SCoBi-Veg: A Generalized Bistatic Scattering Model of Reflectometry From Vegetation for Signals of Opportunity Applications

SCoBi-Veg stands for Signals of opportunity Coherent Bistatic scattering model for Vegetated terrains. It simulates polarimetric reflectometry of vegetation canopy over a flat ground using a Monte Carlo scheme. The model is aimed at assessing the value of navigation and communication satellite Signals of Opportunity in a range of frequencies from P- to S-bands for remote sensing of a number of geophysical land parameters such as soil moisture and biomass. A fully polarimetric expression for bistatic scattering from a vegetation canopy is first formulated for a general case and is then specialized to the practical case of ground-based/low-altitude platforms with passive receivers overlooking vegetation using the signals transmitted from large distances. Using analytical wave theory in conjunction with distorted Born approximation, the transmit and receive antenna effects (i.e., polarization crosstalk/mismatch, orientation, and altitude) are explicitly accounted for. The forward model developed here enables the understanding of the effect of different geophysical parameters and system configurations on the coherent and incoherent components of the reflected signatures. It can thus help developing robust inverse algorithm for extraction of soil moisture and biomass. The model is applied to P-band signals of geostationary communication satellites to describe polarimetric reflections from tree canopies as observed from down-looking platforms at various altitudes. The relative contributions of diffuse and specular scattering on total reflected power and reflectivity are quantified for various observing scenarios.

[1]  Thomas J. Jackson,et al.  A First-Order Radiative Transfer Model for Microwave Radiometry of Forest Canopies at L-Band , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[2]  Yoshio Yamaguchi,et al.  Basic Concepts of Radar Polarimetry , 1992 .

[3]  Nazzareno Pierdicca,et al.  Use of Satellite Radar Bistatic Measurements for Crop Monitoring: A Simulation Study on Corn Fields , 2013, Remote. Sens..

[4]  A. Ludwig The definition of cross polarization , 1973 .

[5]  Nazzareno Pierdicca,et al.  Forest biomass monitoring with GNSS-R: Theoretical simulations , 2011 .

[6]  C. Zuffada,et al.  Modeling bistatic scattering signatures from sources of opportunity in P-Ka bands , 2017, 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA).

[7]  James L. Garrison,et al.  Bistatic Radar Measurements of Significant Wave Height Using Signals of Opportunity in L-, S-, and Ku-Bands , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Alicia T. Joseph,et al.  Development of a coherent bistatic vegetation model for signal of opportunity applications at VHF/UHF-bands , 2017, 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[9]  Cinzia Zuffada,et al.  Theoretical description of a bistatic system for ocean altimetry using the GPS signal , 2003 .

[10]  A. Fung,et al.  Electromagnetic wave scattering from some vegetation samples , 1988 .

[11]  Estel Cardellach,et al.  Foreword to the Special Issue on GNSS Reflectometry , 2016, IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens..

[12]  Rashmi Shah,et al.  Demonstrating soil moisture remote sensing with observations from the UK TechDemoSat‐1 satellite mission , 2016 .

[13]  Mahta Moghaddam,et al.  Radiative transfer model for microwave bistatic scattering from forest canopies , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[14]  A. T. Joseph,et al.  Development of VHF (240–270 MHz) antennas for SoOp (signal of opportunity) receiver for 6U Cubesat platforms , 2016, 2016 Progress in Electromagnetic Research Symposium (PIERS).

[15]  Adriano Camps,et al.  Land Geophysical Parameters Retrieval Using the Interference Pattern GNSS-R Technique , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[16]  Roger H. Lang,et al.  Simulation of microwave backscatter from a red pine stand , 1995, Remote Sensing.

[17]  Rashmi Shah,et al.  Wetland monitoring with Global Navigation Satellite System reflectometry , 2017, Earth and space science.

[18]  L. Foldy,et al.  The Multiple Scattering of Waves. I. General Theory of Isotropic Scattering by Randomly Distributed Scatterers , 1945 .

[19]  Marco Brogioni,et al.  SAVERS: A Simulator of GNSS Reflections From Bare and Vegetated Soils , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[20]  E. Colin-Koeniguer,et al.  Bistatic scattering from forest components. Part I: coherent polarimetric modelling and analysis of simulated results , 2010 .

[21]  Emanuele Santi,et al.  Global Navigation Satellite Systems Reflectometry as a Remote Sensing Tool for Agriculture , 2012, Remote. Sens..

[22]  Martin Unwin,et al.  Detection and Processing of bistatically reflected GPS signals from low Earth orbit for the purpose of ocean remote sensing , 2005, IEEE Transactions on Geoscience and Remote Sensing.

