Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (3)

Search Parameters:
Keywords = Valencia anchor station

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
20 pages, 10895 KiB  
Article
In-Situ GNSS-R and Radiometer Fusion Soil Moisture Retrieval Model Based on LSTM
by Tianlong Zhang, Lei Yang, Hongtao Nan, Cong Yin, Bo Sun, Dongkai Yang, Xuebao Hong and Ernesto Lopez-Baeza
Remote Sens. 2023, 15(10), 2693; https://doi.org/10.3390/rs15102693 - 22 May 2023
Cited by 1 | Viewed by 2534
Abstract
Global navigation satellite system reflectometry (GNSS-R) is a remote sensing technology of soil moisture measurement using signals of opportunity from GNSS, which has the advantages of low cost, all-weather detection, and multi-platform application. An in situ GNSS-R and radiometer fusion soil moisture retrieval [...] Read more.
Global navigation satellite system reflectometry (GNSS-R) is a remote sensing technology of soil moisture measurement using signals of opportunity from GNSS, which has the advantages of low cost, all-weather detection, and multi-platform application. An in situ GNSS-R and radiometer fusion soil moisture retrieval model based on LSTM (long–short term memory) is proposed to improve accuracy and robustness as to the impacts of vegetation cover and soil surface roughness. The Oceanpal GNSS-R data obtained from the experimental campaign at the Valencia Anchor Station are used as the main input data, and the TB (brightness temperature) and TR (soil roughness and vegetation integrated attenuation coefficient) outputs of the ELBARA-II radiometer are used as auxiliary input data, while field measurements with a Delta-T ML2x ThetaProbe soil moisture sensor were used for reference and validation. The results show that the LSTM model can be used to retrieve soil moisture, and that it performs better in the data fusion scenario with GNSS-R and radiometer. The STD of the multi-satellite fusion model is 0.013. Among the single-satellite models, PRN13, 20, and 32 gave the best retrieval results with STD = 0.011, 0.012, and 0.007, respectively. Full article
Show Figures

Graphical abstract

18 pages, 4492 KiB  
Article
Modeling Influence of Soil Properties in Different Gradients of Soil Moisture: The Case of the Valencia Anchor Station Validation Site, Spain
by Ester Carbó, Pablo Juan, Carlos Añó, Somnath Chaudhuri, Carlos Diaz-Avalos and Ernesto López-Baeza
Remote Sens. 2021, 13(24), 5155; https://doi.org/10.3390/rs13245155 - 19 Dec 2021
Cited by 5 | Viewed by 3664
Abstract
The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic [...] Read more.
The prediction of spatial and temporal variation of soil water content brings numerous benefits in the studies of soil. However, it requires a considerable number of covariates to be included in the study, complicating the analysis. Integrated nested Laplace approximations (INLA) with stochastic partial differential equation (SPDE) methodology is a possible approach that allows the inclusion of covariates in an easy way. The current study has been conducted using INLA-SPDE to study soil moisture in the area of the Valencia Anchor Station (VAS), soil moisture validation site for the European Space Agency SMOS (Soil Moisture and Ocean Salinity). The data used were collected in a typical ecosystem of the semiarid Mediterranean conditions, subdivided into physio-hydrological units (SMOS units) which presents a certain degree of internal uniformity with respect to hydrological parameters and capture the spatial and temporal variation of soil moisture at the local fine scale. The paper advances the knowledge of the influence of hydrodynamic properties on VAS soil moisture (texture, porosity/bulk density and soil organic matter and land use). With the goal of understanding the factors that affect the variability of soil moisture in the SMOS pixel (50 km × 50 km), five states of soil moisture are proposed. We observed that the model with all covariates and spatial effect has the lowest DIC value. In addition, the correlation coefficient was close to 1 for the relationship between observed and predicted values. The methodology applied presents the possibility to analyze the significance of different covariates having spatial and temporal effects. This process is substantially faster and more effective than traditional kriging. The findings of this study demonstrate an advancement in that framework, demonstrating that it is faster than previous methodologies, provides significance of individual covariates, is reproducible, and is easy to compare with models. Full article
(This article belongs to the Special Issue Earth Observation in Support of Sustainable Soils Development)
Show Figures

Graphical abstract

16 pages, 6655 KiB  
Article
Intercomparison of Soil Moisture Retrieved from GNSS-R and from Passive L-Band Radiometry at the Valencia Anchor Station
by Cong Yin, Ernesto Lopez-Baeza, Manuel Martin-Neira, Roberto Fernandez-Moran, Lei Yang, Enrique A. Navarro-Camba, Alejandro Egido, Antonio Mollfulleda, Weiqiang Li, Yunchang Cao, Bin Zhu and Dongkai Yang
Sensors 2019, 19(8), 1900; https://doi.org/10.3390/s19081900 - 22 Apr 2019
Cited by 13 | Viewed by 4787
Abstract
In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an [...] Read more.
In this paper, the SOMOSTA (Soil Moisture Monitoring Station) experiment on the intercomparison of soil moisture monitoring from Global Navigation Satellite System Reflectometry (GNSS-R) signals and passive L-band microwave radiometer observations at the Valencia Anchor Station is introduced. The GNSS-R instrument has an up-looking antenna for receiving direct signals from satellites, and a dual-pol down-looking antenna for receiving LHCP (left-hand circular polarization) and RHCP (right-hand circular polarization) reflected signals from the soil surface. Data were collected from the three different antennas through the two channels of Oceanpal GNSS-R receiver and, in addition, calibration was performed to reduce the impact from the differing channels. Reflectivity was thus measured, and soil moisture could be retrieved. The ESA (European Space Agency)-funded ELBARA-II (ESA L Band Radiometer II) is an L-band radiometer with two channels with 11 MHz bandwidth and respective center frequencies of 1407.5 MHz and 1419.5 MHz. The ELBARAII antenna is a large dual-mode Picket horn that is 1.4 m wide, with a length of 2.7 m with −3 dB full beam width of 12° (±6° around the antenna main direction) and a gain of 23.5 dB. By comparing GNSS-R and ELBARA-II radiometer data, a high correlation was found between the LHCP reflectivity measured by GNSS-R and the horizontal/vertical reflectivity from the radiometer (with correlation coefficients ranging from 0.83 to 0.91). Neural net fitting was used for GNSS-R soil moisture inversion, and the RMSE (Root Mean Square Error) was 0.014 m3/m3. The determination coefficient between the retrieved soil moisture and in situ measurements was R2 = 0.90 for Oceanpal and R2 = 0.65 for Elbara II, and the ubRMSE (Unbiased RMSE) were 0.0128 and 0.0734 respectively. The soil moisture retrievals by both L-band remote sensing methods show good agreement with each other, and their mutual correspondence with in-situ measurements and with rainfall was also good. Full article
(This article belongs to the Special Issue Satellite Remotely Sensed Soil Moisture)
Show Figures

Figure 1

Back to TopTop