光学与SAR遥感协同反演土壤水分研究进展

doi:10.6046/zrzyyg.2020416

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摘要

土壤水分在农业生产应用中有着不可替代的作用,农业用水、估产、旱情监测等都与土壤水分有着密不可分的关系,因此进行土壤水分变化的监测具有重要意义。目前遥感技术是进行大区域土壤水分变化监测的有效手段。光学遥感对地表植被组份信息敏感,微波可穿透植被获取植被下土壤水分信息,但合成孔径雷达(synthetic aperture Radar,SAR)的后向散射对土壤水分变化的敏感性受冠层影响较大。在植被覆盖区,微波遥感会受到地表粗糙度和植被的双重影响,因此采用光学和SAR遥感协同的策略能更好地去除植被和粗糙度影响,提高土壤水分的反演精度。总结了目前光学与SAR遥感协同反演土壤水分研究中常用的遥感模型和反演方法,并对研究中存在的困难与未来发展进行了总结与展望。

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	艾璐
	孙淑怡
	李书光
	马红章

关键词 ：土壤水分, 光学遥感, SAR, 植被, 粗糙度, 散射模型

Abstract：

Soil moisture (SM) plays an irreplaceable role in agricultural production, and agricultural water use, yield estimation, and drought monitoring are all closely related to SM. Therefore, it is of great significance to monitor the changes in SM. At present, the remote sensing technique is an effective tool for the monitoring of the changes in SM in large areas. Optical remote sensing is sensitive to the composition of surface vegetation, while microwaves can penetrate vegetation to obtain the information of SM under vegetation. Meanwhile, the sensitivity of synthetic aperture Radar (SAR) backscattering to the changes in SM is greatly affected by the vegetation canopy. In areas covered by vegetation, microwave remote sensing will be affected by both surface roughness and vegetation. Therefore, the joint application of optical and SAR remote sensing can well remove the impacts of vegetation and surface roughness, thus improving the inversion accuracy of SM. This paper summarizes the remote sensing models and retrieval methods commonly used in the research on the cooperative inversion of SM using optical and SAR remote sensing. Meanwhile, it proposes the difficulties in the research and the future development of the cooperative inversion.

Key words： soil moisture optical remote sensing SAR vegetation canopy roughness scattering model

收稿日期: 2020-12-23 出版日期: 2021-12-23

ZTFLH:

TP79

基金资助:国家自然科学基金面上项目“复杂城市地表不透水面多源高分遥感成像机理与分层优化提取方法”(41971292);山东省自然科学基金项目“光学与微波遥感协同反演植被覆盖区土壤水分研究”(ZR2017MD007)

通讯作者: 李书光

作者简介: 艾璐(1996-),女,硕士研究生,主要从事光学和微波遥感协同反演土壤水分研究。Email: 873176610@qq.com。

引用本文:

艾璐, 孙淑怡, 李书光, 马红章. 光学与SAR遥感协同反演土壤水分研究进展[J]. 自然资源遥感, 2021, 33(4): 10-18.
AI Lu, SUN Shuyi, LI Shuguang, MA Hongzhang. Research progress on the cooperative inversion of soil moisture using optical and SAR remote sensing. Remote Sensing for Natural Resources, 2021, 33(4): 10-18.

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https://www.gtzyyg.com/CN/10.6046/zrzyyg.2020416 或 https://www.gtzyyg.com/CN/Y2021/V33/I4/10

