Soil moisture inversion in the vegetation-covered area：A case study of Beijing City
JIANG Jinbao1, ZHANG Ling1, CUI Ximin1, CAI Qingkong1, Sun Hao2
1. College of Geoscience and Surveying Engineering, China University of Mining & Technology, Beijing 100083, China;
2. College of Resources Science & Technology, Beijing Normal University, Beijing 100875, China
以北京市为研究区域，联合使用光学遥感数据和雷达数据，对植被覆盖区地表土壤水分进行反演研究。在利用同期光学数据提取出归一化水分指数（normalized differential water index，NDWI）之后，利用water-cloud模型去除植被层在土壤水分后向散射中的贡献，然后考虑到地表粗糙度，在构建后向散射数据库的基础上分别利用HH和HV极化方式的后向散射系数构建土壤水分反演模型，并对反演结果进行对比研究。结果表明，采用HH极化方式反演土壤水分的均方根误差为0.044，相对误差为15.5%；采用HV极化方式反演土壤水分的均方根误差为0.057，相对误差为20.3%；相比而言，HH极化的反演效果更好。
Taking Beijing as the study area, the authors developed a method of soil moisture inversion by using Radar data and optical remote sensing images in the vegetation-covered area. Firstly, NDWI was extracted by using homochronous optical images, and then water-cloud model was used to eliminate the contribution of backscattering coefficients caused by the vegetation. Secondly, HH and HV backscattering coefficients were employed to construct the soil moisture inversion model in consideration of surface roughness based on backscattering database built by AIEM model and Oh model. Then the simulating data were used to validate the accuracy of this model. The result shows that the RMSE and relative error of HH is 0.044 and 15.5%, and the RMSE and relative error of HV is 0.057 and 20.3% respectively. It is proved that the result of using HH backscattering coefficient is much better than that of using HH backscattering coefficient.
蒋金豹, 张玲, 崔希民, 蔡庆空, 孙灏. 植被覆盖区土壤水分反演研究——以北京市为例[J]. 国土资源遥感, 2014, 26(2): 27-32.
JIANG Jinbao, ZHANG Ling, CUI Ximin, CAI Qingkong, Sun Hao. Soil moisture inversion in the vegetation-covered area：A case study of Beijing City. REMOTE SENSING FOR LAND & RESOURCES, 2014, 26(2): 27-32.
[1] 刘伟,施建成,王建明.极化分解技术在估算植被覆盖地区土壤水分变化中的应用[J].遥感信息,2005(4),3-6. Liu W,Shi J C,Wang J M.Applying the decomposition technique in vegetated surface to estimate soil moisture by multitemporal measurements[J].Remote Sensing Information,2005(4),3-6.[2] 周鹏,丁建丽,高婷婷.C波段多极化SAR反演土壤水分研究[J]新疆农业科学,2010,47(7):1416-1420. Zhou P,Ding J L,Gao T T.A study on soil moisture retrieval by C-band multi-polarization SAR[J].Xinjiang Agricultural Sciences,2010,47(7):1416-1420.[3] 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 Y S,Liu L Y,Wang J H.Soil moisture estimation based on optical and microwave remote sensing data[J].Journal of Beijing Normal University:Natural Science,2007,43(3):228-233.[4] Dabrowska-zielinska K,lnoue Y,Kowalik W,et al.Inferring the effect of plant and soil variables on C and L band SAR backscatter over agricultural fields,based on model analysis[J].Advances in Space Research,2007,39(1):139-148.[5] Notarnicola C,Angiulli M,Posa F.Use of Radar and optical remotely sensed data for soil moisture retrieval over vegetated areas[J].IEEE Trans Geosci Remote Sensing,2006,44(4):925-935.[6] Ulaby F,Sarabandi K,Donald K M,et al.Michigan microwave canopy scattering model[J].International Journal of Remote Sensing,1990,11(7):1223-1253.[7] Attema E P W.Vegetation modeled as a water cloud[J].Radio Science,1978,13(2):357-364.[8] De Roger R D,Yang D,Ulaby F T,et al.A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion[J].IEEE Transactions on Geoscience and Remote Sensing.2001,39(4):864-872.[9] Bindlish R,Barros A P.Parameterization of vegetation backscatter in Radar-based soil moisture estimation[J].Remote Sensing of Environment.2001,76(1),130-137.[10] Taconet O,Vidal-madjar D,Emblanch C,et al.Taking into account vegetation effects to estimate soil moisture from C-band Radar measurements[J],Remote Sensing of Environment.1996,56(1),52-56.[11] 余凡,赵英时.ASAR和TM数据协同反演植被覆盖地表土壤水分的新方法[J].中国科学:地球科学,2001,41(4):532-540. Yu F,Zhao Y S.The new way of soil moisture inversion in vegetated areas using ASAR and TM data[J].Science China:Geoscience,2001,41(4):532-540.[12] 张友静,王军战,鲍艳松.多源遥感数据反演土壤水分方法[J].水科学进展,2010,21(2):222-228. Zhang Y J,Wang J Z,Bao Y S.Soil moisture retrieval from multi-resource remotely sensed images over a wheat area[J].Advances in Water Science,2010,21(2):222-228.[13] Shimada M,Isoguchi O,Tadono T,et al.PALSAR radiometric and geometric calibration[J].Geoscience and Remote Sensing.2009,47(12):3915-3932.[14] 周鹏,丁建丽,王飞,等.植被覆盖地表土壤水分遥感反演[J].遥感学报,2010,14(5):966-973. Zhou P,Ding J L,Wang F,et al.Retrieval methods of soil water content in vegetation covering areas based on multi-source remote sensing data[J].Journal of Remote Sensing,2010,14(5):966-973.[15] 刘万侠,刘旭拢,王娟,等.华南农作物覆盖区土壤水分ENVISAT-ASAR与MODIS数据联合反演算法研究[J].干旱区农业研究,2008,26(3):39-43. Liu W X,Liu X L,Wang J,et al.Remote sensing retrieval of soil moisture using ENVISAT-ASAR and MODIS images in vegetated areas of Huanan[J].Agricultural Research in the Arid Areas,2008,26(3):39-43.[16] Bindlish R,Barros A P.Parameterization of vegetation backscatter in Radar-based soil moisture estimation[J].Remote Sensing of Environment.2001,76(1):130-137.[17] 刘小磊,覃志豪.NDWI与NDVI指数在区域干旱监测中的比较分析——以2003年江西夏季干旱为例[J].遥感技术与应用,2007,22(5):608-612. Liu X L,Qin Z H.Comparative analysis between NDWI and NDVI indices in regional drought monitoring[J].Remote Sensing Technology and Application,2007,22(5):608-612.[18] Jackson T J,Chen D Y,Cosh M,et al.Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans[J].Remote Sensing of Environment.2004,92(4):475-482.[19] 曾远文,陈浮,雷少刚,等.基于雷达和光学影像监测土壤表层水分[J].江苏农业科学,2012,40(5):320-323. Zeng Y W,Chen F,Lei S G,et al.Soil moisture monitoring using Radar and optical data[J].Jiangsu Agricultural Sciences,2012,40(5):320-323.
2014, Vol. 26 