[23]  Harold Mott,et al.  Polarization in antennas and radar , 1986 .

[24]  Philip Jales,et al.  Spaceborne GNSS-Reflectometry on TechDemoSat-1: Early Mission Operations and Exploitation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[25]  Hyuk Park,et al.  Sensitivity of GNSS-R Spaceborne Observations to Soil Moisture and Vegetation , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Valery U. Zavorotny,et al.  GPS Multipath and Its Relation to Near-Surface Soil Moisture Content , 2010, IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens..

[27]  M. Unwin,et al.  CYGNSS: Enabling the Future of Hurricane Prediction [Remote Sensing Satellites] , 2013, IEEE Geoscience and Remote Sensing Magazine.

[28]  Roger H. Lang,et al.  MICROWAVE SCATTERING MODELS FOR CYLINDRICAL VEGETATION COMPONENTS , 2005 .

[29]  J. L. Garrison,et al.  Recent results on soil moisture remote sensing using P-band signals of opportunity , 2017, 2017 International Conference on Electromagnetics in Advanced Applications (ICEAA).

[30]  Thuy Le Toan,et al.  Deriving forest canopy parameters for backscatter models using the AMAP architectural plant model , 2001, IEEE Trans. Geosci. Remote. Sens..

[31]  Shuanggen Jin,et al.  GNSS-Reflectometry: Forest canopies polarization scattering properties and modeling , 2014 .

[32]  Stephen J. Katzberg,et al.  Foreword to Special Issue on Reflectometry using Global Navigation Satellite Systems and Other Signals of Opportunity (GNSS+R) , 2014, IEEE J. Sel. Top. Appl. Earth Obs. Remote. Sens..

[33]  B. Choudhury,et al.  Effect of surface roughness on the microwave emission from soils , 1979 .

[34]  James L. Garrison,et al.  Demonstration of Bistatic Radar for Ocean Remote Sensing Using Communication Satellite Signals , 2012, IEEE Geoscience and Remote Sensing Letters.

[35]  Kamal Sarabandi,et al.  A Monte Carlo coherent scattering model for forest canopies using fractal-generated trees , 1999, IEEE Trans. Geosci. Remote. Sens..

[36]  Roger H. Lang,et al.  Scattering from arbitrarily oriented dielectric disks in the physical optics regime , 1983 .

[37]  Response of an antenna to arbitrary incident fields , 2005, 2005 IEEE Antennas and Propagation Society International Symposium.

[38]  Roger H. Lang,et al.  Electromagnetic backscattering from a sparse distribution of lossy dielectric scatterers , 1981 .

[39]  Emanuele Santi,et al.  Airborne GNSS-R Polarimetric Measurements for Soil Moisture and Above-Ground Biomass Estimation , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[40]  J. Stephen,et al.  Utilizing GPS To Determine Ionospheric Delay Over the Ocean , 1996 .

[41]  Valery U. Zavorotny,et al.  Vegetation Sensing Using GPS-Interferometric Reflectometry: Theoretical Effects of Canopy Parameters on Signal-to-Noise Ratio Data , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[42]  Raul Onrubia Ibáñez,et al.  Dual-Polarization GNSS-R Interference Pattern Technique for Soil Moisture Mapping , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[43]  A. Schneider,et al.  Electromagnetic scattering from a dielectric cylinder of finite length , 1988 .

[44]  M. Lax Multiple Scattering of Waves , 1951 .

[45]  Jorge Querol,et al.  First Results of a GNSS-R Experiment From a Stratospheric Balloon Over Boreal Forests , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[46]  Adriano Camps,et al.  Tutorial on Remote Sensing Using GNSS Bistatic Radar of Opportunity , 2014, IEEE Geoscience and Remote Sensing Magazine.

[47]  Simon Yueh,et al.  Remote Sensing of Snow Water Equivalent Using P-Band Coherent Reflection , 2017, IEEE Geoscience and Remote Sensing Letters.

[48]  Steven A. Margulis,et al.  HydroCube mission concept: P-Band signals of opportunity for remote sensing of snow and root zone soil moisture , 2017, Remote Sensing.

[49]  O. Torres,et al.  Utilizing calibrated GPS reflected signals to estimate soil reflectivity and dielectric constant: Results from SMEX02 , 2006 .

[50]  Roger H. Lang,et al.  Scattering from thin dielectric disks , 1984 .

[51]  Akira Ishimaru,et al.  Wave propagation and scattering in random media , 1997 .