scholarly journals Hydrosoil, Soil Moisture and Vegetation Parameters Retrieval with a C-Band GB-SAR: Campaign Implementation and First Results

2021 ◽  
Author(s):  
Albert Aguasca ◽  
Antoni Broquetas ◽  
Xavier Fabregas ◽  
Jordi J. Mallorqui ◽  
Pol Vilalvilla ◽  
...  
Keyword(s):  
Soil Moisture ◽  
First Results ◽  
Vegetation Parameters

scholarly journals Towards Including Dynamic Vegetation Parameters in the EUMETSAT H SAF ASCAT Soil Moisture Products

2021 ◽  
Vol 13 (8) ◽  
pp. 1463
Author(s):  
Susan C. Steele-Dunne ◽  
Sebastian Hahn ◽  
Wolfgang Wagner ◽  
Mariette Vreugdenhil
Keyword(s):  
Soil Moisture ◽  
Interannual Variability ◽  
Incidence Angle ◽  
Pearson Correlation ◽  
The United States ◽  
Research Network ◽  
Window Width ◽  
High Frequency Variability ◽  
Vegetation Parameters ◽  
The Impact

The TU Wien Soil Moisture Retrieval (TUW SMR) approach is used to produce several operational soil moisture products from the Advanced Scatterometer (ASCAT) on the Metop series of satellites as part of the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management (H SAF). The incidence angle dependence of backscatter is described by a second-order Taylor polynomial, the coefficients of which are used to normalize ASCAT observations to the reference incidence angle of 40∘ and for correcting vegetation effects. Recently, a kernel smoother was developed to estimate the coefficients dynamically, in order to account for interannual variability. In this study, we used the kernel smoother for estimating these coefficients, where we distinguished for the first time between their two uses, meaning that we used a short and fixed window width for the backscatter normalisation while we tested different window widths for optimizing the vegetation correction. In particular, we investigated the impact of using the dynamic vegetation parameters on soil moisture retrieval. We compared soil moisture retrievals based on the dynamic vegetation parameters to those estimated using the current operational approach by examining their agreement, in terms of the Pearson correlation coefficient, unbiased RMSE and bias with respect to in situ soil moisture. Data from the United States Climate Research Network were used to study the influence of climate class and land cover type on performance. The sensitivity to the kernel smoother half-width was also investigated. Results show that estimating the vegetation parameters with the kernel smoother can yield an improvement when there is interannual variability in vegetation due to a trend or a change in the amplitude or timing of the seasonal cycle. However, using the kernel smoother introduces high-frequency variability in the dynamic vegetation parameters, particularly for shorter kernel half-widths.

Soil moisture and vegetation effects on the ASCAT backscatter-incidence angle dependence

2021 ◽  
Author(s):  
Isabella Pfeil ◽  
Wolfgang Wagner ◽  
Sebastian Hahn ◽  
Raphael Quast ◽  
Susan Steele-Dunne ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Radiative Transfer ◽  
Incidence Angle ◽  
Transfer Model ◽  
Study Region ◽  
Vegetation Effects ◽  
First Order ◽  
Vegetation Parameters ◽  
Low Incidence

