CYGNSS土壤盐分反演方法研究：以黄河三角洲为例
A remote sensing method for retrieving soil salinity based on CYGNSS： Taking the Yellow River Delta as an example
- 2023年27卷第2期 页码：351-362
DOI： 10.11834/jrs.20210466
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摘要
土壤盐碱胁迫是植物生产力低下的关键因素，也是全球盐碱区可持续发展的瓶颈；如何较为高效可靠地获取区域土壤盐分信息是需要优先解决的问题。随着全球导航卫星系统反射测量GNSS-R（Global Navigation Satellite System Reflectometry）的迅速发展，运用星载GNSS-R测量区域范围的土壤盐分成为一种可能。全球飓风导航卫星系统CYGNSS（The Cyclone Global Navigation Satellite System）作为星载GNSS-R计划的重要组成部分之一，其卫星传感器使用的L波段能够敏感地获取土壤介电常数信息，为反演土壤盐分提供了理论基础。本文以CYGNSS作为主要数据源，选取土壤盐渍化十分严重且具有典型代表性的黄河三角洲区域作为研究区域，首次探讨CYGNSS反演土壤盐分的可行性，并建立了一套土壤盐分的反演方法。首先，利用基于相干信号的双基雷达方程对 CYGNSS 数据进行计算获取地表反射率，并校正地表反射率的地表粗糙度和植被衰减效应，计算得到土壤介电常数的幅值；然后，以改进的Dobson-S土壤介电常数模型为物理模型结合土壤水分主动—被动探测卫星SMAP（Soil Moisture Active Passive Mission）土壤水分产品数据为主要的辅助数据，构建一套土壤盐分反演方法，完成2020年5月份黄河三角洲高效经济生态区的土壤盐分反演；最后，利用实测电导率数值对反演结果进行真实性检验，决定系数（,R,2,）为0.88、均方根误差（RMSE）为1.06 mS/cm，拟合精度较高。本研究成果表明运用CYGNSS反演土壤盐分具有一定的可行性，并为区域尺度上的土壤盐分提供一种新思路。
Abstract
Soil saline-alkali stress is the key factor of low plant productivity and the bottleneck of sustainable development in global saline-alkali areas. Obtaining regional soil salinity information both efficiently and reliably is a necessary problem to be solved. The rapid development of global navigation satellite system reflectometry (GNSS-R) provides a new opportunity to use spaceborne GNSS-R to retrieve soil salinity. The Cyclone Global Navigation Satellite System (CYGNSS) is one of the important components of the spaceborne GNSS-R mission, and the L-band used by CYGNSS is very sensitive to the soil dielectric constant, which provides a theoretical basis for estimating soil salinity. In this paper, CYGNSS was taken as the main data source, and the Yellow River Delta region, a typical area with extreme salinization of soil, was selected as the research religion to discuss the feasibility of soil salinity estimation by CYGNSS for the first time. A set of soil salinity retrieval methods was established.,We proposed a physical model that took CYGNSS as the main data with some other auxiliary data fused. First, the surface reflectance was obtained by calculating the CYGNSS data of coherent signals based on the bistatic radar equation, and then the surface roughness and vegetation attenuation effects of the surface reflectance were corrected to calculate the magnitude of the soil dielectric constant. Second, based on the improved Dobson-S soil dielectric constant model as the physical model and the Soil Moisture Active Passive Mission (SMAP) soil moisture product as the main auxiliary data, a set of soil salinity retrieval methods was constructed to complete the soil salinity estimation in the Yellow River Delta High-efficiency Ecological Economic Zone in May 2020. Finally, the result was verified by the ground-measured conductivity value.,It was found that the soil salinity retrieved from the CYGNSS data correlated well with the ground-measured conductivity, with a coefficient of determination (,R,2,) equal to 0.88 and a Root Mean Squared Error (RMSE) equal to 1.06 mS/cm. Therefore, a high-precision soil salinization map of the Yellow River Delta was made by kriging interpolation based on the estimation result, which showed an obvious trend that soil salinity decreased gradually from coastal to inland at the regional scale with a strong spatial heterogeneity itself.,In this paper, a physical model for soil salinity estimation based on the bistatic radar equation and dielectric constant model was proposed using CYGNSS as the main data source. The results of this study indicated that it is feasible to use CYGNSS to estimate soil salinity and proved the sensitivity of the L-band to soil salinity, providing a new idea for soil salinity retrieval on a regional scale. In future studies, the method of multisource data fusion can be considered to transform the estimation results from point data to planar area for expression, and a new validation method of high precision is needed due to the strong spatial heterogeneity of soil salinity.
