In this work we address the synergy of optical, SAR (Synthetic Aperture Radar) and topographic data in soil moisture retrieval over an Alpine area. As estimation technique, we consider Gaussian Process Regression (GPR). The test area is located in South Tyrol, Italy where the main land types are meadows and pastures. Time series of ASAR Wide Swath - SAR, optical, topographic and ancillary data (meteorological information and snow cover maps) acquired repetitively in 2010 were examined. Regarding optical data, we used both, daily MODIS reflectances, and daily NDVI, interpolated from the 16-day MODIS composite. Slope, elevation and aspect were extracted from a 2.5 m DEM (Digital Elevation Model) and resampled to 10 m. Daily soil moisture measurements were collected in the three fixed stations (two located in meadows and one located in pasture). The snow maps were used to mask the points covered by snow. The best performance was obtained by adding MODIS band 6 at
1640 nm to SAR and DEM features. The corresponding coefficient of determination, R2, was equal to 0.848, and the root mean square error, RMSE, to 5.4 % Vol. Compared to the case when no optical data were considered, there was an increase of ca. 0.05 in R2 and a decrease in RMSE of ca. 0.7 % Vol. This work showed that the joint use of NDVI or water absorption reflectance with SAR and topographic data can improve the estimation of soil moisture in specific Alpine area and that GPR is an effective method for estimation.
The goal of this study was to assess the applicability of medium resolution SAR time-series, in combination with in-situ
point measurements and machine learning, for the estimation of soil moisture content (SMC). One of the main
challenges was the combination of SMC point measurements and satellite data. Due to the high spatial variability of soil
moisture a direct linkage can be inappropriate. Data used in this study were a combination of in-situ data, satellite data
and modelled SMC from the hydrological model GEOtop. To relate the point measurements with the satellite pixel
footprint resolution, a spatial upscaling method was developed. It was found that both temporal and spatial SMC patterns
obtained from various data sources (ASAR WS, GEOtop and meteorological stations) show similar behaviors.
Furthermore, it was possible to increase the absolute accuracy of the estimated SMC through spatial upscaling of the
obtained in-situ data. Introducing information on the temporal behavior of the SAR signal proves to be a promising
method to increase the confidence and accuracy of SMC estimations. Following steps were identified as critical for the
retrieval process: the topographic correction and geocoding of SAR data, the calibration of the meteorological stations
and the spatial upscaling.
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.
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 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
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.