Abstract
A simplified physically-based algorithm for surface soil moisture inversion from satellite microwave radiometer data is presented. The algorithm is based on a radiative transfer model, and the assumption that the optical depth of the vegetation is polarization independent. The algorithm combines the effects of vegetation and roughness into a single parameter. Then the microwave polarization difference index (MPDI) is used to eliminate the effects of surface temperature, and to obtain soil moisture, through a nonlinear iterative procedure. To verify the present algorithm, the 6.9 GHz dual-polarized brightness temperature data from the Advanced Microwave Scanning Radiometer (AMSR-E) were used. Then the soil moisture values retrieved by the present algorithm were validated by in-situ data from 20 sites in the Tibetan Plateau, and compared with both the NASA AMSR-E soil moisture products, and Soil Moisture and Ocean Salinity (SMOS) soil moisture products. The results show that the soil moisture retrieved by the present algorithm agrees better with ground measurements than the two satellite products. The advantage of the algorithm is that it doesn’t require field observations of soil moisture, surface roughness, or canopy biophysical data as calibration parameters, and needs only single-frequency brightness temperature observations during the whole retrieval process.
Similar content being viewed by others
References
Anterrieu E, Khazaal A (2011). One year of RFI detection and quantification with L1a signals provided by SMOS reference radiometers. In: Proceedings of IEEE International Geoscience and Remote Sensing Symposium (IGARSS), 2245–2248
Becker F, Choudhury B J (1988). Relative sensitivity of normalized difference vegetation index (NDVI) and microwave polarization difference index (MPDI) for vegetation and desertification monitoring. Remote Sens Environ, 24(2): 297–311
Berthon L, Mialon A, Cabot F, Bitar A A, Richaume P, Kerr Y, Leroux D, Bircher S, Lawrence H, Quesney A, Jacquette E (2012). CATDS level 3 data product description. CESBIO-SA Technical Report
Chen L, Shi J C, Wigneron J P, Chen K S (2010). A parameterized surface emission model at L-band for soil moisture retrieval. IEEE Geosci Remote Sens Lett, 7(1): 127–130
Chen Y Y, Yang K, Qin J, Zhao L, Tang W J, Han M L (2013). Evaluation of AMSR-E retrievals and GLDAS simulations against observations of a soil moisture network on the central Tibetan plateau. J Geophys Res, D, Atmospheres, 118, doi: 10.1002/jgrd.50301
De Jeu R A M, Owe M (2003). Further validation of a new methodology for surface moisture and vegetation optical depth retrieval. Int J Remote Sens, 24(22): 4559–4578
Dente L, Su Z B, Wen J (2012a). Validation of SMOS soil moisture products over the Maqu and Twente regions. Sensors (Basel), 12(8): 9965–9986
Dente L, Vekerdy Z, Wen J, Su Z B (2012b). Maqu network for validation of satellite-derived soil moisture products. Int J Appl Earth Obs Geoinf, 17: 55–65
Draper C S, Walker J P, Steinle P J, De Jeu R A M, Holmes T R H (2009). An evaluation of AMSR-E derived soil moisture over Australia. Remote Sens Environ, 113(4): 703–710
Du J Y (2012). A method to improve satellite soil moisture retrievals based on Fourier analysis. Geophys Res Lett, 39(15): L15404, doi: 10.1029/2012GL052435
Entekhabi D, Njoku E, O’Neill P E, Kellogg K H, Crow W T, Edelstein W N, Entin J K, Goodman S D, Jackson T J, Johnson J, Kimball J, Piepmeier J R, Koster R D, Martin N, McDonald K C, Moghaddam M, Moran S, Reichle R, Shi J C, Spencer M W, Thrman S W, Tsang L, van Zyl J (2010). The soil moisture active passive (SMAP) mission. Proc IEEE, 98(5): 704–716
Guo P, Shi J C, Liu Q, Du J Y (2013). A new algorithm for soil moisture retrieval with L-band radiometer. IEEE J Sel Top Appl Farth Observ Remote Sens, 6(3): 1147–1155
Hallikainen M T, Ulaby F T, Dobson M C, El-Rayes M A, Wu L K (1985). Microwave dielectric behavior of wet soil-part 1: empirical models and experimental observations. IEEE Trans Geosci Rem Sens, GE-23(1): 25–34
Holmes T R H, De Jeu R A M, Owe M, Dolman A J (2009). Land surface temperature from Ka band (37 GHz) passive microwave observations. J Geophys Res, 114(D4): D04113
