Accurate information about soil moisture content (SMC) in mountain catchments is of great importance in hydrological
applications, agriculture and climate change impact analysis. In the last two decades microwave remote sensing sensors
such as Synthetic Aperture Radar (SAR) have been deeply exploited for surface SMC estimation. However, obtaining
reliable predictions of fine-scale spatial and temporal patterns of SMC in mountain areas is still challenging due to the
extreme variability in topography, soil and vegetation properties. In this contribution we analyze the spatial and temporal
dynamic of surface SMC of alpine meadows and pastures with different techniques: (I) a network of fixed stations; (II)
field campaigns with mobile ground sensors; (III) SMC retrieval from RADARSAT2 SAR images; (IV) simulations
using the GEOtop 2.0 hydrological model. The strength and the weaknesses of the different estimation techniques are
evaluated and the physical controls of the observed SMC patterns are analyzed. Results show that SAR SMC estimation
corresponds well to the spatial ground surveys, but shows different patterns with respect to the model, especially for
irrigated meadows. In fact, SAR patterns reflect vegetation, soil type and topography. Model output is in agreement with
fixed stations observations, but it shows less spatial variability compared to SAR. Differences are likely due to the
difficulties to know with sufficient spatial detail model parameters and irrigation amount. Therefore, results suggest that
SAR products have a good ability to reproduce small-scale SMC patterns in mountain regions, thus complementing the
ability of the hydrological model to predict temporal variations of SMC.
Three algorithms for the retrieval of soil moisture content (SMC) from MetOp-ASCAT acquisitions have been
developed and compared. This activity has been carried out in the framework of the ASCAT Round Robin exercise that
was supported by ESA as a part of the Climate Change Initiative Phase 1 Soil Moisture Project.
The algorithms have been developed and tested using the ASCAT Round Robin data package (ASCAT – RRDP) that
was distributed among the partners of the project for developing and validating the algorithms and that was composed by
a selection of 150 test sites derived from the International Soil Moisture Network (ISMN) was used.
The locations of the in situ soil moisture measurements used for the Round Robin represent a wide range of climate
conditions, covering a wide variety of vegetation types and vegetation density classes.
In this work, the characterization and extraction of snowpack parameters from X-band SAR imagery has been addressed. A preliminary sensitivity analysis was carried out by exploiting datasets of snowpack parameters (depth, density, snow grain radius, temperature and wetness) collected from the available meteorological stations on two test sites in the Italian Alps. This is a crucial step, since it provides indications on the sensitivity of the input features (i.e., backscattering coefficients and ancillary data) to variations in the target snow parameters. X-band data has been found to contribute to retrieval of the snow water equivalent under specific conditions, i.e., that the snow cover is characterized by a snow depth of roughly 60-70 cm (snow water equivalent <100-150mm) and with relatively large crystal dimensions. After this phase, the retrieval process is addressed. The method is based on a Neural Network retrieval algorithm trained by using a DRTM electromagnetic model in order to estimate the snow water equivalent. The proposed approach also makes use of the threshold criterion for detecting the wet snow cover extent on which the retrieval cannot be performed. The method has been developed and calibrated on the Cordevole plateau located in the Dolomites, Eastern Italian Alps, where ground data collected by the Avalanche Center in Arabba and meteorological data measured by a network of
automatic stations were available. The method was then validated on a second site located in South Tyrol region (Eastern
Italian Alps), where also manual and automatic ground measurements of snow parameters were available. The activity was carried out in the framework of two projects funded by the Italian Space Agency (HYDROCOSMO and SNOX) for the exploitation of X-band satellite SAR data for the analysis and characterization of snow in mountain areas.
This study presents an analysis on the retrieval of soil moisture content from medium resolution wide swath SAR images for monitoring regional scale spatial and temporal patterns of this variable in the challenging Alpine environment. The possibility to retrieve soil moisture content from satellite high resolution SAR imagery in Alpine areas was successfully investigated in a previous contribution. The rationale behind this work is the fact that multi-scale and multi-sensor products could lead to a more general and comprehensive understanding of the phenomena at the ground, since different perspectives and trade-offs among spatial and temporal resolution can be exploited. In more detail, the analysis proposed here aims at: i) assessing the effectiveness of the proposed retrieval algorithm when applied to medium resolution wide swath SAR imagery; and ii) investigating the feasibility of mapping spatial patterns and temporal dynamics of soil moisture content at a regional scale. ENVISAT ASAR Wide Swath images acquired over the Alto Adige/Süd Tirol Province during the years 2010-2011 are used for the experimental analysis. Achieved results are compared with ground measurements and meteorological data, indicating good agreement in terms of both spatial distribution and temporal dynamics of estimated soil moisture content values.