<div> <p>Soil moisture (SM) datasets retrieved from the advanced scatterometer (ASCAT) sensor are well established and widely used for various hydro-meteorological, agricultural, and climate monitoring applications. Besides SM, ASCAT is sensitive to vegetation structure and vegetation water content, enabling the retrieval of vegetation optical depth (VOD; 1). The challenge in the retrieval of SM and vegetation products from ASCAT observations is to separate the two effects. As described by Wagner et al. (2), SM and vegetation affect the relation between backscatter and incidence angle differently.  At high incidence angles, the response from bare soil and thus the sensitivity to SM conditions is significantly weaker than at low incidence angles, leading to decreasing backscatter with increasing incidence angle. The presence of vegetation on the other hand decreases the backscatter dependence on the incidence angle. The dependence of backscatter on the incidence angle can be described by a second-order Taylor polynomial based on a slope and a curvature coefficient. It was found empirically that SM conditions have no significant effect on the steepness of the slope, and that therefore, SM and vegetation effects can be separated using the slope (2).  This is a major assumption in the TU Wien soil moisture retrieval algorithm used in several operational soil moisture products. However, recent findings by Quast et al. (3) using a first-order radiative transfer model for the inversion of soil and vegetation parameters from scatterometer observations indicate that SM may influence the slope, as the SM-induced backscatter increase is more pronounced at low incidence angles. </p> </div><div> <div> <p>The aim of this analysis is to revisit the assumption that SM does not affect the slope of the backscatter incidence angle relations by investigating if short-term variability, observed in ASCAT slope timeseries on top of the seasonal vegetation cycle, is caused by SM. We therefore compare timeseries and anomalies of the ASCAT slope to air temperature, rainfall and SM from the ERA5-Land dataset. We carry out the analysis in a humid continental climate (Austria) and a Mediterranean climate study region (Portugal). First results show significant negative correlations between slope and SM anomalies. However, correlations between temperature and slope anomalies are of a similar magnitude, albeit positive, which may reflect temperature-induced vegetation dynamics. The fact that temperature and SM are strongly correlated with each other complicates the interpretation of the results. Thus, our second approach is to investigate daily slope values and their change between dry and wet days. The results of this study shall help to quantify the uncertainties in ASCAT SM products caused by the potentially inadequate assumption of a SM-independent slope. </p> </div> <div> <p> </p> </div> <div> <p>(1) Vreugdenhil, Mariette, et al. "Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval." IEEE Transactions on Geoscience and Remote Sensing 54.6 (2016): 3513-3531.</p> <p><span>(2) Wagner, Wolfgang, et al. "Monitoring soil moisture over the Canadian Prairies with the ERS scatterometer." IEEE Transactions on Geoscience and Remote Sensing 37.1 (1999): 206-216. </span></p> </div> <div> <p>(3) Quast, Raphael, et al. "A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations." Remote Sensing 11.3 (2019): 285.</p> </div> </div>

The Soil Moisture and Ocean Salinity (SMOS) Mission: first results and achievements

2014 ◽  
pp. 12-19
Author(s):  
Yann Kerr ◽  
Philippe Waldteufel ◽  
Jean-Pierre Wigneron ◽  
Jacqueline Boutin ◽  
Nicolas Reul ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Grand Nombre ◽  
Ocean Salinity ◽  
First Results ◽  
Les Six

La mission SMOS (Soil moisture and Ocean Salinity) a été lancée avec succès le 2 novembre 2009. Cette mission menée par l'ESA (Agence Spatiale Europénne) est dédiée à la mesure de l'humidité superficielle des sols sur les continents (avec une précision recherchée de 0,04m3/m3) et la salinité des océans (objectif 0.1psu). Ces deux quantités géophysiques sont très importantes car elle contrôle le budget énergétique à l'interface sol-atmosphère. Leur connaissance à l'échelle globale est utile pour les recherches sur le climat et la météorologie, en particulier pour les modèles de prévision numérique. Elles ont aussi un très grand potentiel un très grand nombre d'application, comme par exemple pour le suivi des ouragans ou la gestion des ressources en eau. Les six premiers mois ont été dédiés à la recette en vol qui a permis de vérifier le satellite le segment sol et l'étalonnage. Cette phase s'est achevée avec succès en mai 2010 et SMOS fonctionne de façon opérationnelle depuis, fournissant de données à la communauté internationale. Les performances de l'instrument sont globalement conformes aux spécifications. Cependant, les interférences radio sont présentes au-dessus de l'Europe, du Moyen-Orient et de l'Asie. Cesémissions parasites dans la bande protégée perturbent la mesure de façon significative. La génération des produits de niveau 2 et 3 est une activité en cours avec des améliorations régulières de sorties.

scholarly journals The Effect of Soil and Vegetation Parameters in the ECMWF Land Surface Scheme

2004 ◽  
Vol 5 (6) ◽  
pp. 1131-1146 ◽  
Author(s):  
H. Richter ◽  
A. W. Western ◽  
F. H. S. Chiew
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Weather Prediction ◽  
Lower Boundary ◽  
Soil Parameters ◽  
Model Parameters ◽  
Lower Boundary Condition ◽  
Land Surface Scheme ◽  
Vegetation Parameters ◽  
Surface Scheme

