Normalized Microwave Reflection Index: A Vegetation Measurement Derived From GPS Networks

Measurements of vegetation state are required both for modeling and satellite validation. Reflected GPS signals recorded by the Plate Boundary Observatory network provide a source of new information about vegetation state in the western United States and Alaska. The GPS ground stations were installed between 2005 and 2008 to measure plate boundary deformation. They operate continuously and transmit their data to a public facility at least once per day. However, they also act as bi-static radars by recording the interference between a direct GPS signal (transmitted at 1.5 GHz) and a reflected GPS signal. The frequency of this interference pattern primarily depends on the vertical distance between the antenna and the ground reflector. As an L-band sensor, the amplitude of the interference pattern depends on vegetation water content. A daily vegetation metric that depends on reflection amplitudes, Normalized Microwave Reflection Index (NMRI), is defined. A method for removing outliers caused by snow and rain is described. The footprint of NMRI depends on the antenna height and local terrain. The minimum footprint is 1000 m2. A database of more than 300 station NMRI time series has been compiled; these data span the period from 2007 to 2012. Comparisons between NMRI and in situ sampling of vegetation state are the subject of a companion paper.

[1]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 1 - Theoretical approach , 2002 .

[2]  Stephen J. Katzberg,et al.  Wind speed measurement using forward scattered GPS signals , 2002, IEEE Trans. Geosci. Remote. Sens..

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

[4]  W. Prescott,et al.  Global Positioning System Measurements for Crustal Deformation: Precision and Accuracy , 1989, Science.

[5]  F. Ulaby,et al.  Microwave radar response to canopy moisture, leaf-area index, and dry weight of wheat, corn, and sorghum☆ , 1981 .

[6]  M. Vall-llossera,et al.  Review of crop growth and soil moisture monitoring from a ground‐based instrument implementing the Interference Pattern GNSS‐R Technique , 2011 .

[7]  Eric E. Small,et al.  Normalized Microwave Reflection Index: Validation of Vegetation Water Content Estimates From Montana Grasslands , 2014, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[8]  A. Bilich Improving the precision and accuracy of geodetic GPS: Applications to multipath and seismology , 2006 .

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

[10]  B. Gao NDWI—A normalized difference water index for remote sensing of vegetation liquid water from space , 1996 .

[11]  Emanuele Santi,et al.  The contribution of multitemporal SAR data in assessing hydrological parameters , 2004, IEEE Geoscience and Remote Sensing Letters.

[12]  J. Sperry,et al.  Water Relations of Plants and Soils , 1995 .

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

[14]  D. Roberts,et al.  Deriving Water Content of Chaparral Vegetation from AVIRIS Data , 2000 .

[15]  E. Small,et al.  Use of GPS receivers as a soil moisture network for water cycle studies , 2008 .

[16]  J. Paruelo,et al.  ANPP ESTIMATES FROM NDVI FOR THE CENTRAL GRASSLAND REGION OF THE UNITED STATES , 1997 .

[17]  G. Gutman,et al.  The derivation of the green vegetation fraction from NOAA/AVHRR data for use in numerical weather prediction models , 1998 .

[18]  J. D. Tarpley,et al.  The multi‐institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system , 2004 .

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

[20]  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.

[21]  C. Zuffada,et al.  5‐cm‐Precision aircraft ocean altimetry using GPS reflections , 2002 .

[22]  Jeffrey T. Freymueller,et al.  The Accidental Tide Gauge: A GPS Reflection Case Study From Kachemak Bay, Alaska , 2013, IEEE Geoscience and Remote Sensing Letters.

[23]  Felipe G. Nievinski,et al.  Forward modeling of GPS multipath for near-surface reflectometry and positioning applications , 2014, GPS Solutions.

[24]  Jakob J. van Zyl,et al.  A Time-Series Approach to Estimate Soil Moisture Using Polarimetric Radar Data , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[25]  P. Axelrad,et al.  Use of the Correct Satellite Repeat Period to Characterize and Reduce Site-Specific Multipath Errors , 2005 .

