基于SVM的绿洲荒漠交错带土壤水分与地下水埋深反演
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摘要
为深入研究浅层地下水、植被和土壤的相互作用,以新疆渭干河-库车河绿洲为研究区,通过Sentinel-1A数据和Landsat数据以及土壤含水率、地下水埋深数据,结合植被以及土壤条件,通过支持向量机模型(Support vector machine,SVM)定量反演研究区土壤水分以及地下水埋深信息。结果表明:0～10 cm的土壤含水率与地下水埋深之间的相关性最高。通过地形校正C模型(Topographic correction model),得到温度植被干旱指数(Temperature vegetation drought index,TVDI)精度有所提高。建立不同参数的SVM模型反演地下水埋深可行,对于单因子建模,TVDI_(MSAVI)构建的模型精度最高,建模集R~2=0. 74,均方根误差(Root mean square error,RMSE)为4. 66%,验证集R~2=0. 70,RMSE为4. 65%。相比只考虑单因子(后向散射系数(σ_(soil)~0)或TVDI),σ_(soil)~0和TVDI_(MSAVI)组合共同作用于模型精度最好,建模集R~2=0. 86,RMSE为4. 16%,验证集R~2=0. 92,RMSE为2. 73%。利用最优模型参数结果反演土壤水分区域和地下水埋深区域,其结果精度较好。地下水埋深反演结果平均相对误差为8. 23%,优于研究区以往研究18. 06%的结果。
In order to further study the interaction between shallow groundwater,vegetation and soil of arid and semi-arid regions,the database of the Sentinel-1 A,Landsat images,soil moisture and the groundwater depth were utilized to quantitatively analyze the information of soil moisture and groundwater depth in the study area by the model of support vector machine( SVM) regression algorithm.Furthermore,the comparison of optical remote sensing and microwave remote sensing collaborative inversion in soil moisture and groundwater depth was also analyzed. By the survey of soil moisture and groundwater depth in the study area,the results indicated that the highest accuracy in SVM model was the correlation between soil water content in 0 ～ 10 cm and groundwater depth. The accuracy of temperature vegetation drought index( TVDI) was improved,through the C calibration model. It was feasible to invert the groundwater depth by SVM model with different parameters. For single factor modeling,the model constructed by TVDI_(MSAVI)had the highest accuracy and the R~2 of modeling set was0. 74,the value of RMSE was 4. 66%,and the R~2 of verification set was 0. 70,the value of RMSE was4. 65%,compared with only single factor( σ_(soil)~0or TVDI),σ_(soil)~0and TVDI_(MSAVI)combination work with the highest model accuracy,R~2 was 0. 86,and RMSE was 4. 16%,the R~2 of verification set was 0. 92,and RMSE was 2. 73%. The results of the optimal model parameters were used to retrieve the soil moisture and groundwater with good accurate. The average relative error of groundwater was 8. 23%,which was better than the previous results of the study area of 18. 06%.
In order to further study the interaction between shallow groundwater,vegetation and soil of arid and semi-arid regions,the database of the Sentinel-1 A,Landsat images,soil moisture and the groundwater depth were utilized to quantitatively analyze the information of soil moisture and groundwater depth in the study area by the model of support vector machine( SVM) regression algorithm.Furthermore,the comparison of optical remote sensing and microwave remote sensing collaborative inversion in soil moisture and groundwater depth was also analyzed. By the survey of soil moisture and groundwater depth in the study area,the results indicated that the highest accuracy in SVM model was the correlation between soil water content in 0 ～ 10 cm and groundwater depth. The accuracy of temperature vegetation drought index( TVDI) was improved,through the C calibration model. It was feasible to invert the groundwater depth by SVM model with different parameters. For single factor modeling,the model constructed by TVDI_(MSAVI)had the highest accuracy and the R~2 of modeling set was0. 74,the value of RMSE was 4. 66%,and the R~2 of verification set was 0. 70,the value of RMSE was4. 65%,compared with only single factor( σ_(soil)~0or TVDI),σ_(soil)~0and TVDI_(MSAVI)combination work with the highest model accuracy,R~2 was 0. 86,and RMSE was 4. 16%,the R~2 of verification set was 0. 92,and RMSE was 2. 73%. The results of the optimal model parameters were used to retrieve the soil moisture and groundwater with good accurate. The average relative error of groundwater was 8. 23%,which was better than the previous results of the study area of 18. 06%.
引文
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[4]刘广明,杨劲松,何丽丹,等.基于模糊综合评判法的新疆典型干旱区土壤盐漠退化风险评价[J].农业工程学报,2011,27(3):1-5.LIU Guangming, YANG Jinsong, HE Lidan, et al. Fuzzy comprehensive evaluation based assessment of soil alkaline desertification in typical arid area of Xinjiang[J]. Transactions of the CSAE,2011,27(3):1-5.(in Chinese)
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[11]苏凯,岳德鹏,YANG Di,等.基于IL-HMMs预测模型的地下水埋深预测研究[J/OL].农业机械学报,2017,48(12):263-268.SU Kai,YUE Depeng,YAND Di,et al. Groundwater depth forecast based on IL-HMMs[J/OL]. Transactions of the Chinese Society for Agricultural Machinery,2017,48(12):263-268. http:∥www. j-csam. org/jcsam/ch/reader/view_abstract.aspx? file_no=20171231&flag=1. DOI:10. 6041/j. issn. 1000-1298. 2017. 12. 031.(in Chinese)
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[13]付俊娥,苏庆珣,潘世兵,等.基于支持向量机理论的地下水动态遥感监测模型与应用[J].地球信息科学学报,2010,12(4):466-472.FU Jun'e,SU Qingxun,PAN Shibing,et al. Support vector machine based groundwater level monitoring model by using remote sensing images[J]. Journal of Geo-information Science,2010,12(4):466-472.(in Chinese)
[14]尹涛,王瑞燕,杜文鹏,等.黄河三角洲地区植被生长旺盛期地下水埋深遥感反演[J].灌溉排水学报,2018,37(2):95-100.YIN Tao,WANG Ruiyan,DU Wenpeng,et al. Remote sensing inversion of groundwater level in the Yellow river delta during plants thrive[J]. Journal of Irrigation and Drainage,2018,37(2):95-100.(in Chinese)
[15]田凯,李小青,康相武,等.基于MODIS数据研究黄河中上游区域地下水对地表植被影响的方法[J].高技术通讯,2009,19(11):1201-1205.TIAN Kai,LI Xiaoqing,KANG Xiangwu,et al. Research on the influence of groundwater on vegetation development in the upper-middle reaches of the Yellow river based on modis sensing data[J]. High Technology Letters,2009,19(11):1201-1205.(in Chinese)
[16]毛德发,周会珍,胡明罡,等.区域节水效果的遥感评价方法研究与应用[J].遥感学报,2011,15(2):340-348.MAO Defa,ZHOU Huizhen,HU Minggang,et al. Application and study on evaluation method of the overall effect of water saving efforts in a region based on remote sensing[J]. Journal of Remote Sensing,2011,15(2):340-348.(in Chinese)
[17] RODELL M,FAMIGLIETTI J S. The potential for satellite-based monitoring of groundwater storage changes using GRACE:the high plains aquifer,Central US[J]. Journal of Hydrology,2002,263:245-256.
[18] KOMAROV S A,MIRONOV V L. Remote sensing of the water table:measurement and a data processing algorithm[J].Mapping Sciences&Remote Sensing,1999,36(1):1-10.
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