scholarly journals MULTISENSOR EXPERIMENTS OVER VINEYARD: NEW CHALLENGES FOR THE GNSS-R TECHNIQUE

2015 ◽  
Vol XL-7/W3 ◽  
pp. 1299-1303 ◽  
Author(s):  
N. Sánchez ◽  
A. Alonso-Arroyo ◽  
J. Martínez-Fernández ◽  
A. Camps ◽  
A. González-Zamora ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Field Measurements ◽  
Climatic Conditions ◽  
Global Navigation Satellite Systems ◽  
Landsat 8 ◽  
Spectral Measurements ◽  
Satellite Systems ◽  
Vegetation Parameters ◽  
Global Navigation Satellite ◽  
Different Sources

An airborne campaign was performed during August, 2014 in an agricultural area in the Duero basin (Spain) in order to appraise the synergy between very different sources of Earth Observation imagery, and very different instruments for soil moisture retrieval. During the flight, an intensive field campaign comprising soil, plant and spectral measurements was carried out. An innovative sensor based on the Global Navigation Satellite Systems Reflectometry (GNSS-R) was on board the manned vehicle, the Light Airborne Reflectometer for GNSS-R Observations (LARGO) engineered by the Universitat Politècnica de Catalunya. While the synergy between thermal, optical and passive microwave spectra observations is well known for vegetation parameters and soil moisture retrievals, the experiment aimed to evaluate the synergy of GNSS-R reflectivity with a time-collocated Landsat 8 imagery for soil moisture retrieval under semiarid climatic conditions. LARGO estimates, field measurements, and optical, NIR, SWIR and thermal bands from Landsat 8 were compared. Results showed that the joint use of GNSS-R reflectivity with vegetation and water indices together with thermal maps from Landsat 8 thoroughly improved the soil moisture estimation.

scholarly journals NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 101-107
Author(s):  
N. Sánchez ◽  
J. M. Lopez-Sanchez ◽  
B. Arias-Pérez ◽  
R. Valcarce-Diñeiro ◽  
J. Martínez-Fernández ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Global Navigation Satellite Systems ◽  
Radar Signal ◽  
Landsat 8 ◽  
Area Index ◽  
Satellite Systems ◽  
Crop Parameters ◽  
Global Navigation Satellite

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.

scholarly journals NEW MICROWAVE-BASED MISSIONS APPLICATIONS FOR RAINFED CROPS CHARACTERIZATION

2016 ◽  
Vol XLI-B1 ◽  
pp. 101-107
Author(s):  
N. Sánchez ◽  
J. M. Lopez-Sanchez ◽  
B. Arias-Pérez ◽  
R. Valcarce-Diñeiro ◽  
J. Martínez-Fernández ◽  
...  
Keyword(s):  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Field Measurements ◽  
Global Navigation Satellite Systems ◽  
Radar Signal ◽  
Landsat 8 ◽  
Area Index ◽  
Satellite Systems ◽  
Crop Parameters ◽  
Global Navigation Satellite

A multi-temporal/multi-sensor field experiment was conducted within the Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS) in Spain, in order to retrieve useful information from satellite Synthetic Aperture Radar (SAR) and upcoming Global Navigation Satellite Systems Reflectometry (GNSS-R) missions. The objective of the experiment was first to identify which radar observables are most sensitive to the development of crops, and then to define which crop parameters the most affect the radar signal. A wide set of radar variables (backscattering coefficients and polarimetric indicators) acquired by Radarsat-2 were analyzed and then exploited to determine variables characterizing the crops. Field measurements were fortnightly taken at seven cereals plots between February and July, 2015. This work also tried to optimize the crop characterization through Landsat-8 estimations, testing and validating parameters such as the leaf area index, the fraction of vegetation cover and the vegetation water content, among others. Some of these parameters showed significant and relevant correlation with the Landsat-derived Normalized Difference Vegetation Index (R>0.60). Regarding the radar observables, the parameters the best characterized were biomass and height, which may be explored for inversion using SAR data as an input. Moreover, the differences in the correlations found for the different crops under study types suggested a way to a feasible classification of crops.

scholarly journals Spatial uncertainty of a geoid undulation model in Guayaquil, Ecuador

2017 ◽  
Vol 9 (1) ◽  
Author(s):  
E.G. Chicaiza ◽  
C.A. Leiva ◽  
J.J. Arranz ◽  
X.E. Buenańo
Keyword(s):  
Estimation Error ◽  
Model Fitting ◽  
Field Measurements ◽  
Geoid Undulation ◽  
Spatial Uncertainty ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Estimation Uncertainty ◽  
Geostatistical Approach ◽  
Global Navigation Satellite

AbstractGeostatistics is a discipline that deals with the statistical analysis of regionalized variables. In this case study, geostatistics is used to estimate geoid undulation in the rural area of Guayaquil town in Ecuador. The geostatistical approach was chosen because the estimation error of prediction map is getting. Open source statistical software R and mainly geoR, gstat and RGeostats libraries were used. Exploratory data analysis (EDA), trend and structural analysis were carried out. An automatic model fitting by Iterative Least Squares and other fitting procedures were employed to fit the variogram. Finally, Kriging using gravity anomaly of Bouguer as external drift and Universal Kriging were used to get a detailed map of geoid undulation. The estimation uncertainty was reached in the interval [-0.5; +0.5] m for errors and a maximum estimation standard deviation of 2 mm in relation with the method of interpolation applied. The error distribution of the geoid undulation map obtained in this study provides a better result than Earth gravitational models publicly available for the study area according the comparison with independent validation points. The main goal of this paper is to confirm the feasibility to use geoid undulations from Global Navigation Satellite Systems and leveling field measurements and geostatistical techniques methods in order to use them in high-accuracy engineering projects.