Fig.1 土壤水分反演技术路线

[1]	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):959-973.
	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):959-973.
[2]	Hajj M E, Baghdadi N, Zribi M, et al. Soil moisture retrieval over irrigated grassland using X-band SAR data[J]. Remote Sensing of Environment, 2016,176:202-218. doi: 10.1016/j.rse.2016.01.027
[3]	Jackson T J, Le Vine D M, Hsu A Y, et al. Soil moisture mapping at regional scales using microwave radiometry:The southern great plains hydrology experiment[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999,37(5):2136-2151. doi: 10.1109/36.789610
[4]	Narayanan R M, Hegde M S. Soil moisture estimation using combined multifrequency SAR data:A comparison between two inversion models using simulation[J]. Geocarto International, 2000,15(3):65-76. doi: 10.1080/10106040008542165
[5]	Zhao T, Hu L, Shi J, et al. Soil moisture retrievals using L-band radiometry from variable angular ground-based and airborne observations[J]. Remote Sensing of Environment, 2020,248:111958. doi: 10.1016/j.rse.2020.111958
[6]	De Roo R D, Du Y, 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. doi: 10.1109/36.917912
[7]	Verhoest N E C, Lievens H, Wagner W, et al. On the soil roughness parameterization problem in soil moisture retrieval of bare surfaces from synthetic aperture Radar[J]. Sensors, 2008,8(7):4213-4248. pmid: 27879932
[8]	Marzahn P, Ludwig R. On the derivation of soil surface roughness from multi parametric PolSAR data and its potential for hydrological modeling[J]. Hydrology and Earth System Sciences, 2009,13(3):381-394. doi: 10.5194/hess-13-381-2009
[9]	赵凯, 黄资彧. 基于改进热惯量模型的表层土壤水反演研究[J]. 测绘与空间地理信息, 2017,40(5):41-43.
	Zhao K, Huang Z Y. The study of soil moisture retrieval based on improved thermal inertia model[J]. Geomatics and Spatial Information Technology, 2017,40(5):41-43.
[10]	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), 2011,41(4):532-540.
	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, 2011,41(4):532-540.
[11]	孔金玲, 李菁菁, 甄珮珮, 等. 微波与光学遥感协同反演旱区地表土壤水分研究[J]. 地球信息科学学报, 2016,18(6):857-863. doi: 10.3724/SP.J.1047.2016.00857
	Kong J L, Li J J, Zhen P P, et al. Inversion of soil moisture in arid area based on microwave and optical remote sensing data[J]. Journal of Geo-Information Science, 2016,18(6):857-863.
[12]	李奎, 张瑞, 段金亮, 等. 利用SAR影像与多光谱数据反演广域土壤湿度[J]. 农业工程学报, 2020,36(7):134-140.
	Li K, Zhang R, Duan J L, et al. Wide-area soil moisture retrieval using SAR images and multispectral data[J]. Transactions of the Chinese Society of Agricultural Engineering, 2020,36(7):134-140.
[13]	马红章, 张临晶, 孙林, 等. 光学与微波数据协同反演农田区土壤水分[J]. 遥感学报, 2014,18(3):673-685.
	Ma H Z, Zhang L J, Sun L, et al. Farmland soil moisture inversion by synergizing optical and microwave remote sensing data[J]. Journal of Remote Sensing, 2014,18(3):673-685.
[14]	Paloscia S, Pettinato S, Santi E, et al. Soil moisture mapping using Sentinel-1 images:Algorithm and preliminary validation[J]. Remote Sensing of Environment, 2013,134:234-248. doi: 10.1016/j.rse.2013.02.027
[15]	Huang S, Ding J, Liu B, et al. The capability of integrating optical and microwave data for detecting soil moisture in an oasis region[J]. Remote Sensing, 2020,12(9):1358. doi: 10.3390/rs12091358
[16]	何连, 秦其明, 任华忠, 等. 利用多时相Sentinel-1 SAR数据反演农田地表土壤水分[J]. 农业工程学报, 2016,32(3):142-148.
	He L, Qin Q M, Ren H Z, et al. Soil moisture retrieval using multi-temporal Sentinel-1 SAR data in agricultural areas[J]. Transactions of the Chinese Society of Agricultural Engineering, 2016,32(3):142-148.
