卫星遥感反演土壤水分研究综述

doi:10.11867/j.issn.1001-8166.2012.11.1192

土壤水分是影响地表过程的核心变量之一。精准地测量土壤水分及其时空分布，长期以来是定量遥感研究领域的难点问题。简要回顾基于光学、被动微波、主动微波和多传感器联合反演等卫星遥感反演土壤水分的主要反演算法、存在的难点和前沿性研究问题，介绍了应用土壤水分反演算法所形成的3种主要全球土壤水分数据集，包括欧洲气象业务卫星(ERS/ MetOp)数据集、高级微波扫描辐射计（AMSR-E）数据集、土壤湿度与海洋盐分卫星（SMOS）数据集，并结合目前存在的问题探讨卫星遥感反演土壤水分研究的发展趋势。

关键词: 土壤水分反演算法, 光学遥感, 微波遥感, 多传感器联合反演, 全球数据集

Soil moisture is a key variable influencing a variety of land surface processes. Accurate estimation of spatio-temporally distributed soil moisture is one of the challenging issues in quantitative remote sensing. This paper briefly describes the major algorithms for retrieving soil moisture using optical, passive-microwave and active-microwave remote sensing, or their combinations. The optical algorithms have relatively low accuracy of retrieval, but good spatial and temporal resolutions. The typical algorithms include the Index-based approach and the soil thermal inertia-based approach. The passive-microwave algorithms have relative high accuracy but low spatial resolutions. It can be grouped into the retrieval approaches for soil moisture only and the approaches for relevant parameters in addition to soil moisture. The active-microwave algorithms have generally high accuracy with a high spatial resolution. The algorithms can be divided into three classes: empirical, physical and semi-empirical approaches. In addition, a number of algorithms have been proposed, which combines in particular optical, passivemicrowave, or active-microwave data. Because the algorithms often combine the advantages of the multi-sensors, they can achieve a high accuracy with a good spatial resolution. With the achievement of retrieval techniques, several global soil moisture data sets have been generated. The widely used data sets include the European Remote Sensing satellites/ Meteorological Operational satellite programme (ERS/MetOp) data sets, the Advanced Microwave Scanning Radiometer for EOS (AMSR-E) data sets, and the Soil Moisture and Ocean Salinity (SMOS) data sets. The ERS/MetOp data sets provides global soil moisture data with a spatial resolution of 25-km so far since July, 1991, retrieved from the TU-Wien approach using C-band microwave data. The AMSR-E data sets provides global soil moisture data with a spatial resolution of 25-km for the period from June, 2002 to September, 2011, retrieved from the Land Parameter Retrieval Model (LPRM) using C-band and X-band microwave data. The SMOS data sets provides global soil moisture data with a spatial resolution of 40-km so far since November, 2009, retrieved from the L-band Microwave Emission of the Biosphere model (LMEB) using L-band microwave data. To improve retrieval accuracy of soil moisture, the new satellite sensors are scheduled to be launched into space, for example, the Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR2) in 2013 and the Soil Moisture Active Passive (SMAP) in 2014.

Key words: Soil moisture retrieval algorithm, Optical remote sensing, Microwave remote sensing, Multisensor based retrieval, Global soil moisture data sets

中图分类号:

TP751.1

[1]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(3/4): 125-161.

[2]Wang J R, O’Neill P E, Jackson T J, et al. Multi-frequency measurements of the effects of soil moisture, soil texture, and surface roughness[J]. IEEE Transactions on Geoscience and Remote Sensing, 1983, 21: 44-51.

[3]Puri S, Stephen H, Ahmad S. Relating TRMM precipitation radar land surface backscatter response to soil moisture in the Southern United States[J]. Journal of Hydrology,2011, 402(1/2): 115-125.

[4]Oh Y, Sarabandi K, Ulaby F T. An empirical model and an inversion technique for radar scattering from bare soil surface[J]. IEEE Transactions on Geosciences and Remote Sensing, 1992,30: 370-381.

[5]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.

[6]Shi J C, 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 Geosciences and Remote Sensing, 1997, 35(5): 1 254-1 266.

[7]De Roo 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 Geosciences and Remote Sensing, 2001, 39(4): 864-872.

[8]Barrett B W, Dwyer E, Whelan P. Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques[J]. Remote Sensing,2009, 1(3): 210-242.

