Refining a Polarimetric Decomposition of Multi-Angular UAVSAR Time Series for Soil Moisture Retrieval Over Low and High Vegetated Agricultural Fields

The model-based polarimetric decomposition under multi-angular condition is refined to estimate soil moisture over agricultural fields covered by different crops from Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) time series. The approach allows to disentangle the vegetation and ground scattering components in order to invert the latter for the retrieval of soil moisture. For the vegetation volume separation, the crop orientations were estimated from SAR observations acquired at different incidence angles, and the associated volume scattering component was subtracted from each acquisition individually. Afterward, the soil moisture was retrieved from both ground scattering components (surface, dihedral), using the developed multi-angular cost functions comprised of dominant Bragg surface (β) or Fresnel dihedral (α) scattering parameters. Compared to former soil moisture retrievals from model-based decomposition of multi-angular polarimetric SAR data, the present refined approach that integrated both ground components, surface and dihedral, is independent of the power attenuation and loss during the microwave propagation through the vegetation. In this way, the ambiguity in the dihedral scattering component (most prominent around 45° incidence angle) was overcome, enabling a more robust retrieval methodology by clearly decoupling the soil and vegetation dielectric constants. The proposed multi-angular approach for soil moisture retrieval was validated with respect to the ground measurements conducted during the Soil Moisture Active Passive Validation Experiment in 2012. Due to the increased number of valid dominant surface/dihedral components which are used to retrieve the soil moisture in the multi-angular approach, an overall retrieval rate of 90%, significantly higher than that of the single-angular condition (50%), is obtained. The results indicate an overall retrieval rmse of 0.07–0.09 m3/m3 for the early crop growth stage, and a rmse of 0.09–0.12 m3/m3 for the later crop development until mature stage. However, the retrieval performance is highly dependent on the crop structure and phenological development stages, but the multi-angular rmse range is mostly lower than all single-angular rmses, indicating better quality of the multi-angular inversion than the single-angular one.

[1]  Fernando Vicente-Guijalba,et al.  A Complete Procedure for Crop Phenology Estimation With PolSAR Data Based on the Complex Wishart Classifier , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Shusen Wang,et al.  Using SAR-Derived Vegetation Descriptors in a Water Cloud Model to Improve Soil Moisture Retrieval , 2018, Remote. Sens..

[3]  Kevin Barraclough,et al.  I and i , 2001, BMJ : British Medical Journal.

[4]  Yoshio Yamaguchi,et al.  Three-Component Power Decomposition for Polarimetric SAR Data Based on Adaptive Volume Scatter Modeling , 2012, Remote. Sens..

[5]  Bruce G. Colpitts,et al.  The integral equation model and surface roughness signatures in soil moisture and tillage type determination , 1998, IEEE Trans. Geosci. Remote. Sens..

[6]  Kalifa Goita,et al.  The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12): Prelaunch Calibration and Validation of the SMAP Soil Moisture Algorithms , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[7]  Simonetta Paloscia,et al.  The relationship between the backscattering coefficient and the biomass of narrow and broad leaf crops , 2001, IEEE Trans. Geosci. Remote. Sens..

[8]  Zhao-Liang Li,et al.  A practical approach for deriving all-weather soil moisture content using combined satellite and meteorological data , 2017 .

[9]  E. Pottier,et al.  Polarimetric Radar Imaging: From Basics to Applications , 2009 .

[10]  Thomas J. Jackson,et al.  Multiple Scattering Effects With Cyclical Correction in Active Remote Sensing of Vegetated Surface Using Vector Radiative Transfer Theory , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[11]  Heather McNairn,et al.  Evaluation of near-surface soil moisture data from an AAFC monitoring network in Manitoba, Canada: Implications for L-band satellite validation , 2015 .

[12]  Irena Hajnsek,et al.  Inversion of surface parameters from polarimetric SAR , 2003, IEEE Trans. Geosci. Remote. Sens..

[13]  Thomas J. Jackson,et al.  Incidence Angle Normalization of Radar Backscatter Data , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[14]  Thomas J. Jackson,et al.  Mapping surface roughness and soil moisture using multi-angle radar imagery without ancillary data , 2008 .

[15]  Roger D. De Roo,et al.  Howdoes dew affect L-band backscatter? analysis of pals data at the Iowa validation site and implications for smap , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[16]  Hiroyoshi Yamada,et al.  Four-component scattering model for polarimetric SAR image decomposition , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Irena Hajnsek,et al.  Assessment of soil moisture effects on L-band radar interferometry , 2015 .