[17]	刘正春, 冯美臣, 徐立帅, 等. 基于Sentinel多源数据的晋南农田地表土壤水分反演[J]. 中国农学通报, 2020,36(20):51-58.
	Liu Z C, Feng M C, Xu L S, et al. Soil moisture retrieval of farmland in southern Shanxi:Based on Sentinel multi-source data[J]. Chinese Agricultural Science Bulletin, 2020,36(20):51-58.
[18]	张滢, 丁建丽, 周鹏. 干旱区土壤水分微波遥感反演算法综述[J]. 干旱区地理, 2011,34(4):671-678.
	Zhang Y, Ding J L, Zhou P. Model algorithm of soil moisture retrieval base on microwave remote sensing in arid regions[J]. Arid Land Geography, 2011,34(4):671-678.
[19]	王树果, 李新, 韩旭军, 等. 利用多时相ASAR数据反演黑河流域中游地表土壤水分[J]. 遥感技术与应用, 2009,24(5):582-587,552.
	Wang S G, Li X, Han X J, et al. Derivation of surface soil moisture in the middle stream of Heihe River Basin using multi-temporal ASAR images[J]. Remote Sensing Technology and Application, 2009,24(5):582-587,552.
[20]	梁顺林, 白瑞, 陈晓娜, 等. 2019年中国陆表定量遥感发展综述[J]. 遥感学报, 2020,24(6):618-671.
	Liang S L, Bai R, Chen X N, et al. Review of China's land surface quantitative remote sensing development in 2019[J]. Journal of Remote Sensing, 2020,24(6):618-671.
[21]	Karthikeyan L, Pan M, Konings A G, et al. Simultaneous retrieval of global scale vegetation optical depth,surface roughness,and soil moisture using X-band AMSR-E observations[J]. Remote Sensing of Environment, 2019,234:111473. doi: 10.1016/j.rse.2019.111473
[22]	Notarnicola C. A bayesian change detection approach for retrieval of soil moisture variations under different roughness conditions[J]. IEEE Geoscience and Remote Sensing Letters, 2013,11(2):414-418. doi: 10.1109/LGRS.8859
[23]	Zeng J, Chen K S, Bi H, et al. A comprehensive analysis of rough soil surface scattering and emission predicted by AIEM with comparison to numerical simulations and experimental measurements[J]. IEEE Transactions on Geoscience and Remote Sensing, 2017,55(6):1696-1708. doi: 10.1109/TGRS.2016.2629759
[24]	Wu T D, Chen K S, Shi J, et al. A transition model for the reflection coefficient in surface scattering[J]. IEEE Transactions on Geo-science and Remote Sensing, 2001,39(9):2040-2050. doi: 10.1109/36.951094
[25]	Chen K S, Wu T D, Tsang L, et al. Emission of rough surfaces calculated by the integral equation method with comparison to three-dimensional moment method simulations[J]. IEEE Transactions on Geoscience and Remote Sensing, 2003,41(1):90-101. doi: 10.1109/TGRS.2002.807587
[26]	Wu T D, Chen K S. A reappraisal of the validity of the IEM model for backscattering from rough surfaces[J]. IEEE Transactions on Geoscience and Remote Sensing, 2004,42(4):743-753. doi: 10.1109/TGRS.2003.815405
[27]	Yang Y, Chen K S, Tsang L, et al. Depolarized backscattering of rough surface by AIEM model[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017,10(11):4740-4752. doi: 10.1109/JSTARS.4609443
[28]	Shi J, Chen K S, Li Q, et al. A parameterized surface reflectivity model and estimation of bare-surface soil moisture with L-band radiometer[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002,40(12):2674-2686. doi: 10.1109/TGRS.2002.807003
[29]	Dubois P C, Van Zyl J, Engman T. Measuring soil moisture with imaging Radars[J]. IEEE Transactions on Geoscience and Remote Sensing, 1995,33(4):915-926. doi: 10.1109/36.406677
[30]	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. doi: 10.1109/36.134086
[31]	Wigneron J-P, Laguerre L, Kerr Y H. A simple parameterization of the L-band microwave emission from rough agricultural soils[J]. IEEE Transactions on Geoscience and Remote Sensing, 2001,39(8):1697-1707. doi: 10.1109/36.942548
[32]	Allen W A, Gausman H W, Richardson A J, et al. Interaction of isotropic light with a compact plant leaf[J]. Josa, 1969,59(10):1376-1379. doi: 10.1364/JOSA.59.001376