scholarly journals GNSS-Reflectometry and Remote Sensing of Soil Moisture: A Review of Measurement Techniques, Methods, and Applications

2020 ◽  
Vol 12 (4) ◽  
pp. 614 ◽  
Author(s):  
Komi Edokossi ◽  
Andres Calabia ◽  
Shuanggen Jin ◽  
Iñigo Molina
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Land Surface ◽  
Low Cost ◽  
Measurement Techniques ◽  
Passive Microwave ◽  
Global Navigation Satellite Systems ◽  
Ecological Processes ◽  
Satellite Systems ◽  
Global Navigation Satellite

The understanding of land surface-atmosphere energy exchange is extremely important for predicting climate change and weather impacts, particularly the influence of soil moisture content (SMC) on hydrometeorological and ecological processes, which are also linked to human activities. Unfortunately, traditional measurement methods are expensive and cumbersome over large areas, whereas measurements from satellite active and passive microwave sensors have shown advantages for SMC monitoring. Since the launch of the first passive microwave satellite in 1978, more and more progresses have been made in monitoring SMC from satellites, e.g., the Soil Moisture Active and Passive (SMAP) and Soil Moisture and Ocean Salinity (SMOS) missions in the last decade. Recently, new methods using signals of opportunity have been emerging, highlighting the Global Navigation Satellite Systems-Reflectometry (GNSS-R), which has wide applications in Earth’s surface remote sensing due to its numerous advantages (e.g., revisiting time, global coverage, low cost, all-weather measurements, and near real-time) when compared to the conventional observations. In this paper, a detailed review on the current SMC measurement techniques, retrieval approaches, products, and applications is presented, particularly the new and promising GNSS-R technique. Recent advances, future prospects and challenges are given and discussed.

scholarly journals Sensitivity of TDS-1 GNSS-R Reflectivity to Soil Moisture: Global and Regional Differences and Impact of Different Spatial Scales

2018 ◽  
Vol 10 (11) ◽  
pp. 1856 ◽  
Author(s):  
Adriano Camps ◽  
Mercedes Vall·llossera ◽  
Hyuk Park ◽  
Gerard Portal ◽  
Luciana Rossato
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Incidence Angle ◽  
Spatial Scales ◽  
Ground Truth ◽  
Global Navigation Satellite Systems ◽  
Satellite Systems ◽  
Left Hand ◽  
The Uk ◽  
Global Navigation Satellite

The potential of Global Navigation Satellite Systems-Reflectometry (GNSS-R) techniques to estimate land surface parameters such as soil moisture (SM) is experimentally studied using 2014–2017 global data from the UK TechDemoSat-1 (TDS-1) mission. The approach is based on the analysis of the sensitivity to SM of different observables extracted from the Delay Doppler Maps (DDM) computed by the Space GNSS Receiver–Remote Sensing Instrument (SGR-ReSI) instrument using the L1 (1575.42 MHz) left-hand circularly-polarized (LHCP) reflected signals emitted by the Global Positioning System (GPS) navigation satellites. The sensitivity of different GNSS-R observables to SM and its dependence on the incidence angle is analyzed. It is found that the sensitivity of the calibrated GNSS-R reflectivity to surface soil moisture is ~0.09 dB/% up to 30° incidence angle, and it decreases with increasing incidence angles, although differences are found depending on the spatial scale used for the ground-truth, and the region. The sensitivity to subsurface soil moisture has been also analyzed using a network of subsurface probes and hydrological models, apparently showing some dependence, but so far results are not conclusive.

scholarly journals Elevation change of Fedchenko Glacier, Pamir Mountains, from GNSS field measurements and TanDEM-X elevation models, with a focus on the upper glacier

2018 ◽  
Vol 64 (246) ◽  
pp. 637-648 ◽  
Author(s):  
A. LAMBRECHT ◽  
C. MAYER ◽  
A. WENDT ◽  
D. FLORICIOIU ◽  
C. VÖLKSEN
Keyword(s):  
Field Measurements ◽  
Global Navigation Satellite Systems ◽  
High Accumulation ◽  
Glacier Surface ◽  
Mean Values ◽  
Satellite Systems ◽  
Pamir Mountains ◽  
Elevation Changes ◽  
Global Navigation Satellite ◽  
Radar Wave

ABSTRACTFedchenko Glacier experienced a large thickness loss since the first scientific investigations in 1928. As the largest glacier in the Pamir Mountains, this glacier plays an important role for the regional glacier mass budget. We use a series of Global Navigation Satellite Systems observations from 2009 to 2016 and TanDEM-X elevation models from 2011 to 2016 to investigate recent elevation changes. Accounting for radar wave penetration minimizes biases in elevation that can otherwise reach up to 6 m in dry snow on Fedchenko Glacier, with mean values of 3–4 m in the high accumulation regions. The seasonal elevation changes reach up to ±5 m. The glacier surface elevation decreased along its entire length over multi-year periods. Thinning rates increased between 2000 and 2016 by a factor of 1.8 compared with 1928–2000, resulting in peak values of 1.5 m a−1. Even the highest accumulation basins above 5000 m elevation have been affected by glacier thinning with change rates between −0.2 and −0.4 m a−1 from 2009 to 2016. The estimated glacier-wide mass-balance rates are −0.27 ± 0.05 m w.e. a−1 for 2000 to 2011 and −0.51 ± 0.04 m w.e. a−1 between 2011 and 2016.

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