关键词
遥感土壤盐分CYGNSS双基雷达方程介电常数黄河三角洲
Keywords
remote sensingsoil salinityCYGNSSthe bistatic radar equationdielectric constantYellow River Delta
references
Aldabaa A A A, Weindorf D C, Chakraborty S, Sharma A and Li B. 2015. Combination of proximal and remote sensing methods for rapid soil salinity quantification. Geoderma, 239-240: 34-46 [DOI: 10.1016/j.geoderma.2014.09.011http://dx.doi.org/10.1016/j.geoderma.2014.09.011]
Allbed A, Kumar L and Aldakheel Y Y. 2014. Assessing soil salinity using soil salinity and vegetation indices derived from IKONOS high-spatial resolution imageries: applications in a date palm dominated region. Geoderma, 230-231: 1-8 [DOI: 10.1016/j.geoderma.2014.03.025http://dx.doi.org/10.1016/j.geoderma.2014.03.025]
Bai C L. 2020. Scientific and technological innovation leads high-quality development of agriculture in the Yellow River Delta. Bulletin of Chinese Academy of Sciences, 35(2): 137-144
白春礼. 2020. 科技创新引领黄河三角洲农业高质量发展. 中国科学院院刊, 35(2): 137-144 [DOI: 10.16418/j.issn.1000-3045.20200201001http://dx.doi.org/10.16418/j.issn.1000-3045.20200201001]
Bell D, Menges C, Ahmad W and van Zyl J J. 2001. The application of dielectric retrieval algorithms for mapping soil salinity in a tropical coastal environment using airborne polarimetric SAR. Remote Sensing of Environment, 75(3): 375-384 [DOI: 10.1016/S0034-4257(00)00180-2http://dx.doi.org/10.1016/S0034-4257(00)00180-2]
Calabia A, Molina I and Jin S G. 2020. Soil moisture content from GNSS reflectometry using dielectric permittivity from Fresnel reflection coefficients. Remote Sensing, 12(1): 122 [DOI: 10.3390/rs12010122http://dx.doi.org/10.3390/rs12010122]
Camps A, Park H, Pablos M, Foti G, Gommenginger C P, Liu P W and Judge J. 2016. Sensitivity of GNSS-R spaceborne observations to soil moisture and vegetation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 9(10): 4730-4742 [DOI: 10.1109/JSTARS.2016.2588467http://dx.doi.org/10.1109/JSTARS.2016.2588467]
Camps A, Vall·Llossera M, Park H, Portal G and Rossato L. 2018. Sensitivity of TDS-1 GNSS-R reflectivity to soil moisture: global and regional differences and impact of different spatial scales. Remote Sensing, 10(11): 1856 [DOI: 10.3390/rs10111856http://dx.doi.org/10.3390/rs10111856]
Chen Q, Zeng J Y, Cui C Y, Li Z, Chen K S, Bai X J and Xu J. 2018. Soil moisture retrieval from SMAP: a validation and error analysis study using Ground-Based observations over the little Washita watershed. IEEE Transactions on Geoscience and Remote Sensing, 56(3): 1394-1408 [DOI: 10.1109/TGRS.2017.2762462http://dx.doi.org/10.1109/TGRS.2017.2762462]
Chew C and Small E. 2020. Description of the UCAR/CU soil moisture product. Remote Sensing, 12(10): 1558 [DOI: 10.3390/rs12101558http://dx.doi.org/10.3390/rs12101558]
Chew C C and Small E E. 2018. Soil moisture sensing using spaceborne GNSS reflections: comparison of CYGNSS reflectivity to SMAP soil moisture. Geophysical Research Letters, 45(9): 4049-4057 [DOI: 10.1029/2018GL077905http://dx.doi.org/10.1029/2018GL077905]