[33]	Verhoef W. Light scattering by leaf layers with application to canopy reflectance modeling:The SAIL model[J]. Remote Sensing of Environment, 1984,16(2):125-141. doi: 10.1016/0034-4257(84)90057-9
[34]	Suits G H. The calculation of the directional reflectance of a vegetative canopy[J]. Remote Sensing of Environment, 1971,2:117-125. doi: 10.1016/0034-4257(71)90085-X
[35]	Jacquemoud S. Inversion of the PROSPECT+ SAIL canopy reflectance model from AVIRIS equivalent spectra:Theoretical study[J]. Remote Sensing of Environment, 1993,44(2-3):281-292. doi: 10.1016/0034-4257(93)90022-P
[36]	Ulaby F T, Allen C T, Eger Iii G, et al. Relating the microwave backscattering coefficient to leaf area index[J]. Remote Sensing of Environment, 1984,14(1-3):113-133. doi: 10.1016/0034-4257(84)90010-5
[37]	Lievens H, Verhoest N E. On the retrieval of soil moisture in wheat fields from L-band SAR based on water cloud modeling,the IEM,and effective roughness parameters[J]. IEEE Geoscience and Remote Sensing Letters, 2011,8(4):740-744. doi: 10.1109/LGRS.2011.2106109
[38]	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. doi: 10.1109/TGRS.2014.2364914
[39]	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. doi: 10.1109/TGRS.2008.917214
[40]	Joseph A T, Van der Velde R, O'Neill P E, et al. Effects of corn on C- and L-band Radar backscatter:A correction method for soil moisture retrieval[J]. Remote Sensing of Environment, 2011,114(11):2417-2430. doi: 10.1016/j.rse.2010.05.017
[41]	Prakash R, Singh D, Pathak N P. A fusion approach to retrieve soil moisture with SAR and optical data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2012,5(1):196-206. doi: 10.1109/JSTARS.4609443
[42]	Bai X, He B. Potential of Dubois model for soil moisture retrieval in prairie areas using SAR and optical data[J]. International Journal of Remote Sensing, 2015,36(21-22):5737-5753. doi: 10.1080/01431161.2015.1103920
[43]	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. doi: 10.1080/01431169008955090
[44]	杜鹤娟, 柳钦火, 李静, 等. 光学与微波植被指数协同反演农作物叶面积指数的可行性分析[J]. 遥感学报, 2013,17(6):1587-1611.
	Du H J, Liu Q H, Li J, et al. Retrieving crop leaf area index by combining optical and microwave vegetation indices:A feasibility analysis[J]. Journal of Remote Sensing, 2013,17(6):1587-1611.
[45]	马腾, 韩玲, 刘全明. 考虑地表粗糙度改进水云模型反演西班牙农田地表土壤含水率[J]. 农业工程学报, 2019,35(24):129-135.
	Ma T, Han L, Liu Q M. Inversion of surface soil moisture content of Spanish farmland using modified water cloud model[J]. Transactions of the Chinese Society of Agricultural Engineering, 2019,35(24):129-135.
[46]	Han L, Chen L W, Zhang Y C, et al. A method of microwave soil moisture inversion without dependence on the field measurement data[J]. International Archives of the Photogrammetry,Remote Sensing and Spatial Information Sciences, 2018,XLII-3:459-465. doi: 10.5194/isprs-archives-XLII-3-459-2018
[47]	韩玲, 张延成. 光学与微波数据协同反演植被覆盖区土壤水分[J]. 水资源与水工程学报, 2018,29(4):230-235.
	Han L, Zhang Y C. Synergistic inversion of soil moisture in vegetation-covered area based on optical and microwave data[J]. Journal of Water Resources and Water Engineering, 2018,29(4):230-235.
[48]	Yadav V P, Prasad R, Bala R, et al. An improved inversion algorithm for spatio-temporal retrieval of soil moisture through modified water cloud model using C- band Sentinel-1A SAR data[J]. Computers and Electronics in Agriculture, 2020,173:105447. doi: 10.1016/j.compag.2020.105447
[49]	Park S E, Jung Y T, Cho J H, et al. Theoretical evaluation of water cloud model vegetation parameters[J]. Remote Sensing, 2019,11(8):894. doi: 10.3390/rs11080894