This work focuses on the retrieval of Titan’s dune field characteristics addressing different radar modes. The main
purpose of the proposed work is to exploit a possible synergy between SAR and altimeter acquisitions modes to provide
information about dune field. Cassini has performed 86 Titan flybys in which several observations of dune fields have
been collected in altimetry mode. There are several cases in which SAR and altimeter have been acquired over same
areas covered by dune fields, such as during T28 (SAR) and T30 (altimeter) flybys. Altimetry together with SAR data
have been used to derive the rms slopes of dunes (large scale) over Fensal area, this information has been employed to
calculate SAR incidence angle with respect to dunes. We extracted backscattering coefficients of bright and dark areas
detected in the analyzed SAR image in order to evaluate the angular response of scattering. Through the Geometric
Optics model we retrieve roughness values (small scale rms slope) for both dune bright and dark areas.
This work is developed in the framework of the SOFIA project (ESA AO-6280) which aims at estimating important
biophysical variables in the Alpine area by using advanced state of the art retrieval methods in combination with new
generation satellite polarimetric SAR data. As a first analysis in this direction, in a previous contribution we investigated
the effectiveness of fully polarimetric RADARSAT2 C-band SAR data and proposed the use of the Support Vector
Regression technique and the integration of additional information on the investigated area obtained from ancillary data.
In this paper we move the attention on the exploitation of L-band SAR data. In more detail, our analysis aims at: 1)
assessing the effectiveness of the proposed retrieval algorithm with different satellite SAR data, namely the L-band data;
2) comparing the estimates obtained with the use of C- and L-band SAR imagery, in order to understand common
patterns and eventually discrepances due to the different penetration capability of the signals; and 3) understanding the
feasibility of a synergic use of L and C band SAR data (when both available) for improving the retrieval of soil moisture
in Alpine areas. The experimental analysis is carried out with the use of polarimetric RADARSAT2 (C-band) and
ALOS PalSAR (L-band) SAR data. The achieved results indicate the potential of the synergic use of C and L band SAR
imagery for the retrieval of soil moisture also in the challenging alpine environment. This feature is properly exploited
by the proposed retrieval algorithm, thus pointing out its effectiveness in handling data with different spatial and
radiometric characteristics.
This paper presents the analysis of C and X band images in the scope of soil moisture detection in agricultural fields.
Archived data have been analyzed in order to understand the SAR signal behavior of vegetated fields in comparison to
bare soils. The results indicate that the sensitivity to bare fields of C and X band signatures is very close, while it
changes in presence of vegetation. In particular the effect is directly proportional to amount of vegetation that in this
preliminary analysis has been evaluated through the NDVI variable.
After this analysis, a statistical approach has been applied to SAR images to infer the information on the soil moisture
values. Several experiments have been carried out by considering only C band data, only X band data and a combination
of C and X band data. For bare soils, C and X band data determine very similar results and in good agreement to ground
measurements. For vegetated fields, C band data tend to underestimate soil moisture due to the vegetation attenuation,
while X band data, mainly influenced by vegetation, determine very poor results. Encouraging results are obtained by the
combination of C and X band data, thus indicating that X band data can be used in combination to C band data in order
to compensate the effect of vegetation.
Soil moisture estimation is one of the most challenging problems in the context of biophysical parameter estimation from
remotely sensed data. Typically, microwave signals are used thanks to their well known sensitivity to variations in the
water content of soil. However, other target properties such as soil roughness and the presence of vegetation affect the
microwave signals, thus increasing the complexity of the estimation problem. The latter problem becomes even more
complex when we move on mountain areas, such as the Alps, where the high heterogeneity of the topographic condition
further affect the signals acquired by remote sensors. In this paper, we explore the use of polarimetric RADARSAT2
SAR images for the estimation of soil moisture content in an alpine catchment. In greater detail, we first exploit field
measurements and ancillary data to carry out an analysis on the sensitivity of the SAR signal to the moisture content of
soil and other target properties, such as topography and vegetation/land-cover heterogeneity, that characterize the
mountain environment. On the basis of the findings emerged from this analysis, we propose a technique for estimating
moisture content of soils in these challenging operative conditions. This technique is based on the Support Vector
Regression algorithm and the integration of ancillary data. Preliminary results are discussed both in terms of accuracy
over point measurements and effectiveness in handling spatially distributed data.
Soil moisture estimation is one of the most challenging problems in the context of biophysical parameter estimation from
remotely sensed data. Different approaches have been proposed in the literature, but in the last years there is a growing
interest in the use of non-linear machine learning estimation techniques. This paper presents an experimental analysis in
which two non-linear machine learning techniques, the well known and commonly adopted MultiLayer Perceptron
neural network and the more recent Support Vector Regression, are applied to solve the problem of soil moisture
retrieval from active and passive microwave data. Thank to the use of both simulated and real in situ data, it was possible
to investigate the effectiveness of both techniques in different operative scenarios, including the situation of limited
availability of training samples which is typical in real estimation problems. Moreover, for each scenario, different
configurations of the input channels (polarization, acquisition frequency and angle) have been considered. The
comparison between the two methods has been carried out in terms of different figure of merits, including error
measurements and correlation coefficients between estimated and true values of the desired biophysical parameter. The
results achieved indicate the Support Vector Regression as an effective alternative to the neural network approach, due to
a general better estimation accuracy and a higher robustness to outliers, especially in case of limited availability of
samples.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.