Clarizia M P, Pierdicca N, Costantini F and Floury N. 2019. Analysis of CYGNSS data for soil moisture retrieval. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 12(7): 2227-2235 [DOI: 10.1109/JSTARS.2019.2895510http://dx.doi.org/10.1109/JSTARS.2019.2895510]
Crespo J A, Posselt D J and Asharaf S. 2019. CYGNSS surface heat flux product development. Remote Sensing, 11(19): 2294 [DOI: 10.3390/rs11192294http://dx.doi.org/10.3390/rs11192294]
Cui C Y, Xu J, Zeng J Y, Chen K S, Bai X J, Lu H, Chen Q and Zhao T J. 2018. Soil moisture mapping from satellites: an intercomparison of SMAP, SMOS, FY3B, AMSR2, and ESA CCI over two dense network regions at different spatial scales. Remote Sensing, 10(1): 33 [DOI: 10.3390/rs10010033http://dx.doi.org/10.3390/rs10010033]
Dobson M C, Ulaby F T, Hallikainen M T and El-Rayes M A. 1985. Microwave dielectric behavior of wet soil-part II: dielectric mixing models. IEEE Transactions on Geoscience and Remote Sensing, GE-23(1): 35-46 [DOI: 10.1109/TGRS.1985.289498http://dx.doi.org/10.1109/TGRS.1985.289498]
Eroglu O, Kurum M, Boyd D and Gurbuz A C. 2019. High spatio-temporal resolution CYGNSS soil moisture estimates using artificial neural networks. Remote Sensing, 11(19): 2272 [DOI: 10.3390/rs11192272http://dx.doi.org/10.3390/rs11192272]
Fischer G, Nachtergaele F, Prieler S, van Velthuizen H T, Verelst L and Wiberg D. 2008. Global Agro-Ecological Zones Assessment for Agriculture (GAEZ 2008). IIASA, FAO
Hou H H, Wang C T, Wang X D and Gao F H. 2014. A study of improvement effects by biological measures in the Yellow River delta saline-alkali soil. China Rural Water and Hydropower, (7): 1-6
侯贺贺, 王春堂, 王晓迪, 高范华. 2014. 黄河三角洲盐碱地生物措施改良效果研究. 中国农村水利水电, (7): 1-6 [DOI: 10.3969/j.issn.1007-2284.2014.07.001http://dx.doi.org/10.3969/j.issn.1007-2284.2014.07.001]
Hu Q R. 2003. Studies on Microwave Dielectric Behavior of Moist Salt Soil and Its Effect on Backscattering Coefficients Extracted from Radar Image. Beijing: Chinese Academy of Sciences, Institute of Remote Sensing Applications
胡庆荣. 2003. 含水含盐土壤介电特性实验研究及对雷达图像的响应分析. 北京: 中国科学院研究生院(遥感应用研究所)
Jensen K, McDonald K, Podest E, Rodriguez-Alvarez N, Horna V and Steiner N. 2018. Assessing L-Band GNSS-Reflectometry and imaging radar for detecting Sub-Canopy inundation dynamics in a tropical wetlands complex. Remote Sensing, 10(9): 1431 [DOI: 10.3390/rs10091431http://dx.doi.org/10.3390/rs10091431]
Morris M and Ruf C S. 2017. Estimating tropical cyclone integrated kinetic energy with the CYGNSS satellite constellation. Journal of Applied Meteorology and Climatology, 56(1): 235-245 [DOI: 10.1175/JAMC-D-16-0176.1http://dx.doi.org/10.1175/JAMC-D-16-0176.1]
Nachtergaele F, van Velthuizen H and Verelst L. 2008. Harmonized World Soil Database. Food and Agriculture Organization of the United Nations
O’Neill P, Chan S, Njoku E G, Jackson T and Bindlish R. 2018. Algorithm Theoretical Basis Document Level 2 & 3 Soil Moisture (Passive) Data Products. Pasadena: Jet Propulsion Laboratory
