Toward an Operational Bare Soil Moisture Mapping Using TerraSAR-X Data Acquired Over Agricultural Areas

TerraSAR-X data are processed for an “operational” mapping of bare soils moisture in agricultural areas. Empirical relationships between TerraSAR-X signal and soil moisture were established and validated over different North European agricultural study sites. The results show that the mean error on the soil moisture estimation is less than 4% regardless of the TerraSAR-X configuration (incidence angle, polarization) and the soil surface characteristics (soil surface roughness, soil composition). Furthermore, the potential of TerraSAR-X data (signal, texture features) to discriminate bare soils from other land cover classes in an agricultural watershed was evaluated. The mean signal backscattered from bare soils can be easily differentiated from signals from other land cover classes when the neighboring plots are covered by fully developed crops. This was observed regardless of the TerraSAR-X configuration and the soil moisture conditions. When neighboring plots are covered by early growth crops, a TerraSAR-X image acquired under wet conditions can be useful for discriminating bare soils. Bare soil masks were calculated by object-oriented classifications of mono-configuration TerraSAR-X data. The overall accuracies of the bare soils mapping were higher than 84% for validation based on object and pixel. The bare soils mapping method and the soil moisture relationships were applied to TerraSAR-X images to generate soil moisture maps. The results show that TerraSAR-X sensors provide useful data for monitoring the spatial variations of soil moisture at the within-plot scale. The methods of bare soils moisture mapping developed in this paper can be used in operational applications in agriculture, and hydrology.

[1]  Pascale C. Dubois,et al.  Measuring soil moisture with imaging radars , 1995, IEEE Trans. Geosci. Remote. Sens..

[2]  Amine Merzouki,et al.  Mapping Soil Moisture Using RADARSAT-2 Data and Local Autocorrelation Statistics , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[3]  D. He,et al.  Evaluation of textural and multipolarization radar features for crop classification , 1995, IEEE Trans. Geosci. Remote. Sens..

[4]  T. J. Pultz,et al.  Case studies demonstrating the hydrological applications of C-band multipolarized and polarimetric SAR , 2004 .

[5]  U. Wegmüller.  The effect of freezing and thawing on the microwave signatures of bare soil. , 1990 .

[6]  Nicolas Baghdadi,et al.  Multitemporal Observations of Sugarcane by TerraSAR-X Images , 2010, Sensors.

[7]  J. V. Soares,et al.  An investigation of the selection of texture features for crop discrimination using SAR imagery , 1997 .

[8]  Morton J. Canty,et al.  SEaTH - A new tool for automated feature extraction in the context of object-oriented image analysis , 2006 .

[9]  Dharmendra Singh,et al.  A Fusion Approach to Retrieve Soil Moisture With SAR and Optical Data , 2012, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[10]  Mehrez Zribi,et al.  New methodology for soil surface moisture estimation and its application to ENVISAT-ASAR multi-incidence data inversion , 2005 .

[11]  Seiho Uratsuka,et al.  Texture statistics for classification of land use with multitemporal JERS-1 SAR single-look imagery , 1999, IEEE Trans. Geosci. Remote. Sens..

[12]  M. Mróz,et al.  POTENTIAL OF TERRASAR-X STRIPMAP DATA IN EARLY AND RAPID AGRICULTURAL CROPS MAPPING , 2008 .

[13]  A. Vacca,et al.  Sheet and Rill Erosion , 2006 .

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

[15]  Kamal Sarabandi,et al.  An empirical model and an inversion technique for radar scattering from bare soil surfaces , 1992, IEEE Trans. Geosci. Remote. Sens..

[16]  Mehrez Zribi,et al.  Soil moisture estimation using multi‐incidence and multi‐polarization ASAR data , 2006 .

[17]  A. Chehbouni,et al.  Soil surface moisture estimation over a semi-arid region using ENVISAT ASAR radar data for soil evaporation evaluation , 2011 .

[18]  A. Beaudoin,et al.  SAR observations and modeling of the C-band backscatter variability due to multiscale geometry and soil moisture , 1990 .

[19]  Manfred F. Buchroithner,et al.  Field-based landcover classification using TerraSAR-X texture analysis , 2011 .

[20]  I. Niemeyer,et al.  SEATH-A NEW TOOL FOR AUTOMATED FEATURE EXTRACTION IN THE CONTEXT OF OBJECT-BASED IMAGE ANALYSIS , 2006 .

[21]  Julien Radoux,et al.  Comparison of pixel- and object-based sampling strategies for thematic accuracy assessment , 2008 .

[22]  François Papy,et al.  Influence des systèmes de culture sur les risques d'érosion par ruissellement concentré. I. — Analyse des conditions de déclenchement de l'érosion , 1988 .

[23]  Frédéric Baup,et al.  Analysis of TerraSAR-X data sensitivity to bare soil moisture, roughness, composition and soil crust , 2011 .

[24]  Quan Sun,et al.  Soil Moisture Retrieval From AMSR-E Data in Xinjiang (China): Models and Validation , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Shaun Quegan,et al.  Modeling microwave interactions with crops and comparison with ERS-2 SAR observations , 2000, IEEE Trans. Geosci. Remote. Sens..

[26]  F. Ulaby,et al.  Radar mapping of surface soil moisture , 1996 .

