Retrieval of soil moisture in Zhangye Prefecture based on Radarsat-2 data

doi:10.7523/j.issn.2095-6134.2018.03.007

Abstract

Abstract: Soil moisture is an important parameter in hydrology, agriculture, ecology, and many other research fields. A simplified model for the retrieval of soil moisture is established in the paper. The simplified model improves inversion accuracy by incorparating the characteristics of qual polarization radar data and eliminates roughness in model. The results show that VV/HH, VV/VH, and HH/HV can be used in the inversion of soil moisture in the bare soil region and the VV/VH polarization combination performs the best. In this paper we use Radarsat-2 data to retrieve the soil moisture in bare soil and plant areas in the middle basin of Heihe River and use field survey data to verify the results. The results show that the VV/VH polarization combination inversion results are in good agreement with the field survey results and the RMSE values in the bare and plant areas are 0.006 and 0.017cm³·cm^-3, respectively. The VV/VH polarization can be used to invert the soil water content in the bare soil region. This work has laid a foundation for quick and accurate acquirement of the regional soil moisture.

Key words: soil moisture, Radarsat-2, experience model, AIEM

CLC Number:

S152.7

WANG Ruixin, SONG Xiaoning, MA Jianwei, SUN Chuan. Retrieval of soil moisture in Zhangye Prefecture based on Radarsat-2 data[J]. , 2018, 35(3): 327-335.