[9]Fung A K, Li Z, Chen K S. Backscattering from a randomly rough dielectric surface[J]. IEEE Transactions on Geosciences and Remote Sensing, 1992, 30(2): 356-369.

[10]Paloscia S, Pampaloni P, Pettinato S, et al. The retrieval of soil moisture from ENVISAT/ASAR data[J].EARSeL eProceedings, 2005, 4: 44-51.

[11]Zhan Zhiming, Qin Qiming, Ghulan Abduwasit, et al. NIR-red spectral space based new method for soil moisture monitoring [J]. Science in China (Series D)

,2007, 50(2): 283-289.

[12]Pater N R, Anapashsha R, Kumar S, et al. Assessing potential of MODIS derived temperature/vegetation condition index (TVDI) to infer soil moisture status[J]. International Journal of Remote Sensing, 2009, 30: 23-39.

[13]Gao Z Q, Gao W, Chang N B. Integrating temperature vegetation dryness index (TVDI) and regional water stress index (RWSI) for drought assessment with the aid of LANDSAT TM/ETM+ images[J]. International Journal of Applied Earth Observation and Geoinformation, 2011, 13(3): 495-503.

[14]Yu Fan, Zhao Yingshi. A new semi-empirical model for soil moisture content retrieval by ASAR and TM data in vegetation-covered areas[J]. Science China (Series D), 2011, 41(4): 532-540.[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:D辑, 2011, 41(4): 532-540.]

[15]Li Zhen, Guo Huadong, Shi Jiancheng. Measuring the change of soil Moisture with vegetation cover integration passive and active microwave data[J]. Journal of Remote Sensing, 2002, 6(6): 481-484.[李震, 郭华东, 施建成. 综合主动和被动微波数据监测土壤水分变化[J]. 遥感学报, 2002, 6(6): 481-484.]

[16]Zhao Tianjie, Zhang Lixin, Jiang Lingmei, et al. Joint inversion of soil moisture using active and passive microwave data[J]. Advances in Earth Science, 2009, 24(7): 769-775.[赵天杰, 张立新, 蒋玲梅,等. 利用主被动微波数据联合反演土壤水分[J]. 地球科学进展, 2009, 24(7): 769-775.]

[17]Yan Ming, Zhang Lei. Application the anomalies of vegetation index in crops drought monitoring in Jilin Province[J]. Modern Agricultural Sciences and Technology, 2010, 11: 15-16.[晏明, 张磊. 距平植被指数在吉林省农作物干旱监测中的应用[J]. 现代农业科技, 2010, 11: 15-16.]

[18]Feng Qiang, Tian Guoliang, Wang Angsheng, et al. Remote sensing monitoring of soil humidity using vegetation condition index[J]. Journal of Natural Disasters, 2004, 13(3):81-88. [冯强, 田国良, 王昂生,等. 基于植被状态指数的土壤湿度遥感方法研究[J]. 自然灾害学报, 2004, 13(3):81-88.]

[19]Goward S N, Xue Y, Czajkowski K P. Evaluating land surface moisture conditions from the remotely sensed temperature/vegetation index measurements: An exploration with the simplified simple biosphere model[J]. Remote Sensing of Environment, 2002, 79: 225-242.

[20]Price J C. Using spatial context in satellite data to infer regional scale evapotranspiration[J]. IEEE Transactions on Geosciences and Remote Sensing, 1990, 28: 940-948.

[21]Moran M S, Clarke T R, Inoue Y, et al. Estimating crop water deficit using the relation between surface-air temperature and spectral vegetation index[J]. Remote Sensing of Environment, 1994, 49: 246-263.

[22]Mallick K, Bhattacharya B K, Pater N K. Estimating volumetric surface moisture content for cropped soils using a soil wetness index based on surface temperature and NDVI[J]. Agricultural and Forest Meteorology, 2009, 149: 1 327-1 342.

[23]Hassan Q K, Bourque CP-A,Meng F, et al. A wetness index using terrain-corrected surface temperature and normalized difference vegetation index derived from standard MODIS products: An evaluation of its use in a humid forest-dominated region of eastern Canada[J]. Sensors, 2007, 7: 2 028-2 048.

