Inverting surface soil moisture information from satellite altimetry over arid and semi-arid regions

Abstract Monitoring surface soil moisture (SSM) variability is essential for understanding hydrological processes, vegetation growth, and interactions between land and atmosphere. Due to sparse distribution of in-situ soil moisture networks, over the last two decades, several active and passive radar satellite missions have been launched to provide information that can be used to estimate surface conditions and subsequently soil moisture content of the upper few cm soil layers. Some recent studies reported the potential of satellite altimeter backscatter to estimate SSM, especially in arid and semi-arid regions. They also pointed out some difficulties of such technique including: (i) the noisy behavior of the backscatter estimations mainly caused by surface water in the radar foot-print, (ii) the assumptions for converting altimetry backscatter to SSM, and (iii) the need for interpolating between the tracks. In this study, we introduce a new inversion framework to retrieve soil moisture information from along-track altimetry measurements. First, 20Hz along-track nadir radar backscatter is estimated by post-processing waveforms from Jason-2 (Ku- and C-Band during 2008–2014) and Envisat (Ku- and S-Band during 2002–2008). This provides backscatter measurements every ∼300m along-track within every ∼10 days from Jason, and every ∼35days from Envisat observations. Empirical orthogonal base-functions (EOFs) are then derived from soil moisture simulations of a hydrological model, and used as constraints within the inversion. Finally, along-track altimetry reconstructed surface soil moisture (ARSSM) storage is inverted by fitting these EOFs to the altimeter backscatter. The framework is tested in arid and semi-arid Western Australia, for which a high resolution hydrological model (the Australian Water Resource Assessment, AWRA model) is available. Our ARSSM products are also validated against Soil Moisture and Ocean Salinity (SMOS) L3 products, for which maximum correlation coefficients of bigger than 0.8 are found. Our results also indicate that ARSSM can validate the simulation of hydrological models at least at seasonal time scales.

[1]  A. Cazenave,et al.  Satellite altimetry and earth sciences : a handbook of techniques and applications , 2001 .

[2]  Alenia Aerospazio,et al.  ENVISAT RA-2 ADVANCED RADAR ALTIMETER : INSTRUMENT DESIGN AND PRE-LAUNCH PERFORMANCE ASSESSMENT REVIEW , 1999 .

[3]  Fabio Del Frate,et al.  Application of Neural Networks to Soil Moisture Retrievals from L-Band Radiometric Data , 2008, IGARSS 2008 - 2008 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Arnaud Mialon,et al.  The SMOS Soil Moisture Retrieval Algorithm , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[5]  Yuei-An Liou,et al.  Retrieving soil moisture from simulated brightness temperatures by a neural network , 2001, IEEE Trans. Geosci. Remote. Sens..

[6]  Jan Vanderborght,et al.  Brightness Temperature and Soil Moisture Validation at Different Scales During the SMOS Validation Campaign in the Rur and Erft Catchments, Germany , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[7]  G. Brown.  The average impulse response of a rough surface and its applications , 1977 .

[8]  Chad W. Higgins,et al.  Evapotranspiration: A process driving mass transport and energy exchange in the soil‐plant‐atmosphere‐climate system , 2012 .

[9]  Pierre Borderies,et al.  Radar altimetry backscattering signatures at Ka, Ku, C, and S bands over West Africa , 2015 .

[10]  Thomas R. H. Holmes,et al.  An evaluation of AMSR–E derived soil moisture over Australia , 2009 .

[11]  Thomas J. Jackson,et al.  Soil moisture retrieval from AMSR-E , 2003, IEEE Trans. Geosci. Remote. Sens..

[12]  Pierre Borderies,et al.  Spaceborne altimetry and scatterometry backscattering signatures at C- and Ku-bands over West Africa , 2015 .

[13]  Catherine Prigent,et al.  Altimetry backscattering signatures at Ku and S bands over land and ice sheets , 2015, SPIE Remote Sensing.

