Soil moisture inversion in highland areas based on Sentinel-1 and Landsat 8 remote sensing data

doi:10.13474/j.cnki.11-2246.2024.0725

Abstract

Abstract: Soil moisture is a crucial parameter for agricultural production, water resource management, global climate, and other related fields. Synthetic aperture radar (SAR) serves as a significant mean for acquiring soil moisture, among which, vegetation and surface roughness are two key influencing factors. Therefore, this paper focuses on several aspects. Firstly, using Sentinel-1 radar data to obtain overall backscattering coefficients and which of vegetation are separated utilizing three vegetation indices (NDVI, NDWI, MSAVI) deriving from Landsat 8 optical data using the water-cloud model. Subsequently, the Dobson model is employed in conjunction with the advanced integral equation model (AIEM) to establish a table of backscattering coefficients lacking surface roughness, determining optimal roughness parameters through a minimum-cost function. Finally, a least squares method is used to determine the coefficients of the empirical equation for soil moisture inversion. Experimental results demonstrate that in highland areas, the model's inversion results exhibit good consistency with ground-truth measurements. Among these, the use of the normalized difference water index (NDWI2) as input for the water-cloud model combined with optimal roughness inversion yields the best results, with a fitting coefficient of 0.840 2 and a root mean square error of 0.027 21 cm³/cm³.

Key words: soil moisture, AIEM, optimal roughness, multi-source remote sensing, Dobson

CLC Number:

P237

WANG Xiaying, SHE Yulin, ZHANG Shuangcheng, XIA Yuanping, NIU Yufen. Soil moisture inversion in highland areas based on Sentinel-1 and Landsat 8 remote sensing data[J]. Bulletin of Surveying and Mapping, 2024, 0(7): 140-146.

References

[1] 陈书林,刘元波,温作民.卫星遥感反演土壤水分研究综述[J].地球科学进展,2012,27(11):1192-1203.
[2] GHERBOUDJ I,MAGAGI R,BERG A A,et al.Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data[J].Remote Sensing of Environment,2011,115(1): 33-43.
[3] SENEVIRATNE S I,CORTI T,DAVIN E L,et al.Investigating soil moisture-climate interactions in a changing climate: a review[J].Earth Science Reviews,2010,99(3): 125-161.
[4] BAGHDADI N,BOYER N,TODOROFF P,et al.Potential of SAR sensors TerraSAR-X,ASAR/ENVISAT and PALSAR/ALOS for monitoring sugarcane crops on Reunion Island[J].Remote Sensing of Environment,2009,113(8): 1724-1738.
[5] 胡洁,姜成桢,杨勇,等.黑河中下游植被覆盖度变化遥感监测[J].测绘通报,2021(10): 34-38.
[6] 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 and Remote Sensing,1992,30(2): 370-381.
[7] BAGHDADI N,ZRIBI M.Evaluation of radar backscatter models IEM,OH and Dubois using experimental observations[J].International Journal of Remote Sensing,2006,27(18): 3831-3852.
[8] OH Y,SARABANDI K,ULABY F T.Semi-empirical model of the ensemble-averaged differential Mueller matrix for microwave backscattering from bare soil surfaces[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(6): 1348-1355.
[9] FUNG A K,LI Z,CHEN K S.Backscattering from a randomly rough dielectric surface[J].IEEE Transactions on Geoscience and Remote Sensing,1992,30(2): 356-369.
[10] LI Qin,SHI Jiancheng,CHEN K S.A generalized power law spectrum and its applications to the backscattering of soil surfaces based on the integral equation model[J].IEEE Transactions on Geoscience and Remote Sensing,2002,40(2): 271-280.
[11] FUNG A K,CHEN K S.An update on the IEM surface backscattering model[J].IEEE Geoscience and Remote Sensing Letters,2004,1(2): 75-77.
[12] 张双成,鲍琳,马中民,等.多源哨兵数据解译农田区土壤湿度算法研究[J].测绘科学,2022,47(8): 94-104.
[13] DE ZAN F,GOMBA G.Vegetation and soil moisture inversion from SAR closure phases: first experiments and results[J].Remote Sensing of Environment,2018,217: 562-572.
[14] 陈晶,贾毅,余凡.双极化雷达反演裸露地表土壤水分[J].农业工程学报,2013,29(10): 109-115.
[15] JOSEPH A T,VAN DER VELDE R,O'NEILL P E,et al.Soil moisture retrieval during a corn growth cycle using L-band (1.6 GHz) radar observations[J].IEEE Transactions on Geoscience and Remote Sensing,2008,46(8): 2365-2374.
[16] ATTEMA E P W,ULABY F T.Vegetation modeled as a water cloud[J].Radio Science,1978,13(2): 357-364.
[17] 白晓静.基于多波段多极化SAR数据的草原地表土壤水分反演方法研究[D].成都:电子科技大学,2017.
[18] BAGHDADI N,KING C,CHANZY A,et al.An empirical calibration of the integral equation model based on SAR data,soil moisture and surface roughness measurement over bare soils[J].International Journal of Remote Sensing,2002,23(20): 4325-4340.
[19] ZRIBI M,BAGHDADI N,HOLAH N,et al.New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion[J].Remote Sensing of Environment,2005,96(3/4): 485-496.
[20] KWEON S K,OH Y.A modified water-cloud model with leaf angle parameters for microwave backscattering from agricultural fields[J].IEEE Transactions on Geoscience and Remote Sensing,2015,53(5): 2802-2809.
[21] 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.
[22] GAO Ying,WALKER J P,ALLAHMORADI M,et al.Optical sensing of vegetation water content: a synthesis study[J].IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2015,8(4): 1456-1464.
[23] DOBSON M,ULABY F,HALLIKAINEN M,et al.Microwave dielectric behavior of wet soil-part II: dielectric mixing models[J].IEEE Transactions on Geoscience and Remote Sensing,1985,23(1): 35-46.
[24] ZRIBI M,DECHAMBRE M.A new empirical model to retrieve soil moisture and roughness from C-band radar data[J].Remote Sensing of Environment,2003,84(1): 42-52.
[25] 余凡,李海涛,张承明,等.利用双极化微波遥感数据反演土壤水分的新方法[J].武汉大学学报(信息科学版),2014,39(2): 225-228.
[26] 孔金玲,甄珮珮,李菁菁,等.基于新的组合粗糙度参数的土壤水分微波遥感反演[J].地理与地理信息科学,2016,32(3): 34-38.
[27] 甄珮珮.基于粗糙度参数的风沙滩地区土壤水分微波遥感反演模型研究[D].西安: 长安大学,2016.