[27]  B. Surv,et al.  ESTIMATING SOIL MOISTURE PROFILE DYNAMICS FROM NEAR-SURFACE SOIL MOISTURE MEASUREMENTS AND STANDARD METEOROLOGICAL DATA , 2000 .

[28]  Qiang Wu,et al.  A framework for risk assessment on soil erosion by water using an integrated and systematic approach , 2007 .

[29]  A. Weimann,et al.  Soil moisture estimation with ERS-1 SAR data in the East-German loess soil area , 1998 .

[30]  Yann Kerr,et al.  Soil moisture retrieval from space: the Soil Moisture and Ocean Salinity (SMOS) mission , 2001, IEEE Trans. Geosci. Remote. Sens..

[31]  Mehrez Zribi,et al.  Use of TerraSAR-X Data to Retrieve Soil Moisture Over Bare Soil Agricultural Fields , 2012, IEEE Geoscience and Remote Sensing Letters.

[32]  M. Eimberck.  Facteurs d'érodibilité des sols limoneux : réflexions à partir du cas du Pays de Caux , 1989 .

[33]  D. R. Fatland,et al.  Change detection on Alaska's North Slope using repeat-pass ERS-1 SAR images , 1993, IEEE Trans. Geosci. Remote. Sens..

[34]  Mohammed Dabboor,et al.  A multi-level segmentation methodology for dual-polarized SAR data , 2011, Int. J. Appl. Earth Obs. Geoinformation.

[35]  L. Ferro-Famil,et al.  Segmentation of polarimetric SAR images , 2001, IGARSS 2001. Scanning the Present and Resolving the Future. Proceedings. IEEE 2001 International Geoscience and Remote Sensing Symposium (Cat. No.01CH37217).

[36]  Geoff Smith,et al.  An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities , 2003 .

[37]  C. G. J. Schotten,et al.  Assessment of the capabilities of multi-temporal ERS-1 SAR data to discriminate between agricultural crops , 1995 .

[38]  A. Fung.  Microwave Scattering and Emission Models and their Applications , 1994 .

[39]  James R. Irons,et al.  Texture transforms of remote sensing data , 1981 .

[40]  Malcolm Davidson,et al.  Dense Temporal Series of C- and L-band SAR Data for Soil Moisture Retrieval Over Agricultural Crops , 2011, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[41]  F. Ulaby,et al.  Microwave Backscatter Dependence on Surface Roughness, Soil Moisture, and Soil Texture: Part I-Bare Soil , 1978, IEEE Transactions on Geoscience Electronics.

[42]  Jakob J. van Zyl,et al.  Change detection techniques for ERS-1 SAR data , 1993, IEEE Trans. Geosci. Remote. Sens..

[43]  Brian W. Barrett,et al.  Soil Moisture Retrieval from Active Spaceborne Microwave Observations: An Evaluation of Current Techniques , 2009, Remote. Sens..

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

[45]  Fawwaz T. Ulaby,et al.  Land-cover classification and estimation of terrain attributes using synthetic aperture radar , 1995 .

[46]  B. Bouman,et al.  Crop parameter estimation from ground-based x-band (3-cm wave) radar backscattering data , 1991 .

[47]  Eric Rignot,et al.  Winter and Spring Thaw as Observed with Imaging Radar at BOREAS , 1997 .

[48]  F. Ulaby,et al.  Textural Infornation in SAR Images , 1986, IEEE Transactions on Geoscience and Remote Sensing.

[49]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[50]  Thomas L. Ainsworth,et al.  A wavelet multiresolution technique for polarimetric texture analysis and segmentation of SAR images , 2004, IGARSS 2004. 2004 IEEE International Geoscience and Remote Sensing Symposium.

[51]  Manfred Reich,et al.  Forest monitoring with TerraSAR-X: first results , 2010, European Journal of Forest Research.

[52]  Sylvie Le Hégarat-Mascle,et al.  Soil moisture estimation from ERS/SAR data: toward an operational methodology , 2002, IEEE Trans. Geosci. Remote. Sens..

[53]  Helko Breit,et al.  TerraSAR-X Ground Segment Basic Product Specification Document , 2008 .

[54]  Christophe Sannier,et al.  Estimating Surface Soil Moisture from TerraSAR-X Data over Two Small Catchments in the Sahelian Part of Western Niger , 2011, Remote. Sens..

[55]  Mehrez Zribi,et al.  Operational performance of current synthetic aperture radar sensors in mapping soil surface characteristics in agricultural environments: application to hydrological and erosion modelling , 2008 .

[56]  Nagendra Prasad Pathak,et al.  Microwave specular scattering response of soil texture at X-band , 2009 .

[57]  Hari Shanker Srivastava,et al.  Use of multiincidence angle RADARSAT-1 SAR data to incorporate the effect of surface roughness in soil moisture estimation , 2003, IEEE Trans. Geosci. Remote. Sens..

[58]  M. Zribi,et al.  A new empirical model to retrieve soil moisture and roughness from C-band radar data , 2003 .

[59]  E. Martin,et al.  Operational Mapping of Soil Moisture Using Synthetic Aperture Radar Data: Application to the Touch Basin (France) , 2007, Sensors.