[24]Du Xiao. Monitoring and Spatio-Temporal Evolution Researching on Vegetation Leaf Water[D]. Beijing: Graduate Unitersity of Chinese Academy of Sciences, 2006.[杜晓. 农作物叶面水遥感监测及其时空特征分析[D].北京:中国科学院研究生院, 2006.]

[25]Zhang Hongwei. Remote Sensing Dynamic Monitoring for Spring Drought of Winter Wheat in Henan Province with the EOS/MODIS Data[D]. Nanjing: Nanjing University of Information Science and Technology, 2009.[张红卫. 基于EOS/MODIS数据的河南省冬小麦春季干旱遥感动态监测研究[D]. 南京: 南京信息工程大学, 2009.]

[26]Sui Hongzhi, Tian Guoliang, Li Fuqin. Two-layer model for monitoring drought using remote sensing[J]. Journal of remote sensing, 1997, 1(3): 220-224.[隋洪智, 田国良, 李付琴. 农田蒸散双层模型及其在干旱遥感监测中的应用[J]. 遥感学报, 1997,1(3):220-224.]

[27]Yu Tao, Tian Guoliang. The application of thermal inertia method the monitoring of soil moisture of north china plain based on NOAA-AVHRR data[J].Journal of Remote Sensing,1997, 1(1): 24-31.[余涛, 田国良. 热惯量法在监测土壤表层水分变化中的研究[J]. 遥感学报, 1997, 1(1): 24-31.]

[28]Wang Litao, Wang Shixin, Zhou Yi, et al. Advances and application analysis of drought monitoring using remote sensing[J]. Journal of Remote Sensing,2011, 15(6): 1 315-1 330.[王丽涛, 王世新, 周艺,等. 旱情遥感监测研究进展与应用案例分析[J]. 遥感学报, 2011, 15(6): 1 315-1 330.]

[29]Zhang Renhua, Sun Xiaomin, Zhu Zhilin, et al. A remote sensing model for monitoring soil evaporation based on differential thermal inertia and its validation[J]. Science in China (Series D), 2003, 46(4): 344-355.

[30]Schmugge T. Applications of passive microwave observations of surface soil moisture[J]. Journal of Hydrology, 1998, 212/213: 188-197.

[31]Wigneron J P, Calvet J C, Pellarin T, et al. Retrieving near-surface soil moisture from microwave radiometric observations: Current status and future plans[J]. Remote Sensing of Environment, 2003, 85(4): 489-506.

[32]Jackson T J, Schmugge T J, Wang J R. Passive microwave sensing of soil moisture under vegetation canopies[J]. Water Resources Research, 1982, 18: 1 137-1 142.

[33]Liu S F, Liou Y A, Wang W J, et al. Retrieval of crop biomass and soil moisture from measured 1.4 and 10.65 brightness temperatures[J]. IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(6): 1 260-1 268.

[34]Jackson T J, Le Vine D M, Swift C T, et al. Large area mapping of soil moisture using the ESTAR passive microwave radiometer in Washita ’92[J]. Remote Sensing of Environment, 1995, 53:27-37.

[35]Van de Griend A A, Owe M. Microwave vegetation optical depth and inverse modelling of soil emissivity using Nimbus/SMMR satellite observations[J]. Meteorology and Atmospheric Physics, 1994, 54: 225-239.

[36]Wigneron J P, Chanzy A, Calvet J C, et al. A simple algorithm to retrieve soil moisture and vegetation biomass using passive microwave measurements over crop fields[J]. Remote Sensing of Environment, 1995, 51: 331-341.

[37]Calvet J C, Chanzy A, Wigneron J P. Surface temperature and soil moisture retrieval in the Sahel from airborne multifrequency microwave radiometry[J]. IEEE Transactions on Geoscience and Remote Sensing, 1996, 34(2): 588-600.

[38]Van de Griend A A, Owe M. The influence of polarization on canopy transmission properties at 6.6 GHz and implications for large scale soil moisture monitoring in semi-arid environments[J]. IEEE Transactions on Geoscience and Remote Sensing, 1994, 32: 409-415.

[39]Njoku E G, Li L. Retrieval of land surface parameters using passive microwave measurements at 6-18 GHz[J]. IEEE Transactions on Geoscience and Remote Sensing, 1999, 37(1): 79-93.

