, normalized brightness index and soil salinity index. The prediction accuracy of the inversion model based on the original reflectance data, one differential reflectance data, and four soil indices were compared. From 65% modeling samples and 35% prediction samples it showed that the inversion model based on the soil index in the inversion model had the highest prediction accuracy. In the verification of the prediction group, ρ was 0.816 and the RMSE was 1.7287. Finally, the inversion model was applied to the inversion estimation of soil organic matter content by hyperspectral imagery. The actual measured SOM was consistent with the image inversion SOM content change trend, and the correlation reached 80.023%, which verified the accuracy of the model's inversion estimation.

Key words： GF-5 hyperspectral remote sensing partial least squares regression soil index soil organic matter inversion estimation

Received: 27 December 2019

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X53

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	ZHAO Rui
	CUI Xi-min
	LIU Chao

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ZHAO Rui,CUI Xi-min,LIU Chao. Inversion estimation of soil organic matter content based on GF-5 hyperspectral remote sensing image[J]. CHINA ENVIRONMENTAL SCIENCECE, 2020, 40(8): 3539-3545.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2020/V40/I8/3539

[1]	刘占峰,傅伯杰.土壤质量与土壤质量指标及其评价[J].生态学报, 2006,26(3):902-903. Liu Z F, Fu B J. Soil quality:Concept, indicators and its assessment[J]. Acta Ecologica Sinica, 2006,26(3):902-903.
[2]	张娟娟,田永超.不同类型土壤的光谱特征及其有机质含量预测[J].中国农业科学, 2009,42(9):3154-3163. Zhang J J, Tian Y C. Spectral characteristics and estimation of organic matter contents of differents soil types[J]. Scientia Agricultural Sinica, 2009,42(9):3154-3163.
[3]	方少文,杨梅花,赵小敏,等.红壤区土壤有机质光谱特征与定量估算--以江西省吉安县为例[J].土壤学报, 2014,51(5):1003-1010. Fang S W,Yang M H, Zhao X M, et al. Spectral characteristics and quantitative estimation of SOM in red soil typical of Ji'an county, Jiangxi Province[J]. Acta Pedological Sinica, 2014,51(5):1003-1010.
[4]	文瑶,李明赞,赵毅,等.玉米苗期冠层多光谱反射率与叶绿素含量诊断[J].农业工程学报, 2015,31(增刊2):193-199. Wen Y, Li M Z, Zhao Y, et al. Multispectral reflectance inversion and chlorophyll content diagnosis of maize at seeding stage[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(Supp.2):193-199.
[5]	程龙,杨可明,王晓峰,等.作物重金属铜污染的HHT边际谱特征与污染预测模型[J].中国环境科学, 2018,38(1):340-347. Cheng L, Yang K M, Wang X F, et al. Characteristic changes of HHT marginal spectra and pollution predicting models on crop polluted by the heavy metal copper[J]. China Environmental Science, 2013,33(4):599-604.
[6]	雷雨,韩德俊,曾庆东,等.基于高光谱成像技术的小麦条锈病病害程度分级方法[J].农业机械学报, 2018,49(5):226-232. Lei Y, Han D J, Zeng Q D, et al. Grading method of disease severity of wheat stripe rust using hyperspectral imaging technology[J]. Transactions of the Chinese Society for Agricultural Machinery, 2018,226-232.
[7]	Rossel R A V, Webster R. Predicting soil properties from the Australian soil visible-near infrared spectroscopic database[J]. European Journal of Soil Science, 2012,63(6):848-860.
[8]	李媛媛,李微,刘远,等.基于高光谱遥感土壤有机质含量预测研究[J].土壤通报, 2014,45(6):1313-1318. Li Y Y, Li W, Liu Y, et al. Study on the prediction of soil organic matter content based on hyperspectral remote sensing[J]. Chinese Journal of Soil Science, 2014,45(6):1313-1318.
[9]	杨长保,李东辉,刘津怿,等.小波包分析在Hyperion数据提取农田土壤有机质含量中的应用研究[J].应用基础与工程科学学报, 2017, 25(5):869-879. Yang C B, Li D H, Liu J Y et al. The study of extracting farmland soil organic matter content from Hyperion data by the wavelet packet analysis[J]. Journal of basic science and engineer, 2017,25(5):869-879.
