Cramer–Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing

Signals of opportunity (SoOp) reflectometry (SoOp-R) is a maturing field for geophysical remote sensing as evidenced by the growing number of airborne and spaceborne experiments. As this approach receives more attention, it is worth analyzing SoOp-R's capabilities to retrieve subsurface soil moisture (SM) by leveraging communication and navigation satellite transmitters. In this research, the Cramer–Rao lower bound (CRLB) is used to identify the effects of variable SoOp-R parameters on the best achievable estimation error for root-zone soil moisture (RZSM). This study investigates the use of multiple frequency, polarization, and incidence angle measurement configurations on a two-layered dielectric profile. The results also detail the effects of variable SM conditions on the capability of SoOp-R systems to predict subsurface SM. The most prevalent observation is the importance of using at least two frequencies to limit uncertainties from subsurface SM estimates. If at least two frequencies are used, the CRLB of a profile is retrievable within the root-zone depending on the surface SM content as well as the number of independent measurements of the profile. For a depth of 30 cm, it is observed that a CRLB corresponding to 4% RZSM estimation accuracy is achievable with as few as two dual-frequency-based SoOp-R measurements. For this depth, increasing number of measurements provided by polarization and incidence angle allow for sensing of increasingly wet SM profile structures. This study, overall, details a methodology by which SoOp-R receiver system can be designed to achieve a desired CRLB using a tradeoff study between the available measurements and SM profile.

[1]  Leung Tsang,et al.  A NUMERICAL KIRCHHOFF SIMULATOR FOR GNSS-R LAND APPLICATIONS , 2019, Progress In Electromagnetics Research.

[2]  Eric E. Small,et al.  Description of the UCAR/CU Soil Moisture Product , 2020, Remote. Sens..

[3]  J. L. Garrison,et al.  SNOOPI: A Technology Validation Mission for P-band Reflectometry using Signals of Opportunity , 2019, IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium.

[4]  Cyril Botteron,et al.  Derivation of the Cramér-Rao Bound in the GNSS-Reflectometry Context for Static, Ground-Based Receivers in Scenarios with Coherent Reflection , 2016, Sensors.

[5]  Simonetta Paloscia,et al.  Exploiting GNSS signals for soil moisture and vegetation biomass retrieval , 2015, 2015 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[6]  Stephen J. Katzberg,et al.  Airborne P-band Signal of Opportunity (SoOP) demonstrator instrument; status update , 2016, 2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS).

[7]  Ali Cafer Gürbüz,et al.  High Spatio-Temporal Resolution CYGNSS Soil Moisture Estimates Using Artificial Neural Networks , 2019, Remote. Sens..

[8]  Mehmet Kurum,et al.  Response of GNSS-R on Dynamic Vegetated Terrain Conditions , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[9]  Stephen J. Katzberg,et al.  Detection of ocean reflected GPS signals: theory and experiment , 1997, Proceedings IEEE SOUTHEASTCON '97. 'Engineering the New Century'.

[10]  S. Jones,et al.  Ground, Proximal, and Satellite Remote Sensing of Soil Moisture , 2019, Reviews of Geophysics.

[11]  Nazzareno Pierdicca,et al.  Bistatic Coherent Scattering From Rough Soils With Application to GNSS Reflectometry , 2020, IEEE Transactions on Geoscience and Remote Sensing.

[12]  Mahta Moghaddam,et al.  P-Band Radar Retrieval of Subsurface Soil Moisture Profile as a Second-Order Polynomial: First AirMOSS Results , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[13]  Abi Komanduru Remote sensing of snow using bistatic radar reflectometry , 2016 .

[14]  Alicia T. Joseph,et al.  SCoBi-Veg: A Generalized Bistatic Scattering Model of Reflectometry From Vegetation for Signals of Opportunity Applications , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[15]  V. L. Mironov,et al.  Temperature Dependable Microwave Dielectric Model for Frozen Soils , 2009 .

[16]  Eric E. Small,et al.  Soil Moisture Sensing Using Spaceborne GNSS Reflections: Comparison of CYGNSS Reflectivity to SMAP Soil Moisture , 2018 .

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

[18]  Nazzareno Pierdicca,et al.  Space-Borne GNSS-R Signal Over a Complex Topography: Modeling and Validation , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[19]  Valery U. Zavorotny,et al.  Effects of Near-Surface Soil Moisture on GPS SNR Data: Development of a Retrieval Algorithm for Soil Moisture , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[20]  Philip Jales,et al.  The First Application of Stare Processing to Retrieve Mean Square Slope Using the SGR-ReSI GNSS-R Experiment on TDS-1 , 2016, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[21]  Randall Rose,et al.  The CYGNSS nanosatellite constellation hurricane mission , 2012, 2012 IEEE International Geoscience and Remote Sensing Symposium.

[22]  S. Gleason,et al.  Remote sensing of ocean, ice and land surfaces using bistatically scattered GNSS signals from low Earth orbit , 2006 .

[23]  Nazzareno Pierdicca,et al.  Bistatic Radar Systems at Large Baselines for Ocean Observation , 2018, IEEE Transactions on Geoscience and Remote Sensing.

[24]  S. Kay Fundamentals of statistical signal processing: estimation theory , 1993 .

[25]  Steven A. Margulis,et al.  P-Band Signals of Opportunity for Remote Sensing of Root Zone Soil Moisture , 2018, IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium.

[26]  Ali Cafer Gürbüz,et al.  Machine Learning-Based CYGNSS Soil Moisture Estimates over ISMN sites in CONUS , 2020, Remote. Sens..

[27]  Simon Yueh,et al.  Experimental Demonstration of Soil Moisture Remote Sensing Using P-Band Satellite Signals of Opportunity , 2020, IEEE Geoscience and Remote Sensing Letters.

[28]  Manuel Martín-Neira,et al.  The PARIS Ocean Altimeter In-Orbit Demonstrator , 2011, IEEE Transactions on Geoscience and Remote Sensing.

[29]  F. Ulaby Radar measurement of soil moisture content , 1974 .

[30]  Marco Brogioni,et al.  Radar Bistatic Configurations for Soil Moisture Retrieval: A Simulation Study , 2008, IEEE Transactions on Geoscience and Remote Sensing.

[31]  Andrew J. Kerr Theoretical performance bounds for the estimation of target parameters from electromagnetic induction data , 2020 .

[32]  Randolph L. Moses,et al.  The Cramer-Rao bound for pole and amplitude coefficient estimates of damped exponential signals in noise , 1993, IEEE Trans. Signal Process..

[33]  Shuanggen Jin,et al.  Pan-tropical soil moisture mapping based on a three-layer model from CYGNSS GNSS-R data , 2020 .

[34]  R. Machuzak,et al.  AirMOSS: An Airborne P-band SAR to measure root-zone soil moisture , 2012, 2012 IEEE Radar Conference.

[35]  Ali Cafer Gürbüz,et al.  SCoBi Multilayer: A Signals of Opportunity Reflectometry Model for Multilayer Dielectric Reflections , 2020, Remote. Sens..