[40]Wigneron J P, Waldteufel P, Chanzy A, et al. Two-D microwave interferometer retrieval capabilities of over land surfaces (SMOS Mission)[J]. Remote Sensing of Environment,2000, 73: 270-282.

[41]Dubois P C, Zyl J V, Engman E T. Measuring soil moisture with imaging radars[J]. IEEE Transactions on Geosciences and Remote Sensing, 1995,33: 915-926.

[42]Engman E T, Chauhan N. Status of microwave soil moisture measurements with remote sensing[J]. Remote Sensing of Environment, 1995, 51(1): 189-198.

[43]Barrett B W, Dwyer E, Whelan P. Soil moisture retrieval from active spaceborne microwave observations: An evaluation of current techniques[J]. Remote Sensing, 2009, 1(3): 210-242.

[44]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.

[45]Thoma D P, Moran M S, Bryant R, et al. Comparison of four models to determine surface soil moisture from C-band radar imagery in a sparsely vegetated semiarid landscape[J]. Water Resources Research, 2006,42:1-12.

[46]Notarnicola C, Angiulli M, Posa F. Soil moisture retrieval from remotely sensed data: Neural network approach versus bayesian method[J]. IEEE Transactions on Geosciences and Remote Sensing, 2008, 46: 547-557.

[47]Nelder J A, Mead R. A simplex method for function minimization[J]. The Computer Journal, 1965, 7(4): 308-313.

[48]Santi E, Paloscia S, Pampaloni P, et al. Retrieval of soil moisture from ENVISAT/ASAR images: A comparison of inversion algorithms[C]//Proceedings of the ESA ENVISAT/ERS Symposium. Austria: Salzburg, 2004.

[49]Paloscia S, Pampaloni P, Pettinato S, et al. A comparison of algorithms for retrieving soil moisture from ENVISAT/ASAR images[J].IEEE Transactions on Geosciences and Remote Sensing,2008, 46: 3 274-3 284.

[50]Wang Shuguo, Li Xin, Han Xujun, 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.[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.]

[51]Ulaby F T, Sarabandi K, McDonald K, et al. Michigan microwave canopy scattering model[J]. International Journal of Remote Sensing, 1990, 11(7): 1 223-1 253.

[52]Attema E P W, Ulaby F T. Vegetation modelled as a water cloud[J]. Radio Science,1978, 13(2): 357-364.

[53]Wagner W, Blschl G, Pampaloni P, et al. Operational readiness of microwave remote sensing of soil moisture for hydrologic applications[J].Nordic hydrology,2007, 38(1): 1-20.

[54]Wilson W J, Yueh S H, Dinardo S J, et al. Observations of soil moisture using a passive and active low-frequency microwave airborne sensor during SGP99[J].IEEE Transactions on Geoscience and Remote Sensing, 2002, 40(12):2 659-2 673.

[55]Li Qin, Zhong Ruofei, Huang Jianxi, et al. Comparison of two retrieval methods with combined passive and active microwave remote sensing observations for soil moisture[J]. Mathematical and Computer Modelling, 2011, 54(3/4): 1 181-1 193.

[56]Zribi M, He′garat-Mascle S L, Ottle′ C, et al. Surface soil moisture estimation from the synergistic use of the (multi-incidence and multi-resolution) active microwave ERS Wind Scatterometer and SAR data[J]. Remote Sensing of Environment, 2003, 86(1): 30-41.

[57]Das N N, Entekhabi D, Njoku E G. An algorithm for merging SMAP radiometer and radar data for high-resolution soil-moisture Retrieval[J]. IEEE Transactions on Geosciences and Remote Sensing, 2011, 49(5): 1 504-1 512.

[58]O′Neill P E, Chauhan N S, Jackson T J. Use of active and passive microwave remote sensing for soil moisture estimation through corn[J]. International Journal of Remote Sensing, 1996, 17(10): 1 851-1 865.

[59]Yang H, Shi J, Li Z, et al. Temporal and spatial soil moisture change pattern detection in an agricultural area using multi-temporal Radarsat ScanSAR data[J].International Journal of Remote Sensing, 2006, 27(19): 4 199-4 212.

[60]Zhou Peng, Ding Jianli, Wang Fei, 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.[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.]