[50]	Wang L, He B, Bai X, et al. Assessment of different vegetation parameters for parameterizing the coupled water cloud model and advanced integral equation model for soil moisture retrieval using time series Sentinel-1A data[J]. Photogrammetric Engineering and Remote Sensing, 2019,85(1):43-54. doi: 10.14358/PERS.85.1.43
[51]	Mattar C, Wigneron J P, Sobrino J A, et al. A combined optical-microwave method to retrieve soil moisture over vegetated areas[J]. IEEE Transactions on Geoscience and Remote Sensing, 2012,50(5):1404-1413. doi: 10.1109/TGRS.2011.2179051
[52]	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.
[53]	雷志斌, 孟庆岩, 田淑芳, 等. 基于GF-3和Landsat8遥感数据的土壤水分反演研究[J]. 地球信息科学学报, 2019,21(12):1965-1976. doi: 10.12082/dqxxkx.2019.190115
	Lei Z B, Meng Q Y, Tian S F, et al. Soil moisture retrieval study based on GF-3 and Landsat8 remote sensing data[J]. Journal of Geo-Information Science, 2019,21(12):1965-1976.
[54]	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. doi: 10.1016/S0034-4257(02)00069-X
[55]	Rahman M M, Moran M S, Thoma D P, et al. A derivation of roughness correlation length for parameterizing Radar backscatter models[J]. International Journal of Remote Sensing, 2007,28(18):3995-4012. doi: 10.1080/01431160601075533
[56]	Zhu L, Walker J P, Ye N, et al. Roughness and vegetation change detection:A pre-processing for soil moisture retrieval from multi-temporal SAR imagery[J]. Remote Sensing of Environment, 2019,225:93-106. doi: 10.1016/j.rse.2019.02.027
[57]	Santi E, Dabboor M, Pettinato S, et al. Combining machine learning and compact polarimetry for estimating soil moisture from C-band SAR data[J]. Remote Sensing, 2019,11(20):2451. doi: 10.3390/rs11202451
[58]	余凡, 赵英时, 李海涛. 基于遗传BP神经网络的主被动遥感协同反演土壤水分[J]. 红外与毫米波学报, 2012,31(3):283-288.
	Yu F, Zhao Y S, Li H T. Soil moisture retrieval based on GA-BP neural networks algorithm[J]. Journal of Infrared and Millimeter Waves, 2012,31(3):283-288. doi: 10.3724/SP.J.1010.2012.00283
[59]	Santi E. Neural networks applications for the remote sensing of hydrological parameters[M]//Artificial Neural Networks—Models and Applications Book.TechOpen, 2016:309-334.
[60]	姜红, 玉素甫江·如素力, 拜合提尼沙·阿不都克日木, 等. 基于支持向量机回归算法的土壤水分光学与微波遥感协同反演[J]. 地理与地理信息科学, 2017,33(6):30-36.
	Jiang H, Yusufujiang R, Baihetinisha A, et al. Soil moisture retrieval by synergizing optical and microwave remote sensing data based on support vector machine regression algorithm[J]. Journal of Geo-Information Science, 2017,33(6):30-36.
[61]	Ma H, Liu S. The potential evaluation of multisource remote sensing data for extracting soil moisture based on the method of BP neural network[J]. Canadian Journal of Remote Sensing, 2016,42(2):117-124. doi: 10.1080/07038992.2016.1160773
[62]	Getachew A, Tsegaye T, Berhan G, et al. Combined use of Sentinel-1 SAR and Landsat sensors products for residual soil moisture retrieval over agricultural fields in the upper Blue Nile Basin,Ethiopia[J]. Sensors, 2020,20(11):3282. doi: 10.3390/s20113282
[63]	Notarnicola C, Angiulli M, Posa F. Soil moisture retrieval from remotely sensed data:Neural network approach Versus Bayesian method[J]. IEEE Transactions on Geoscience and Remote Sensing, 2008,46(2):547-557. doi: 10.1109/TGRS.2007.909951
[64]	Kolassa J, Reichle R H, Liu Q, et al. Estimating surface soil moisture from SMAP observations using a neural network technique[J]. Remote Sensing of Environment, 2018,204:43-59. doi: 10.1016/j.rse.2017.10.045 pmid: 29290638

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