O'Neill P E, Chan S, Njoku E G, Jackson T, Bindlish R and Chaubell J. 2019. Smap Enhanced l3 Radiometer Global Daily 9 km Ease-Grid Soil Moisture, Version 3. NASA National Snow and Ice Data Center Distributed Active Archive Center
Peng J, Biswas A, Jiang Q S, Zhao R Y, Hu J, Hu B F and Shi Z. 2019. Estimating soil salinity from remote sensing and terrain data in southern Xinjiang Province, China. Geoderma, 337: 1309-1319 [DOI: 10.1016/j.geoderma.2018.08.006http://dx.doi.org/10.1016/j.geoderma.2018.08.006]
Rodriguez-Alvarez N, Monerris A, Bosch-Lluis X, Camps A, Vall-Llossera M, Marchan-Hernández J F, Ramos-Pérez I, Valencia E, Martínez-Fernández J, Sánchez-Martín N, Baroncini-Turricchid G and Pérez-Gutiérrez C. 2009. Soil moisture and vegetation height retrieval using GNSS-R techniques//Proceedings of 2009 IEEE International Geoscience and Remote Sensing Symposium. Cape Town: IEEE
Ruf C, Asharaf S, Balasubramaniam R, Gleason S, Lang T, McKague D, Twigg D and Waliser D. 2019. In-orbit performance of the constellation of CYGNSS hurricane satellites. Bulletin of the American Meteorological Society, 100(10): 2009-2023 [DOI: 10.1175/BAMS-D-18-0337.1http://dx.doi.org/10.1175/BAMS-D-18-0337.1]
Ruf C, Chang P, Clarizia M P, Gleason S, Jelenak Z, Murray J, Morris M, Musko S, Posselt D and Provost D. 2016. Cygnss Handbook. Ann Arbor, MI, Michigan Pub: 154
Shao Y, Hu Q R, Guo H D, Lu Y, Dong Q and Han C M. 2003. Effect of dielectric properties of moist salinized soils on backscattering coefficients extracted from RADARSAT image. IEEE Transactions on Geoscience and Remote Sensing, 41(8): 1879-1888 [DOI: 10.1109/TGRS.2003.813499http://dx.doi.org/10.1109/TGRS.2003.813499]
Shrestha R P. 2006. Relating soil electrical conductivity to remote sensing and other soil properties for assessing soil salinity in northeast Thailand. Land Degradation and Development, 17(6): 677-689 [DOI: 10.1002/ldr.752http://dx.doi.org/10.1002/ldr.752]
Stogryn A. 1971. Equations for calculating the dielectric constant of saline water (correspondence). IEEE Transactions on Microwave Theory and Techniques, 19(8): 733-736 [DOI: 10.1109/TMTT.1971.1127617http://dx.doi.org/10.1109/TMTT.1971.1127617]
Ulaby F T, Long D G, Blackwell W J, Elachi C, Fung A K, Ruf C, Sarabandi K, Zebker H A and Van Zyl J. 2014. Microwave Radar and Radiometric Remote Sensing. University of Michigan Press
Ulaby F T, Moore R K and Fung A K. 1986. Microwave Remote Sensing: Active and Passive. Volume 3 - from Theory to Applications. Norwood: Artech House
Valencia E, Camps A, Rodriguez-Alvarez N, Ramos-Perez I, Bosch-Lluis X and Park H. 2011. Improving the accuracy of sea surface salinity retrieval using GNSS-R data to correct the sea state effect. Radio Science, 46(6): RS0C02 [DOI: 10.1029/2011RS004688http://dx.doi.org/10.1029/2011RS004688]
