基于高分二号卫星影像高潜水位煤矿区沉陷地土壤含水量监测
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摘要
高潜水位地区煤炭开采破坏导致地表沉陷出现积水和斜坡,沉陷内土壤含水量会分布不均匀,影响农作物的生长,从而严重影响矿区居民的生产和生活。因此大范围快速、精确监测高潜水位地区煤矿开采区的土壤含水量具有重要现实意义。卫星遥感技术可以快速、准确、高效监测矿区土壤含水量。通过遥感手段对高潜水位采煤塌陷地土壤含水量进行监测,探求出一个比较方便、快速、合理监测高潜水位采煤塌陷地土壤含水量分布状况方法,为矿区环境影响评价、农作物估产、破坏等级评价、耕地损害补偿与土地复垦方案的编制提供参考依据。借鉴土壤含水量遥感监测经验,通过野外实地采集土壤样本并测量土壤光谱数据,在室内测量土壤含水量,分析实测地面光谱数据与土壤含水量的变化关系,结合实测的土壤含水量与光谱特征数据,对土壤含水量与实测水体光谱进行相关性分析,得到土壤含水量光谱数据敏感波段范围。结合高分二号卫星影像谱段数据特点,将实测光谱波长按照波段范围划分为与高分二号卫星影像谱段对应的4个波段,即450～520,520～590,630～690,770～890 nm,再取各个波段范围反射率的平均值与土壤含水量光谱反射率进行相关性分析,寻求高分二号卫星影像监测土壤含水量最敏感的波段数据,在确定遥感探测敏感波段的基础上,建立了土壤含水量与光谱反射率的遥感反演模型,即:S曲线模型、逆函数模型,基于预处理的高分二号卫星影像进行沉陷区地土壤含水量遥感反演,从而得到高潜水位采煤塌陷地土壤含水量的空间分布情况。研究结果表明不同土壤含水量的光谱特征基本相似,实测地面光谱数据与土壤含水量的变化关系为土壤光谱反射率随着波长的增长而增大,呈正相关关系;土壤含水量与高分二号卫星影像数据B3波段的反射率具有显著的负相关关系,可将B3波段作为监测土壤含水量最敏感的波段;通过对S曲线模型、逆函数模型进行分析与检验,S曲线模型比逆函数模型更接近实测值;基于高分二号遥感影像,利用S曲线模型进行遥感反演,可以迅速得到高潜水位采煤塌陷地土壤含水量空间等级分布图。
Coal mining could destroy and lead to surface subsidence resulting in water accumulation and land sloping in the areas with a high submersible water level.The water content in the subsided soil will not be evenly distributed,which seriously affects the growth of crops,and the life of residents in the mining area.Therefore,it is of practical significance to monitor the soil water content in the coal mining area with a high submersible water level. Satellite RS technology can quickly and accurately monitor the soil moisture content of mining areas.The RS technology is used to monitor the soil water content in the coal mining area with a high submersible water level,and to find a more convenient,rapid and reasonable method for monitoring the distribution of soil water content in the coal mining area with a high submersible water level.It provides a reference for the environmental impact assess-ment of mining areas,crop yield estimation,damage grade evaluation,farmland damage compensation and the compilation of land reclamation plan.Based on the experience of RS monitoring of soil moisture,the soil samples in the field are collected,the soil spectral data are measured and the relationship between the measured ground spectral data and soil moisture is analyzed by indoor soil moisture measurement.Combining the measured soil water content and spectral characteristic data,the correlation between soil water content and measured water spectrum is analyzed to obtain the sensitive band range of soil water content spectral data. According to the characteristics of GF-2 image band data,the measured spectral wavelength is divided into four bands corresponding to the image band of GF-2,namely 450-520,520-590,630-690 and 770-890 nm.Then the correlation between the average reflectance of each band and the spectral reflectance of soil moisture content is analyzed.The most sensitive band data for monitoring soil water content with GF-2 is found.On the basis of determining the sensitive band of RS detection,the RS inversion models of soil water content and spectral reflectance are established,which are S curve model and inverse function model.Then the RS inversion of soil water content in subsidence area is carried out through the pre-processed GF-2,and the spatial distribution of soil water content in coal mining subsidence area with a high groundwater level is obtained.The results show that the spectral characteristics of different soil moisture content are basically similar,and the relationship between measured surface spectral data and soil moisture content demonstrates that the soil spectral reflectance increases with the increase of wavelength and the soil moisture content has a significant negative correlation with the reflectance of GF-2 image data in B3.Therefore,the B3 band can be used as the most sensitive band for monitoring soil water content.Through the analysis and test of S-curve model and inverse function model,the results indicate that S-curve model is closer to the measured value than inverse function model.Based on the RS image of GF-2,using S-curve model for RS inversion,the spatial grade distribution map of soil water content in coal mining subsidence area with a high groundwater level can be quickly obtained.