[26]  S. Tarantola,et al.  Designing a spectral index to estimate vegetation water content from remote sensing data: Part 2. Validation and applications , 2002 .

[27]  J. K. Ray,et al.  Synergy Between Global Positioning System Code, Carrier, and Signal-to-Noise Ratio Multipath Errors , 2001 .

[28]  H. Al‐Rizzo,et al.  Analysis of a choke ring groundplane for multipath control in Global Positioning System (GPS) applications , 1994 .

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

[30]  Hyuk Park,et al.  Using GNSS-R Imaging of the Ocean Surface for Oil Slick Detection , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[31]  E. Cardellach,et al.  Characterization of dry-snow sub-structure using GNSS reflected signals , 2012 .

[32]  E. Njoku,et al.  Vegetation and surface roughness effects on AMSR-E land observations , 2006 .

[33]  F. Nievinski,et al.  Can we measure snow depth with GPS receivers? , 2009 .

[34]  T. Jackson,et al.  Vegetation water content estimation for corn and soybeans using spectral indices derived from MODIS near- and short-wave infrared bands , 2005 .

[35]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[36]  Valery U. Zavorotny,et al.  A Physical Model for GPS Multipath Caused by Land Reflections: Toward Bare Soil Moisture Retrievals , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  Claudia M. Castaneda,et al.  Estimating Canopy Water Content of Chaparral Shrubs Using Optical Methods , 1998 .

[38]  Matthew O. Jones,et al.  Comparing land surface phenology derived from satellite and GPS network microwave remote sensing , 2014, International Journal of Biometeorology.

[39]  Hyuk Park,et al.  GNSS-R Derived Centimetric Sea Topography: An Airborne Experiment Demonstration , 2013, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[40]  D. Agnew,et al.  Finding the repeat times of the GPS constellation , 2006 .

[41]  P. Axelrad,et al.  Sea ice remote sensing using surface reflected GPS signals , 2000, IGARSS 2000. IEEE 2000 International Geoscience and Remote Sensing Symposium. Taking the Pulse of the Planet: The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No.00CH37120).

[42]  P. Y. Georgiadou,et al.  On carrier signal multipath effects in relative GPS positioning , 1988, manuscripta geodaetica.

[43]  Roger D. De Roo,et al.  A semi-empirical backscattering model at L-band and C-band for a soybean canopy with soil moisture inversion , 2001, IEEE Trans. Geosci. Remote. Sens..

[44]  B. Wylie,et al.  Satellite mapping of surface biophysical parameters at the biome scale over the North American grasslands a case study , 2002 .

[45]  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.

[46]  J. Wickert,et al.  Detection of Arctic Ocean tides using interferometric GNSS‐R signals , 2011 .

[47]  P. Axelrad,et al.  Initial results of land-reflected GPS bistatic radar measurements in SMEX02 , 2004 .

[48]  T. Schmugge,et al.  Vegetation effects on the microwave emission of soils , 1991 .

[49]  C. Tucker Red and photographic infrared linear combinations for monitoring vegetation , 1979 .

[50]  M. Martín-Neira A pasive reflectometry and interferometry system (PARIS) application to ocean altimetry , 1993 .

[51]  E. Small,et al.  Sensing vegetation growth with reflected GPS signals , 2010 .

[52]  G. Donald,et al.  Relating Radar Backscatter to Biophysical Properties of Temperate Perennial Grassland , 1999 .

[53]  F. Nievinski,et al.  GPS snow sensing: results from the EarthScope Plate Boundary Observatory , 2012, GPS Solutions.

[54]  Charles M. Meertens,et al.  TEQC: The Multi-Purpose Toolkit for GPS/GLONASS Data , 1999, GPS Solutions.

[55]  Per K. Enge,et al.  Global positioning system: signals, measurements, and performance [Book Review] , 2002, IEEE Aerospace and Electronic Systems Magazine.

[56]  Martha C. Anderson,et al.  Vegetation water content mapping using Landsat data derived normalized difference water index for corn and soybeans , 2004 .