Hong S (2010). Global retrieval of small-scale roughness over land surfaces at microwave frequency. J Hydrol (Amst), 389(1–2): 121–126
Jackson T J (1993). Measuring surface soil moisture using passive microwave remote sensing. Hydrol Processes, 7(2): 139–152
Jackson T J, Cosh M H, Bindlish R, Starks P J, Bosch D D, Seyfried M, Goodrich D C, Moran M S, Du J Y (2010). Validation of Advanced Microwave Scanning Radiometer soil moisture products. IEEE Trans Geosci Rem Sens, 48(12): 4256–4272
Jackson T J, Hawley M E, O’Neill P E (1987). Preplanting soil moisture using passive microwave sensors. J Am Water Resour Assoc, 23(1): 11–19
Jackson T J, Le Vine D M, Hsu A Y, Oldak A, Starks P J, Swift C T, Isham J, Haken M (1999). Soil moisture mapping at regional scales using microwave radiometry: the southern Great Plains hydrology experiment. IEEE Trans Geosci Rem Sens, 37(5): 2136–2151
Jacquette E, Al Bita A, Mialon A, Kerr Y, Quesney A, Cabot F, Richaume P (2010). SMOS CATDS level 3 global products over land. Proc SPIE, 7824: 78240K, 78240K-6
Jin R, Li X, Che T (2009). A decision tree algorithm for surface soil freeze/thaw classification over China using SSM/I brightness temperature. Remote Sens Environ, 113(12): 2651–2660
Kerr Y H, Waldteufel P, Wigneron J P, Martinuzzi J, Font J, Berger M (2001). Soil moisture retrieval from space: the soil moisture and ocean salinity (SMOS) mission. IEEE Trans Geosci Rem Sens, 39(8): 1729–1735
Koike T, Nakamura Y, Kaihotsu I, Davaa G, Matsuura N, Tamagawa K, Fujii H (2004). Development of an advanced microwave scanning radiometer (AMSR-E) algorithm of soil moisture and vegetation water content. Annu J Hydraul Eng, JSCE, 48: 217–222
Lacava T, Coviello I, Faruolo M, Mazzeo G, Pergola N, Tramutoli V (2013). A multitemporal investigation of AMSR-E C-band radiofrequency interference. IEEE Trans Geosci Rem Sens, 51(4): 2007–2015
Lu H, Shi J C (2012). Reconstruction and analysis of temporal and spatial variations in surface soil moisture in China using remote sensing. Chin Sci Bull, 57(22): 2824–2834
Mao K B, Tang H J, Zhang L X, Li M C, Guo Y, Zhao D Z (2008). A method for retrieving soil moisture in Tibet region by utilizing microwave index from TRMM/TMI data. Int J Remote Sens, 29(10): 2903–2923
Meesters A G, De Jeu R A M, Owe M (2005). Analytical derivation of the vegetation optical depth from the microwave polarization difference index. IEEE Geosci Remote Sens Lett, 2(2): 121–123
Mladenova I, Lakshmi V, Jackson T J, Walker J P, Merlin O, De Jeu R A M (2011). Validation of AMSR-E soil moisture using L-band airborne radiometer data from National Airborne Field Experiment 2006. Remote Sens Environ, 115(8): 2096–2103
Mo T, Choudhury B J, Schmugge T J, Wang J R, Jackson T J (1982). A model for microwave emission from vegetation-covered fields. J Geophys Res, 87(C13): 11229–11237
Njoku E G, Ashcroft P, Chan T K, Li L (2005). Global survey and statistics of radio-frequency interference in AMSR-E land observations. IEEE Trans Geosci Rem Sens, 43(5): 938–947
Njoku E G, Chan S K (2006). Vegetation and surface roughness effects on AMSR-E land observations. Remote Sens Environ, 100(2): 190–199
Njoku E G, Entekhabi D (1996). Passive microwave remote sensing of soil moisture. J Hydrol (Amst), 184(1–2): 101–129
Njoku E G, Jackson T J, Lakshmi V, Chan T K, Nghiem S V (2003). Soil moisture retrieval from AMSR-E. IEEE Trans Geosci Rem Sens, 41 (2): 215–229
Owe M, De Jeu R A M, Holmes T R H (2008). Multisensor historical climatology of satellite-derived global land surface moisture. J Geophys Res, 113(F1): F01002
Paloscia S, Macelloni G, Santi E (2006). Soil moisture estimates from AMSR-E brightness temperatures by using a dual-frequency algorithm. IEEE Trans Geosci Rem Sens, 44(11): 3135–3144
Paloscia S, Pampaloni P (1988). Microwave polarization index for monitoring vegetation growth. IEEE Trans Geosci Rem Sens, 26(5): 617–621
Reynolds C A, Jackson T J, Rawls W J (2000). Estimating soil water-holding capacities by linking the food and agriculture organization soil map of the world with global pedon databases and continuous pedotransfer functions. Water Resour Res, 36(12): 3653–3662