[61]Merlin O, Bitar A A, Walker J P, et al. An improved algorithm for disaggregating microwave-derived soil moisture based on red, near-infrared and thermal-infrared data[J]. Remote Sensing of Environment, 2010, 114(10): 2 305-2 316.

[62]Temimi M, Leconte R, Chaouch N, et al. A combination of remote sensing data and topographic attributes for the spatial and temporal monitoring of soil wetness[J]. Journal of Hydrology, 2010, 388(1/2): 28-40.

[63]Chauhan N S. Spaceborne soil moisture estimation at high resolution: A microwave-optical/IR synergistic approach[J]. International Journal of Remote Sensing, 2003, 24(22): 4 599-4 622.

[64]Narayan U, Lakshmi V, Jackson T J. High-resolution change estimation of soil moisture using L-band radiometer and Radar observations made during the SMEX02 experiments[J]. IEEE Transactions on Geosciences and Remote Sensing, 2006, 44(6): 1 545-1 554.

[65]Wagner W, Lemoine G, Rott H. A method for estimating soil moisture from ERS scatterometer and soil data[J]. Remote Sensing of Environment, 1999, 70(2): 191-207.

[66]Pellarin T, Calvet J C, Wagner W. Evaluation of ERS Scatterometer soil moisture products over a half-degree region in Southwestern France[J]. Geophysical Research Letters, 2006, 33(17):doi:10.1029/2006GL027231.

[67]Drusch M, Wood E F, Gao H, et al. Soil moisture retrieval during the Southern Great Plains Hydrology Experiment 1999: A comparison between experimental remote sensing data and operational products[J]. Water Resources Research, 2004, 40(2):doi:10.1029/2003WK002441.

[68]Njoku E G, Jackson T J, Lakshmi V, et al. Soil moisture retrieval From AMSR-E[J]. IEEE Transactions on Geosciences and Remote Sensing, 2003, 14(2): 215-229.

[69]De Jeu R A M, Wagner W, Holmes T R H, et al. Global Soil Moisture Patterns Observed by Space Borne Microwave Radiometers and Scatterometers[J]. Surveys in Geophysics, 2008, 29(4/5): 399-420.

[70]Mladenova I, Lakshmi V, Jackson T J, et al. Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006[J]. Remote Sensing of Environment, 2011, 115(8): 2 096-2 103.

[71]Bindlish R, Jackson T J, Gasiewski A J, et al. Soil moisture mapping and AMSR-E validation using the PSR in SMEX02[J]. Remote Sensing of Environment, 2006, 103(2): 127-139.

[72]Choi M, Jacobs J M. Temporal variability corrections for Advanced Microwave Scanning Radiometer E (AMSR-E) surface soil moisture: Case Study in Little River Region, Georgia, U.S[J]. Sensors, 2008, 8: 2 617-2 627.

[73]Njoku E G, Ashcroft P, Chan T K, et al. Global Survey and Statistics of Radio-Frequency Interference in AMSR-E Land Observations[J]. IEEE Transactions on Geosciences and Remote Sensing, 2005, 43(5): 938-947.

[74]Albergel C, Zakharova E, Calvet J C, et al. A first assessment of the SMOS data in southwestern France using in situ and airborne soil moisture estimates: The CAROLS airborne campaign[J]. Remote Sensing of Environment, 2011, 115(10): 2 718-2 728.

[75]Albergel C, Rosnay P D, Gruhier C, et al. Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations[J]. Remote Sensing of Environment,2012, 118: 215-226.

[76]Parrens M, Zakharova E, Lafont S, et al. Comparing soil moisture retrievals from SMOS and ASCAT over France[J]. Hydrology and Earth System Sciences, 2012, 16(2): 423-440.

[77]Das N N, Entekhabi D, Njoku E N. An Algorithm for Merging SMAP Radiometer and Radar Data for High-Resolution Soil-Moisture Retrieval[J]. IEEE Transactions on Geosciences and Remote Sensing, 2011, 49(5): 1 504-1 512.

[78]Advanced Land Observing Satellite-2 (ALOS-2)[EB/OL]. Japan: Earth Observation Research Center, Japan Aerospace Exploration Agency, 2012 [2012-08-12].http://www.jaxa.jp/projects/sat/alos2/index_e.html.

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摘要

Satellite Retrieval of Soil Moisture: An Overview