[10]	邱壑,陈瀚阅,邢世和,等.基于Hyperion数据的耕地土壤有机质含量遥感反演[J].福建农林大学学报(自然科学版), 2017,46(4):460-467. Qiu He, Chen H Y, Xing S H, et al. Soil organic matter estimation models based on Hyperion data[J]. Journal of Fujian Agriculture and Forestry University (Natural Science Edition), 2017,46(4):460-467.
[11]	刘焕军,潘越,窦欣,等.黑土区田块尺度土壤有机质含量遥感反演模型[J].农业工程学报, 2018,34(1):127-133. Liu H J, Pan Y, Dou X, et al. Soil organic matter content inversion model with remote sensing image in field scale of blacksoil area[J]. Transactions of the Chinese Society of Agricultural Engineering, 2018, 34(1):127-133.
[12]	刘效栋.基于高光谱遥感的黄土高原丘陵沟壑区土壤有机质含量估测模型研究[J].西部大开发(土地开发工程研究), 2018,3(12):13-18. Liu XD. The study on hyperspectral characteristics and inversion of SOM in the hill and ravine region of the loess plateau[J]. Land development and engineering research, 2018,3(12):13-18.
[13]	于雷,洪永胜,耿雷,等.基于偏最小二乘回归的土壤有机质含量高光谱估算[J].农业工程学报, 2015,31(14):103-109. Yu L, Hong Y S, Geng L, et al. Hyperspectral estimation of soil organic matter content based on partial least squares regression[J]. Transactions of the Chinese Society of Agricultural Engineering, 2015, 31(14):103-109.
[14]	徐明星,周生路,丁卫,等.苏北沿海滩涂地区土壤有机质含量的高光谱预测[J].农业工程学报, 2011,27(2):219-223. Xu M X, Zhou S L, Ding W, et al. Hyperspectral reflectance models for predicting soil organic matter content in coastal tidal land area, northern Jiangsu[J]. Transactions of the Chinese Society of Agricultural Engineering, 2011,27(2):219-223.
[15]	张东,塔西甫拉提×特依拜,张飞,等.分数阶微分在盐渍土高光谱数据预处理中的应用[J].农业工程学报, 2014,30(24):151-160. Zhang D, Tashpolat×T, Zhang F, et al. Application of fractional differential in preprocessing hyperspectral data of saline soil[J]. Transactions of the Chinese Society of Agricultural Engineering, 2014, 30(24):151-160.
[16]	Krishnan P, Alexander J D, Butler B J, et al. Reflectance technique for predicting soil organic matter[J]. Soil Science Society of America Journal, 1980,44(6):1282-1285.
[17]	Hummel J W, Sudduth K A, Hollinger S E. Soil moisture and organic matter prediction of surface and subsurface soils using an NIR soil sensor[J]. Computers and Electronics in Agriculture, 2001,32(2):149-165.
[18]	Conforti M, Buttafuoco G, Leone A P, et al. Studying the relationship between water-induced soil erosion and soil organic matter using Vis-NIR spectroscopy and geomorphological analysis:A case study in Southern Italy[J]. Catena, 2013,110:44-58.
[19]	李晓明,韩霁昌,李娟.典型半干旱区土壤盐分高光谱特征反演[J].光谱学与光谱分析, 2014,34(4):1081-1084. Li X M, Han J C, Li J. Research on hyperspectral inversion of soil salinity in typical semiarid area[J]. Spectroscopy and Spectral Analysis, 2014,34(4):1081-1084.
[20]	Hossein S, Jan A, Vahid T, et al. A new approach for regional scale interrill and rill erosion intensity mapping using brightness index assessments from medium resolution satellite images[J]. Catena, 2014,113:306-313.
[21]	Fanny Diane A B, Hugues Y G, Philippe D, et al. Are NIR spectra useful for predicting site indices in sandy soils under Eucalyptus stands in Republic of Congo[J]. Forest Ecology and Management, 2011,266:126-137.
[22]	Mohamed A E, Mohamed M, Adel S, et al. Quantitative assessment of soil saline degradation using remote sensing indices in Siwa Oasis[J]. Remote Sensing Applications:Society and Environment, 2019,13(1):53-60.
[23]	Mohammad A K, Kihaachi U, Sake H, et al. CIELAB color variables as indicators of compost stability[J]. Waste Management, 2009,29(12):2969-2975.