[14]  Frédéric Frappart,et al.  Estimating surface soil moisture over Sahel using ENVISAT radar altimetry , 2012 .

[15]  Jeffrey P. Walker,et al.  THE GLOBAL LAND DATA ASSIMILATION SYSTEM , 2004 .

[16]  B. D. Beckley,et al.  Investigating the Performance of the Jason-2/OSTM Radar Altimeter over Lakes and Reservoirs , 2010 .

[17]  C. Celani,et al.  RA-2 SIGMA-0 ABSOLUTE CALIBRATION , 2003 .

[18]  H. Jay Zwally,et al.  Analysis and retracking of continental ice sheet radar altimeter waveforms , 1983 .

[19]  Jiancheng Shi,et al.  The Soil Moisture Active Passive (SMAP) Mission , 2010, Proceedings of the IEEE.

[20]  Tim R. McVicar,et al.  Climate‐related trends in Australian vegetation cover as inferred from satellite observations, 1981–2006 , 2009 .

[21]  Geoff A. W. West,et al.  Use of Soil Moisture Variability in Artificial Neural Network Retrieval of Soil Moisture , 2009, Remote. Sens..

[22]  Jeff Ridley,et al.  Radar backscatter characteristics of a desert surface , 1996 .

[23]  C. Shum,et al.  THE ACCURACY AND APPLICATIONS OF SATELLITE ALTIMETRY , 1995 .

[24]  D. Jones,et al.  High-quality spatial climate data-sets for Australia , 2009 .

[25]  W. Wagner,et al.  A Method for Estimating Soil Moisture from ERS Scatterometer and Soil Data , 1999 .

[26]  Fabrice Papa,et al.  Use of the Topex-Poseidon dual-frequency radar altimeter over land surfaces , 2003 .

[27]  J. Thepaut,et al.  The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .

[28]  Yann Kerr,et al.  SMOS Validation and the COSMOS Campaigns , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[29]  Yann Kerr,et al.  Downscaling SMOS-Derived Soil Moisture Using MODIS Visible/Infrared Data , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[30]  W. Wagner,et al.  Initial soil moisture retrievals from the METOP‐A Advanced Scatterometer (ASCAT) , 2007 .

[31]  Richard K. Moore,et al.  Microwave Remote Sensing, Active and Passive , 1982 .

[32]  Yi Y. Liu,et al.  Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals , 2011 .

[33]  P. Berry,et al.  Estimation of Contamination of ERS-2 and POSEIDON Satellite Radar Altimetry Close to the Coasts of Australia , 2002 .

[34]  A. Lopes,et al.  Preliminary analysis of ERS-1 wind scatterometer data over land surfaces , 1995 .

[35]  R. Preisendorfer,et al.  Principal Component Analysis in Meteorology and Oceanography , 1988 .

[36]  Y. Kerr,et al.  Catchment scale validation of SMOS and ASCAT soil moisture products using hydrological modeling and temporal stability analysis , 2014 .

[37]  Jin Teng,et al.  The Australian Water Resource Assessment Modelling System (AWRA) , 2013 .

[38]  G. Carayon,et al.  Poseidon-3 Radar Altimeter: New Modes and In-Flight Performances , 2010 .

[39]  Edward P. Glenn,et al.  Actual evapotranspiration estimation by ground and remote sensing methods: the Australian experience , 2011 .

[40]  John O. Carter,et al.  Using spatial interpolation to construct a comprehensive archive of Australian climate data , 2001, Environ. Model. Softw..

[41]  Chun-Hsu Su,et al.  SMOS soil moisture retrievals using the land parameter retrieval model: Evaluation over the Murrumbidgee Catchment, southeast Australia , 2015 .

[42]  Philippe Richaume,et al.  Soil Moisture Retrieval Using Neural Networks: Application to SMOS , 2015, IEEE Transactions on Geoscience and Remote Sensing.