Van de Griend A A and Wigneron J P. 2004. The b-factor as a function of frequency and canopy type at H-polarization. IEEE Transactions on Geoscience and Remote Sensing, 42(4): 786-794 [DOI: 10.1109/TGRS.2003.821889http://dx.doi.org/10.1109/TGRS.2003.821889]
Wan W, Liu B J, Zeng Z Y, Chen X, Wu G P, Xu L W, Chen X W and Hong Y. 2019. Using CYGNSS data to monitor china’s flood inundation during typhoon and extreme precipitation events in 2017. Remote Sensing, 11(7): 854 [DOI: 10.3390/rs11070854http://dx.doi.org/10.3390/rs11070854]
Wang Q. 2021. Progress of environmental remote sensing monitoring technology in China and some related frontier issues. National Remote Sensing Bulletin, 25(1): 25-36
王桥. 2021. 中国环境遥感监测技术进展及若干前沿问题. 遥感学报, 25(1): 25-36 [DOI: 10.11834/jrs.20210572http://dx.doi.org/10.11834/jrs.20210572]
Wei L, Wang W Z, Wu Y R and Ma C F. 2017. Comparative analysis of soil water and salt dielectric model. Remote Sensing Technology and Application, 32(6): 1022-1030
魏龙, 王维真, 吴月茹, 马春锋. 2017. 土壤水盐介电模型对比与分析. 遥感技术与应用, 32(6): 1022-1030 [DOI: 10.11873/j.issn.1004-0323.2017.6.1022http://dx.doi.org/10.11873/j.issn.1004-0323.2017.6.1022]
Wu X R, Xia J M, Jin S G, Bai W H and Dong Z N. 2019. Is soil salinity detectable by GNSS-R/IR?//Proceedings of the IGARSS 2019-2019 IEEE International Geoscience and Remote Sensing Symposium. Yokohama: IEEE: 6227-6230
Wu Y R and Wang W Z. 2011. Modelling the backscattering coefficient of salt-affected soils using AIEM model//Proceedings of SPIE 8181, Earth Resources and Environmental Remote Sensing/GIS Applications II. Prague, Czech Republic: SPIE: 81811O [DOI: 10.1117/12.897933http://dx.doi.org/10.1117/12.897933]
Yan J and Pei Y K. 2018. Remote sensing in land applications by using GNSS-reflectometry//Hung M. Recent Advances and Applications in Remote Sensing. Croatia: IntechOpen
Yang J S. 2008. Development and prospect of the research on salt-affected soils in China. Acta Pedologica Sinica, 45(5): 837-845
杨劲松. 2008. 中国盐渍土研究的发展历程与展望. 土壤学报, 45(5): 837-845 [DOI: 10.3321/j.issn:0564-3929.2008.05.010http://dx.doi.org/10.3321/j.issn:0564-3929.2008.05.010]
Zhang S X, Pu Z X, Posselt D J and Atlas R. 2017. Impact of CYGNSS ocean surface wind speeds on numerical simulations of a hurricane in observing system simulation experiments. Journal of Atmospheric and Oceanic Technology, 34(2): 375-383 [DOI: 10.1175/JTECH-D-16-0144.1http://dx.doi.org/10.1175/JTECH-D-16-0144.1]
Zhang Y H, Fang H L, Ma L, Ye Y C and Wang Y. 2020. Estimation of forest leaf area index and clumping index from the Global Positioning System (GPS) satellite carrier-to-noise-density ratio (C/N0). Remote Sensing Letters, 11(2): 146-155 [DOI: 10.1080/2150704X.2019.1692386http://dx.doi.org/10.1080/2150704X.2019.1692386]
Zhong L. 2008. Study on Microwave Remote Sensing Mechanism of Soil. Chengdu: University of Electronic Science and Technology of China (钟亮. 2008. 土壤微波遥感机理研究. 成都: 电子科技大学)
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中国科学院 地理科学与资源研究所 黄河三角洲现代农业工程实验室
中国农业大学 资源与环境学院
天津中科无人机应用研究院
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