Coal mining could destroy and lead to surface subsidence resulting in water accumulation and land sloping in the areas with a high submersible water level.The water content in the subsided soil will not be evenly distributed,which seriously affects the growth of crops,and the life of residents in the mining area.Therefore,it is of practical significance to monitor the soil water content in the coal mining area with a high submersible water level. Satellite RS technology can quickly and accurately monitor the soil moisture content of mining areas.The RS technology is used to monitor the soil water content in the coal mining area with a high submersible water level,and to find a more convenient,rapid and reasonable method for monitoring the distribution of soil water content in the coal mining area with a high submersible water level.It provides a reference for the environmental impact assess-ment of mining areas,crop yield estimation,damage grade evaluation,farmland damage compensation and the compilation of land reclamation plan.Based on the experience of RS monitoring of soil moisture,the soil samples in the field are collected,the soil spectral data are measured and the relationship between the measured ground spectral data and soil moisture is analyzed by indoor soil moisture measurement.Combining the measured soil water content and spectral characteristic data,the correlation between soil water content and measured water spectrum is analyzed to obtain the sensitive band range of soil water content spectral data. According to the characteristics of GF-2 image band data,the measured spectral wavelength is divided into four bands corresponding to the image band of GF-2,namely 450-520,520-590,630-690 and 770-890 nm.Then the correlation between the average reflectance of each band and the spectral reflectance of soil moisture content is analyzed.The most sensitive band data for monitoring soil water content with GF-2 is found.On the basis of determining the sensitive band of RS detection,the RS inversion models of soil water content and spectral reflectance are established,which are S curve model and inverse function model.Then the RS inversion of soil water content in subsidence area is carried out through the pre-processed GF-2,and the spatial distribution of soil water content in coal mining subsidence area with a high groundwater level is obtained.The results show that the spectral characteristics of different soil moisture content are basically similar,and the relationship between measured surface spectral data and soil moisture content demonstrates that the soil spectral reflectance increases with the increase of wavelength and the soil moisture content has a significant negative correlation with the reflectance of GF-2 image data in B3.Therefore,the B3 band can be used as the most sensitive band for monitoring soil water content.Through the analysis and test of S-curve model and inverse function model,the results indicate that S-curve model is closer to the measured value than inverse function model.Based on the RS image of GF-2,using S-curve model for RS inversion,the spatial grade distribution map of soil water content in coal mining subsidence area with a high groundwater level can be quickly obtained.
引文
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[19]吴见,刘民士,李伟涛.干旱半干旱区土壤含水量定量反演技术研究[J].干旱区资源与环境,2014,28(1):26-31.WU Jian,LIU Minshi,LI Weitao.Research on soil moisture content inversion technologies in arid and semiarid area[J]. Journal of Arid Land Resources and Environment,2014,28(1):26-31.
[20]麦霞梅,赵艳玲,龚碧凯,等.东滩煤矿高潜水位采煤塌陷地土壤含水量变化规律研究[J].中国煤炭,2011,37(3):48-51.MAI Xiamei,ZHAO Yanling,GONG Bikai,et al.Soil moisture variation in high ground water level coal mining subsidence in Dongtan Coal Mine[J].China Coal,2011,37(3):48-51.
[2]胡振琪,多玲花,王晓彤.采煤沉陷地夹层式充填复垦原理与方法[J].煤炭学报,2018,43(1):198-206.HU Zhenqi,DUO Linghua,WANG Xiaotong.Principle and method of reclaiming subsidence land with inter-layers of filling materals[J].Journal of China Coal Society,2018,43(1):198-206.
[3]常庆瑞.遥感技术导论[M].北京:科学出版社,2004.
[4] JACKSON R D,IDSO S B,REGINATO R J.Canopy temperature as a crop water stress indicator[J]. Water Resource,1981,17(1):133-138.
[5]卞正富,雷少刚,常鲁群,等.基于遥感影像的荒漠化矿区土壤含水率的影响因素分析[J].煤炭学报,2009,34(4):520-525.BIAN Zhengfu,LEI Shaogang,CHANG Luquan,et al. Affecting factors analysis of soil moisture for arid mining subsidence area based on TM images[J].Journal of China Coal Society,2009,34(4):520-525.
[6] ULABY F T,DUBOIS P C,VAN ZYL J.Radar mapping of surface soil moisture[J].Journal of Hydrology,1996,184(1-2):57-84.