Abstract Numerical Weather Prediction (NWP) and climate models are sensitive to evapotranspiration at the land surface. This sensitivity requires the prediction of realistic surface moisture and heat fluxes by land surface models that provide the lower boundary condition for the atmospheric models. This paper compares simulations of a stand-alone version of the European Centre for Medium-Range Weather Forecasts (ECMWF) land surface scheme, or the Viterbo and Beljaars scheme (VB95), with various soil and vegetation parameter sets against soil moisture observations across the Murrumbidgee River catchment in southeast Australia. The study is, in part, motivated by the adoption of VB95 as the operational land surface scheme by the Australian Bureau of Meteorology in 1999. VB95 can model the temporal fluctuations in soil moisture, and therefore the moisture fluxes, fairly realistically. The monthly model latent heat flux is also fairly insensitive to soil or vegetation parameters. The VB95 soil moisture is sensitive to the soil and, to a lesser degree, the vegetation parameters. The model exhibits a significant (generally wet) bias in the absolute soil moisture that varies spatially. The use of the best Australia-wide available soils and vegetation information did not improve VB95 simulations consistently, compared with the original model parameters. Comparisons of model and observed soil moistures revealed that more realistic soil parameters are needed to reduce the model soil moisture bias. Given currently available continent-wide soils parameters, any initialization of soil moisture with observed values would likely result in significant flux errors. The soil moisture bias could be largely eliminated by using soil parameters that were derived directly from the actual soil moisture observations. Such parameters, however, are only available at very few point locations.

Online ground temperature and soil moisture monitoring of a shallow geothermal system with non-conventional components

2020 ◽  
Author(s):  
Ludwin Duran ◽  
Darius Mottaghy ◽  
Ulf Herrmann ◽  
Rolf Groß
Keyword(s):  
Soil Moisture ◽  
Web Application ◽  
Sampling Rate ◽  
Transport Processes ◽  
Operating Conditions ◽  
High Tech ◽  
Geothermal System ◽  
Test Installation ◽  
Modern Development ◽  
First Results

<p>We present first results from a newly developed monitoring station for a closed loop geothermal heat pump test installation at our campus, consisting of helix coils and plate heat exchangers, as well as an ice-store system. There are more than 40 temperature sensors and several soil moisture content sensors distributed around the system, allowing a detailed monitoring under different operating conditions.</p><p>In the view of the modern development of renewable energies along with the newly concepts known as <em>Internet of Things and Industry 4.0</em> (high-tech strategy from the German government), we created a user-friendly web application, which will connect the things (sensors) with the open network (www). Besides other advantages, this allows a continuous remote monitoring of the data from the numerous sensors at an arbitrary sampling rate.</p><p>Based on the recorded data, we will also present first results from numerical simulations, taking into account all relevant heat transport processes.</p><p>The aim is to improve the understanding of these processes and their influence on the thermal behavior of shallow geothermal systems in the unsaturated zone. This will in turn facilitate the prediction of the performance of these systems and therefore yield an improvement in their dimensioning when designing a specific shallow geothermal installation.</p>

scholarly journals A Forward GPS Multipath Simulator Based on the Vegetation Radiative Transfer Equation Model

2017 ◽  
Author(s):  
Xuerui Wu ◽  
Shuanggen Jin
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Transfer Equation ◽  
Radiative Transfer Equation ◽  
Signal To Noise Ratio ◽  
Equation Model ◽  
Forest Monitoring ◽  
Specular Scattering ◽  
Vegetation Parameters ◽  
Code Pseudorange

GNSS have been widely used in navigation, positioning and timing. Nowadays, the multipath errors previously considered detrimental may be re-utilized for the remote sensing of geophysical parameters (soil moisture, vegetation and snow depth), e.g. GPS- Multipath Reflectometry (GPS-MR). In this paper, a new element describing bistatic scattering properties of vegetation is incorporated into the traditional GPS-MR model. This new element is the first-order radiative transfer equation model. The new forward GPS multipath simulator is able to explicitly link the vegetation parameters with GPS multipath observables (signal-to-noise-ratio (SNR), code pseudorange and carrier phase observables). The trunk layer and its corresponding scattering mechanisms are ignored since GPS-MR is not suitable for high forest monitoring due to the coherence of direct and reflected signals. Based on this new model linking the GPS observables (SNR, phase and pseudorange) with detailed vegetation parameters, the developed simulator can present how the GPS signals (L1 and L2 carrier frequencies, C/A, P(Y) and L2C modulations) are transmitted (scattered and absorbed) through vegetation medium and received by GPS receivers. Simulation results show that wheat will decrease the amplitudes of GPS multipath observables, if we increase the vegetation moisture contents or the scatters sizes (stem or leaf), the amplitudes of GPS multipath observables (SNR, phase and code) decrease. Although the Specular-Ground component dominates the total specular scattering, vegetation covered ground soil moisture has almost no effects on the final multipath signatures. Our simulated results are consistent with published results for environmental parameter detections with GPS-MR.