[18]  Yisok Oh,et al.  Quantitative retrieval of soil moisture content and surface roughness from multipolarized radar observations of bare soil surfaces , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[19]  Mehrez Zribi,et al.  Coupling SAR C-Band and Optical Data for Soil Moisture and Leaf Area Index Retrieval Over Irrigated Grasslands , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[20]  J. Wigneron,et al.  Retrieving near-surface soil moisture from microwave radiometric observations: current status and future plans , 2003 .

[21]  J. Kovacs,et al.  Object-oriented crop mapping and monitoring using multi-temporal polarimetric RADARSAT-2 data , 2014 .

[22]  Irena Hajnsek,et al.  Soil Moisture Estimation Under Low Vegetation Cover Using a Multi-Angular Polarimetric Decomposition , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Imen Gherboudj,et al.  Soil moisture retrieval over agricultural fields from multi-polarized and multi-angular RADARSAT-2 SAR data , 2011 .

[24]  Irena Hajnsek,et al.  Soil Moisture Estimation under Vegetation applying Polarimetric Decomposition Techniques , 2009 .

[25]  Irena Hajnsek,et al.  Potential of Estimating Soil Moisture Under Vegetation Cover by Means of PolSAR , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[26]  Simonetta Paloscia,et al.  The potential of multifrequency polarimetric SAR in assessing agricultural and arboreous biomass , 1997, IEEE Trans. Geosci. Remote. Sens..

[27]  Jakob J. van Zyl,et al.  Adaptive Model-Based Decomposition of Polarimetric SAR Covariance Matrices , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[28]  Ramata Magagi,et al.  Comparison of different polarimetric decompositions for soil moisture retrieval over vegetation covered agricultural area , 2017 .

[29]  Kalifa Goita,et al.  Polarimetric Decomposition for Monitoring Crop Growth Status , 2016, IEEE Geoscience and Remote Sensing Letters.

[30]  Juan M. Lopez-Sanchez,et al.  Retrieval of biophysical parameters of agricultural crops using polarimetric SAR interferometry , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Yann Kerr,et al.  Characterizing the dependence of vegetation model parameters on crop structure, incidence angle, and polarization at L-band , 2004, IEEE Transactions on Geoscience and Remote Sensing.

[32]  Laurent Ferro-Famil,et al.  Estimation of Forest Structure, Ground, and Canopy Layer Characteristics From Multibaseline Polarimetric Interferometric SAR Data , 2010, IEEE Transactions on Geoscience and Remote Sensing.

[33]  Heather McNairn,et al.  Integration of optical and Synthetic Aperture Radar (SAR) imagery for delivering operational annual crop inventories , 2009 .

[34]  Stephen L. Durden,et al.  A three-component scattering model for polarimetric SAR data , 1998, IEEE Trans. Geosci. Remote. Sens..

[35]  Thomas J. Jackson,et al.  Radar Vegetation Index for Estimating the Vegetation Water Content of Rice and Soybean , 2012, IEEE Geoscience and Remote Sensing Letters.

[36]  A. P. Annan,et al.  Electromagnetic determination of soil water content: Measurements in coaxial transmission lines , 1980 .

[37]  Amine Merzouki,et al.  A Hybrid (Multi-Angle and Multipolarization) Approach to Soil Moisture Retrieval Using the Integral Equation Model: Preparing for the RADARSAT Constellation Mission , 2015 .

[38]  Hai Liu,et al.  In-Situ Measurement of Soil Permittivity at Various Depths for the Calibration and Validation of Low-Frequency SAR Soil Moisture Models by Using GPR , 2017, Remote. Sens..

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

[40]  Jakob J. van Zyl,et al.  Model-Based Decomposition of Polarimetric SAR Covariance Matrices Constrained for Nonnegative Eigenvalues , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[41]  Eric Pottier,et al.  A Potential Use for the C-Band Polarimetric SAR Parameters to Characterize the Soil Surface Over Bare Agriculture Fields , 2012, IEEE Transactions on Geoscience and Remote Sensing.

[42]  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.

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

[44]  Irena Hajnsek,et al.  An Iterative Generalized Hybrid Decomposition for Soil Moisture Retrieval Under Vegetation Cover Using Fully Polarimetric SAR , 2015, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.