Roy A, Royer A, Wigneron J P, Langlois A, Bergeron J, Cliche P (2012). A simple parameterization for a boreal forest radiative transfer model at microwave frequencies. Remote Sens Environ, 124: 371–383
Rüdiger C, Calvet J C, Gruhier C, Holmes T R H, De Jeu R A M, Wagner W (2009). An intercomparison of ERS-Scat and AMSR-E soil moisture observations with model simulations over France. J Hydrometeorol, 10(2): 431–447
Saha S K (1995). Assessment of regional soil moisture conditions by coupling satellite sensor data with a soil-plant system heat and moisture balance model. Int J Remote Sens, 16(5): 973–980
Saleh K, Wigneron J P, de Rosnay P, Calvet J C, Kerr Y (2006). Semi-empirical regressions at L-band applied to surface soil moisture retrievals over grass. Remote Sens Environ, 101(3): 415–426
Santi E, Pettinato S, Paloscia S, Pampaloni P, Macelloni G, Brogioni M (2012). An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo. Hydrol Earth Syst Sci, 16(10): 3659–3676
Shi J C, Jackson T, Tao J, Du J, Bindlish R, Lu L, Chen K S (2008). Microwave vegetation indices for short vegetation covers from satellite passive microwave sensor AMSR-E. Remote Sens Environ, 112(12): 4285–4300
Shi J C, Jiang L M, Zhang L X, Chen K S, Wigneron J P, Chanzy A, Jackson T J (2006). Physically based estimation of bare-surface soil moisture with the passive radiometers. IEEE Trans Geosci Rem Sens, 44(11): 3145–3153
Skou N, Misra S, Balling J E, Kristensen S S, Sobjaerg S S (2010). L-band RFI as experienced during airborne campaigns in preparation for SMOS. IEEE Trans Geosci Rem Sens, 48(3): 1398–1407
Su Z, Wen J, Dente L, van der Velde R, Wang L, Ma Y, Yang K, Hu Z (2011). The Tibetan Plateau observatory of plateau scale soil moisture and soil temperature (Tibet-Obs) for quantifying uncertainties in coarse resolution satellite and model products. Hydrol Earth Syst Sci, 15(7): 2303–2316
Van de Griend A A, Owe M (1994). Microwave vegetation optical depth and inverse modelling of soil emissivity using Nimbus/SMMR satellite observations. Meteorol Atmos Phys, 54(1–4): 225–239
Wagner W, Naeimi V, Scipal K, de Jeu R A M, Martínez-Fernández J (2007). Soil moisture from operational meteorological satellites. Hydrogeol J, 15(1): 121–131
Wang J R, Choudhury B J (1981). Remote sensing of soil moisture content over bare fields at 1.4 GHz frequency. J Geophys Res, 86 (C6): 5277–5282
Wang L L, Qu J J (2009). Satellite remote sensing applications for surface soil moisture monitoring: a review. Front Earth Sci China, 3 (2): 237–247
Wen J, Su Z B, Ma Y M (2003). Determination of land surface temperature and soil moisture from tropical rainfall measuring mission/microwave imager remote sensing data. J Geophys Res, 108(D2): 4038
Wigneron J P, Calvet J C, Pellarin T, Van de Griend A A, Berger M, Ferrazzoli P (2003). Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans. Remote Sens Environ, 85(4): 489–506
Wigneron J P, Laguerre L, Kerr Y H (2001). A simple parameterization of the L-Band microwave emission from rough agricultural soils. IEEE Trans Geosci Rem Sens, 39(8): 1697–1707
Xie H, Ye J S, Liu X M, E C Y (2010). Warming and drying trends on the Tibetan Plateau (1971–2005). Theor Appl Climatol, 101(3–4): 241–253
Zhang X F, Zhao J P, Sun Q, Wang X Y, Guo Y L, Li J (2011). Soil moisture retrieval from AMSR-E data in Xinjiang (China): models and validation. IEEE J Sel Top Appl Earth Observ Remote Sens, 4 (1): 117–127
Zhao T J, Zhang L X, Jiang L M, Zhao S J, Chai L N, Jin R (2011a). A new soil freeze/thaw discriminant algorithm using AMSR-E passive microwave imagery. Hydrol Processes, 25(11): 1704–1716
Zhao T J, Zhang L X, Shi J C, Jiang L M (2011b). A physically based statistical methodology for surface soil moisture retrieval in the Tibet Plateau using microwave vegetation indices. J Geophys Res, 116 (D8): D08116
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Zeng, J., Li, Z., Chen, Q. et al. A simplified physically-based algorithm for surface soil moisture retrieval using AMSR-E data. Front. Earth Sci. 8, 427–438 (2014). https://doi.org/10.1007/s11707-014-0412-4
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11707-014-0412-4