[7] SHI J C,WANG J,HSU A Y,et al Estimation of bare surface soil moisture and surface roughness parameter using L-band SAR image data[J].IEEE Transactions on Geoscience and Remote Sensing,1997,35(5):1254-1266.
[8] ALVAREZ-MOZOS J,CASALI J,GONZALEZ-AUDICANA M,et al.Assessment of the operational applicability of RADARSAT-1 data for surface soil moisture estimation[J].IEEE Transactions on Geoscience and Remote Sensing,2006,44(4):913-924.
[9]鲍艳松,刘良云,王纪华.综合利用光学、微波遥感数据反演土壤湿度研究[J].北京师范大学学报(自然科学版),2007,43(3):228-233.BAO Yansong,LIU Liangyun,WANG Jihua. Soil moisture estimation based on optical and microwave remote sensing data[J]. Journal of Beijing Normal University(Natural Science),2007,43(3):228-233.
[10] CLARA S Draper,JEFFREY P Walker,PETER J Steinle,et al.An evaluation of AMSR derived soil moisture over Australia[J]. Remote Sensing of Environment,2009,113:703-710.
[11]杨立娟,武胜利,张钟军.利用主被动微波遥感结合反演土壤水分的理论模型分析[J].国土资源遥,2011(2):53-58.YANG Lijuan,WU Shengli,ZHANG Zhongjun. A model analysis using a combined active/passive microwave remote sensing approach for soil moisture retrieval[J]. Remote Sensing for Land&Resources,2011(2):53-58.
[12]罗时雨,童玲,陈彦.全极化SAR图像的山地低矮植被区域土壤含水量估计[J].遥感学报,2017,21(6):907-916.LUO Shiyu,TONG Ling,CHEN Yan. Soil moisture estimation for mountainous areas covered with low vegetation using fully polar metric SAR images[J]. Journal of Remote Sensing,2017,21(6):907-916.
[13]张穗,谭德宝,王宝忠,等.TM遥感影像监测裸露土壤含水率的方法研究[J].长江科学院院报,2008,25(1):34-36.ZHANG Sui,TANG Debao,WANG Baozhong,et al. Method of measuring soil moisture with thematic mapper data[J].Journal of Yangtze River Scientific Research Institute,2008,25(1):34-36.
[14]丁春林.济阳县潮土光谱特性及含水量估测[D].泰安:山东农业大学,2018.DING Chunlin. Soil moisture spectral characteristics and moisture content estimation in fluvo-aquic soil in Jiyang County[D].Taian:Shandong Agricultural University,2018.
[15]魏珍.基于LandsatETM遥感数据的大柳塔煤炭开发区土壤水分信息提取[D].西安:长安大学,2010.WEI Zhen. The extraction of soil moisture information based on landsat ETM remote sensing data in coal mining zone of Daliuta[D].Xi’an:Chang’an University,2010.
[16]张鹰,张东,胡平香.海岸带潮滩土壤含水量遥感测量[J].海洋学报,2008,30(5):29-33.ZHANG Ying,ZHANG Dong,HU Pingxiang. Water content of soil on outcrop beach measured by remote sensing[J].Acta Oceanologica Sinica,2008,30(5):29-33.
[17]张俊平,胡月明,刘素萍,等.土壤水分空间变异研究综述[J].土壤通报,2009,40(3):683-390.ZHANG Junping,HU Yueming,LIU Suping,et al. A review on soil moisture spatial variability[J].Chinese Journal of Soil Science,2009,40(3):683-390.
[18]李琴,陈曦,FRANK Veroustraete,等.干旱半干旱区土壤含水量反演与验证[J].水科学进展,2010,21(2):201-207.LI Qin,CHEN Xi,FRANK Veroustraete,et al. Validation of soil moisture retrieval in arid and semi-arid areas[J].Advances in Water Science,2010,21(2):201-207.
[19]吴见,刘民士,李伟涛.干旱半干旱区土壤含水量定量反演技术研究[J].干旱区资源与环境,2014,28(1):26-31.WU Jian,LIU Minshi,LI Weitao.Research on soil moisture content inversion technologies in arid and semiarid area[J]. Journal of Arid Land Resources and Environment,2014,28(1):26-31.
[20]麦霞梅,赵艳玲,龚碧凯,等.东滩煤矿高潜水位采煤塌陷地土壤含水量变化规律研究[J].中国煤炭,2011,37(3):48-51.MAI Xiamei,ZHAO Yanling,GONG Bikai,et al.Soil moisture variation in high ground water level coal mining subsidence in Dongtan Coal Mine[J].China Coal,2011,37(3):48-51.