References

[1] 彭记永, 方文松. 郑州地区草地土壤水分时空变化特征及对气候的响应分析[J]. 湖南农业科学, 2013, 322(19):85-87.
[2] 彭小燕. 淮河流域径流模拟试验:对陆面水文模式参数化方案的改进[D]. 南京:南京信息工程大学, 2005.
[3] 刘桂林, 张落成, 刘剑, 等. 基于Landsat TM影像的水体信息提取[J]. 中国科学院大学学报, 2013, 30(5):644-650.
[4] 曹广真, 卢乃锰, 侯鹏, 等. 遥感湖泊面积MTMF提取方法及空间尺度效应研究[J]. 遥感技术与应用, 2016, 31(5):864-871.
[5] 贾德伟, 周磊, 黄灿辉, 等. 基于可见光和红外遥感的农业干旱监测方法研究进展[J]. 科技创新与应用, 2016, 180(32):13-15.
[6] Leng P, Song X N, Li Z L, et al. Bare surface soil moisture retrieval from the synergistic use of optical and thermal infrared data[J]. International Journal of Remote Sensing, 2014, 35(3):988-1003.
[7] Ma J W, Song X N, Li X, et al. Estimation of surface soil moisture from ASAR dual-polarized data in the middle stream of the Heihe River Basin[J]. Wuhan University Journal of Natural Sciences, 2013, 18(2):163-170.
[8] 罗甫林. 基于流形学习和稀疏表示的高光谱遥感影像分类研究[D]. 重庆:重庆大学, 2013.
[9] 李森. 基于IEM的多波段、多极化SAR土壤水分反演算法研究[D]. 北京:中国农业科学院, 2007.
[10] 徐金鸿, 徐瑞松, 夏斌, 等. 土壤遥感监测研究进展[J]. 水土保持研究, 2006, 13(2):17-20.
[11] 姜良美. 基于微波遥感农田土壤水分反演研究[D]. 湘潭:湖南科技大学, 2012.
[12] 李斌, 李震, 魏小兰. 基于微波遥感和陆面模型的流域土壤水分研究[J]. 遥感信息, 2007, 93(5):96-101.
[13] 杜今阳. 多极化雷达反演植被覆盖地表土壤水分研究[D]. 北京:中国科学院遥感应用研究所, 2006.
[14] Chen K S, Wu T D, Tsay M K, et al. Note on the multiple scattering in an IEM model[J]. IEEE Transactions on Geoscience & Remote Sensing, 2000, 38(1):249-256.
[15] Oh Y, Sarabandi K, Ulaby F T. An empirical model and an inversion technique for radar scattering from bare soil surfaces[J]. IEEE Transactions on Geoscience & Remote Sensing, 1992, 30(2):370-381.
[16] Dubois P C, Van Zyl J, Engman T. Measuring soil moisture with imaging radars[J]. IEEE Transactions on Geoscience & Remote Sensing, 1995, 33(4):915-926.
[17] Shi J, 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 & Remote Sensing, 1997, 35(5):1254-1266.
[18] Bindlish R, Barros A P. Multi-frequency soil moisture inversion from SAR measurements using IEM[J]. Remote Sensing of Environment, 2000, 71(1):67-88.
[19] 余凡, 赵英时. 合成孔径雷达反演裸露地表土壤水分的新方法[J]. 武汉大学学报(信息科学版), 2010, 35(3):317-321.
[20] 李爱香. 黑河中游生态变化对策研究[J]. 农业科技与信息, 2015, 454(5):83-85.
[21] 李新, 马明国, 王建, 等. 黑河流域遥感-地面观测同步试验:科学目标与试验方案[J]. 地球科学进展, 2008, 23(9):897-914.
[22] Yu F, Zhao Y S. A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas[J]. Science China Earth Sciences, 2011, 54(12):1955-1964.
[23] 邓科. 大斜视机载SAR成像算法[D]. 南京:南京邮电大学, 2013.
[24] 周宇星. 天基红外测量相机辐射定标及误差估计方法研究[D]. 哈尔滨:哈尔滨工业大学, 2014.
[25] 鲍艳松, 刘良云, 王纪华, 等. 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.
[26] 王树果, 董存辉, 马春锋,等. 黑河生态水文遥感试验:黑河流域中游盈科绿洲与花寨子荒漠机载PLMR地面同步观测数据集(2012年7月7日)[DB/OL].[2017-03-30]. 中国科学院寒区旱区环境与工程研究所; 中国科学院大学; 中国气象局兰州干旱气象研究所; 西北师范大学. 2012. doi:10.3972/hiwater.053.2013.db.
[27] 马春锋, 李大治, 方苗, 等. 黑河流域中游大满超级站TerraSAR-X地面同步观测数据集(2012年6月26日)[DB/OL] [2017-03-30]. 中国科学院寒区旱区环境与工程研究所; 中国科学院大学; 西北师范大学. 2012. doi:10.3972/hiwater.049.2013.db.
[28] 万曙静, 张承明, 马靖. 微波遥感反演地表土壤含水量的方法研究[J]. 山东农业大学学报(自然科学版), 2015, 46(2):221-227.
[29] 钟若飞, 郭华东, 王为民, 等. SZ-4飞船多模态传感器辐射模态数据模拟验证[C]//2004环境遥感学术年会. 长沙:中国地理学会, 2004:231-237.
[30] 刘增灿. 微波散射测量及土壤水分反演研究[D]. 成都:电子科技大学, 2009.
[31] 谭婕, 汪学锋. 海洋风速动态仿真及其在平台模拟系统中的应用[J]. 中国造船, 2014, 208(1):149-157.
[32] 陈长红. 基于RADARSAT-2的风沙滩地区地表土壤水分遥感反演[D]. 西安:长安大学, 2014.
[33] Ulaby F T, Moore R K, Fung A K. Microwave remote sensing:active and passive. Volume 1:Microwave remote sensing fundamentals and radiometry[M]. Boston:Addison-Wesley, 1981.
[34] Zribi M, Dechambre M. A new empirical model to retrieve soil moisture and roughness from C-band radar data[J]. Remote Sensing of Environment, 2002, 84(1):42-52.
[35] Ulaby F T, Sarabandi K, Mcdonald K, et al. Michigan microwave canopy scattering model.[J]. International Journal of Remote Sensing, 1990, 11(7):1223-1253.
[36] Attema E P W, Ulaby F T. Vegetation modeled as a water cloud[J]. Radio Science, 1978,13(2):357-364.
[37] 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.
[38] 金梦婷, 基于合成孔径雷达卫星数据反演表层土壤含水量的研究[D]. 杭州:杭州师范大学, 2016.
[39] 闻熠, 黄春林, 卢玲, 等. 基于ASTER数据黑河中游植被含水量反演研究[J]. 遥感技术与应用, 2015, 30(5):876-883.
[40] Ma M, Che T, Li X, et al. A prototype network for remote sensing validation in China[J]. Remote Sensing, 2015, 7(5):5187-5202.
[41] Mu X, Huang S, Ren H, et al. Validating GEOV1 fractional vegetation cover derived from coarse-resolution remote sensing images over croplands[J]. Selected Topics in Applied Earth Observations & Remote Sensing IEEE Journal of, 2015, 8(2):439-446.
[42] Song W, Mu X, Yan G, et al. Extracting the green fractional vegetation cover from digital images using a shadow-resistant algorithm (SHAR-LABFVC)[J]. Remote Sensing, 2015, 7(8):10425-10443.
[43] Li X, Liu S, Xiao Q, et al. A multiscale dataset for understanding complex eco-hydrological processes in a heterogeneous oasis system[J]. Scientific Data, 2017, 4:170083.