Vegetation Sensing Using GPS Interferometric Reflectometry: Experimental Results With a Horizontally Polarized Antenna

Chen, Qiang; Won, Daehee; Akos, Dennis; Small, E. E.

doi:10.1109/jstars.2016.2565687

Cited by 18 publications

(11 citation statements)

References 31 publications

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“…The results show that when VWC does not exceed 1.5 kg/m 2 , the vegetation water content and SNR amplitude have a linear relationship (Chew et al 2014). Chen and others proposed a method based on the amplitude and frequency analysis of the interference pattern of the SNR to eliminate the influence of VWC in soil moisture retrieval and achieved excellent results (Chen et al 2016). In terms of VWC, Larson and others defined a daily VWC metric based on the amplitude of the reflected signal based on the relationship between the SNR amplitude and VWC, which is the normalized microwave reflection index (NMRI) (Larson and Small 2014).…”

Section: Introductionmentioning

confidence: 99%

Inversion of surface vegetation water content based on GNSS-IR and MODIS data fusion

et al. 2020

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Obtaining high-precision, long-term sequences of vegetation water content (VWC) is of great significance for assessing surface vegetation growth, soil moisture, and fire risk. In recent years, the global navigation satellite system-interferometric reflection (GNSS-IR) has become a new type of remote sensing technology with low cost, all-weather capability, and a high temporal resolution. It has been widely used in the fields of snow depth, sea level, soil moisture content, and vegetation water content. The normalized microwave reflectance index (NMRI) based on GNSS-IR technology has been proven to be effective in monitoring changes in VWC. This paper considers the advantages and disadvantages of remote sensing technology and GNSS-IR technology in estimating VWC. A pointsurface fusion method of GNSS-IR and MODIS data based on the GA-BP neural network is proposed to improve the accuracy of VWC estimation. The vegetation index products (NDVI, GPP, LAI) and the NMRI that unified the temporal and spatial resolution were used as the input and output data of the training model, and the GA-BP neural network was used for training and modeling. Finally, a spatially continuous NMRI product was generated. Taking a particular area of the United States as a research object, experiments show that (1) a neural network can realize the effective fusion of GNSS-IR and MODIS products. By comparing the GA-BP neural network, BP neural network, and multiple linear regression (MLR), the three models fusion effect. The results show that the GA-BP neural network has the best modeling effect, and the r and RMSE between the model estimation result and the reference value are 0.778 and 0.0332, respectively; this network is followed by the BP neural network, in which the r and RMSE are 0.746 and 0.0465, respectively. MLR has the poorest effect, with r and RMSE values of 0.500 and 0.0516, respectively. (2) The spatiotemporal variation in the 16 days/500 m resolution NMRI product obtained by GA-BP neural network fusion is consistent with that in the experimental area. Through the testing of GNSS stations that did not participate in the modeling, the r between the estimated value of the NMRI and the reference value is greater than 0.87, and the RMSE is less than 0.049. Therefore, the method proposed in this paper is optional and effective. The spatially continuous NMRI products obtained by fusion can reflect the changes in VWC in the experimental area more intuitively.

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Section: Introductionmentioning

confidence: 99%

Inversion of surface vegetation water content based on GNSS-IR and MODIS data fusion

et al. 2020

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“…Commercially available geodetic-quality GNSS receivers and antennas can be used for GNSS-IR. The method has been tested with the Global Positioning System (GPS) satellite constellation, and it has been shown to provide consistent measurements of the upper surface soil moisture content (Larson et al, 2008a(Larson et al, , b, 2010Larson and Nievinski, 2013;Chew et al, 2014Chew et al, , 2015Chew et al, , 2016Small et al, 2016;Vey et al, 2016;Wan et al, 2015;Chen et al, 2016;Zhang et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring

Martín

Ibáñez

Baixauli³

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Per capita arable land is decreasing due to the rapidly increasing population, and fresh water is becoming scarce and more expensive. Therefore, farmers should continue to use technology and innovative solutions to improve efficiency, save input costs, and optimise environmental resources (such as water). In the case study presented in this paper, the Global Navigation Satellite System interferometric reflectometry (GNSS-IR) technique was used to monitor soil moisture during 66 d, from 3 December 2018 to 6 February 2019, in the installations of the Cajamar Centre of Experiences, Paiporta, Valencia, Spain. Two main objectives were pursued. The first was the extension of the technique to a multi-constellation solution using GPS, GLONASS, and GALILEO satellites, and the second was to test whether mass-market sensors could be used for this technique. Both objectives were achieved. At the same time that the GNSS observations were made, soil samples taken at 5 cm depth were used for soil moisture determination to establish a reference data set. Based on a comparison with that reference data set, all GNSS solutions, including the three constellations and the two sensors (geodetic and mass market), were highly correlated, with a correlation coefficient between 0.7 and 0.85.

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“…The GNSS-IR technique, first developed by Larson et al (2008a, b), and based on the procedure detailed in Larson et al (2010), Larson and Nievinski (2013), Chew et al (2014Chew et al ( , 2015Chew et al ( , 2016, Vey et al (2016), , Wan et al (2015), Chen et al (2016), Roussel et al (2016), and Zhang et al (2017), is summarized as follows:…”

Section: Introductionmentioning

confidence: 99%

Python software tools for GNSS interferometric reflectometry (GNSS-IR)

2020

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Global Navigation Satellite System (GNSS) interferometric reflectometry, also known as the GNSS-IR, uses data from geodetic-quality GNSS antennas to extract information about the environment surrounding the antenna. Soil moisture monitoring is one of the most important applications of the GNSS-IR technique. This manuscript presents the main ideas and implementation decisions needed to write the Python code for software tools that transform RINEX format observation and navigation files into an appropriate format for GNSS-IR (which includes the SNR observations and the azimuth and elevation of the satellites) and to determine the reflection height and the adjusted phase and amplitude values of the interferometric wave for each individual satellite track. The main goal of the manuscript is to share the software with the scientific community to introduce new users to the GNSS-IR technique.

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Vegetation Sensing Using GPS Interferometric Reflectometry: Experimental Results With a Horizontally Polarized Antenna

Cited by 18 publications

References 31 publications

Inversion of surface vegetation water content based on GNSS-IR and MODIS data fusion

Inversion of surface vegetation water content based on GNSS-IR and MODIS data fusion

Multi-constellation GNSS interferometric reflectometry with mass-market sensors as a solution for soil moisture monitoring

Python software tools for GNSS interferometric reflectometry (GNSS-IR)

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