scholarly journals Long-term Soil Moisture Time Series Analyses based on Active Microwave Backscatter Measurements

2015 ◽  
Vol XL-7/W3 ◽  
pp. 545-550
Author(s):  
W. Wagner ◽  
C. Reimer ◽  
B. Bauer-Marschallinger ◽  
M. Enenkel ◽  
S. Hahn ◽  
...  
Keyword(s):  
Time Series ◽  
Soil Moisture ◽  
Microwave Frequencies ◽  
Vegetation Modelling ◽  
Time Series Analyses ◽  
Vegetation Parameters ◽  
Microwave Backscatter ◽  
Microwave Sensors ◽  
Modelling Approach

Active microwave sensors operating at lower microwave frequencies in the range from 1 to 10 GHz provide backscatter measurements that are sensitive to the moisture content of the soil. Thanks to a series of European C-band (5.3 GHz) scatterometers, which were first flown on board of the European Remote Sensing satellites ERS-1 and ERS-2, and later on board of MetOp-A and MetOp -B, we are now in the possession of a long-term soil moisture time series starting in 1991. The creation of globally consistent long-term soil moisture time series is a challenging task. The TU-Wien soil moisture algorithm is adopted to tackle these challenges. In this paper we present two methodologies that were developed to ensure radiometric stability of the European C-band scatterometers. The objective of sensor intra-calibration is to monitor and correct for radiometric instabilities within one scatterometer mission, while sensor inter-calibration aims to remove radiometric differences across several missions. In addition, a novel vegetation modelling approach is presented that enables the estimation of vegetation parameters for each day across several years to account for yearly to longer-term changes in vegetation phenology and land cover.

scholarly journals Retrieval of Soil Moisture by Integrating Sentinel-1A and MODIS Data over Agricultural Fields

Water ◽  
2020 ◽  
Vol 12 (6) ◽  
pp. 1726 ◽  
Author(s):  
Yizhi Han ◽  
Xiaojing Bai ◽  
Wei Shao ◽  
Jie Wang
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Vegetation Index ◽  
Coupled Model ◽  
Agricultural Drought ◽  
Agricultural Fields ◽  
Backscattering Coefficient ◽  
Modis Data ◽  
Vegetation Parameters ◽  
Scattering Contribution

Soil moisture is an essential variable in the land surface ecosystem, which plays an important role in agricultural drought monitoring, crop status monitoring, and crop yield prediction. High-resolution radar data can be combined with optical remote-sensing data to provide a new approach to estimate high-resolution soil moisture over vegetated areas. In this paper, the Sentinel-1A data and the Moderate Resolution Imaging Spectroradiometer (MODIS) data are combined to retrieve soil moisture over agricultural fields. The advanced integral equation model (AIEM) is utilized to calculate the scattering contribution of the bare soil surface. The water cloud model (WCM) is applied to model the backscattering coefficient of vegetated areas, which use two vegetation parameters to parameterize the scattering and attenuation properties of vegetation. Four different vegetation parameters extracted from MODIS products are combined to predict the scattering contribution of vegetation, including the leaf area index (LAI), the fraction of photosynthetically active radiation (FPAR), normalized difference vegetation index (NDVI), and the enhanced vegetation index (EVI). The effective roughness parameters are chosen to parameterize the AIEM. The Sentinel-1A and MODIS data in 2017 are used to calibrate the coupled model, and the datasets in 2018 are used for soil moisture estimation. The calibration results indicate that the Sentinel-1A backscattering coefficient can be accurately predicted by the coupled model with the Pearson correlation coefficient (R) ranging from 0.58 to 0.81 and a root mean square error (RMSE) ranging from 0.996 to 1.401 dB. The modeled results show that the retrieved soil moisture can capture the seasonal dynamics of soil moisture with R ranging from 0.74 to 0.81. With the different vegetation parameter combinations used for parameterizing the scattering contribution of the canopy, the importance of suitable vegetation parameters for describing the scattering and attenuation properties of vegetation is confirmed. The LAI is recommended to characterize the scattering properties. There is no obvious clue for selecting vegetation descriptors to characterize the attenuation properties of vegetation. These promising results confirm the feasibility and validity of the coupled model for soil moisture retrieval from the Sentinel-1A and MODIS data.

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