Claudia Notarnicola

Polarimetric RADARSAT-2 imagery for soil moisture retrieval in alpine areas

In this work, the polarimetric capability of RADARSAT-2 images is exploited in the aim of soil moisture content retrieval in Alpine meadows and pastures. Three feature extraction methods are investigated: the simple polarimetric intensity and phase processing, the H/A/α polarimetric decomposition, and the Independent Component Analysis (ICA). The features extracted according to these strategies were assessed for their capability to improve the soil moisture estimation by considering both quantitative performance on a set of reference samples and qualitative analysis of the corresponding output soil moisture content maps. The method proposed for the soil moisture estimation was based on the Support Vector Regression technique combined with an innovative multi-objective model selection strategy. The results indicated that the use of polarimetric features such as HH and HV channels improved the estimation of soil moisture content in the investigated mountain area, especially because the HV channel was able to disentangle the vegetation effect on the radar signal. From the preliminary results presented in this paper, the use of the H/A/α polarimetric decomposition and the ICA technique seem to not determine a significant improvement in the soil moisture estimation.

Retrieval of soil moisture profile by combined C-band scatterometer data and a surface hydrological model

The objective of this work is to develop a method to use radar scatterometer data and a hydrological model in order to retrieve soil behaviour at a level greater than C-band microwave penetration depth. For microwave measurements a C-band FM-CW scatterometer has been employed in two campaigns; the device is able to provide backscattering coefficients in the range of+10 dB and -40 dB for incidence angles between 10° and 60°. Subsequently, microwave scatterometer data have been analysed to estimate their sensitivity to the soil moisture patterns of topsoil comparing them with ground truth measurements. For the validation of these radar data, a coupled heat and moisture balance model has been run to predict the hydrological behaviour of the same topsoil starting from point ground truth measurements. In a second run, soil moisture values derived from scatterometer data should have been used for the initialisation of the model. First attempts have been carried out to propagate the surface physical parameters to unreachable soil layers, such as vertical soil moisture profiles.

Estimation of surface soil moisture in alpine areas based on medium spatial resolution SAR time-series and upscaled in-situ measurements

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.

Soil moisture estimation from microwave remote sensing data with nonlinear machine learning techniques

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.

Temporal monitoring of soil surface state by means of a 5.3 GHz ground-based scatterometer

This paper analyses eight different remote sensing campaigns carried out from 1998 to 2001, pointing out the backscattering coefficients behaviour in dependence both to soil moisture and roughness. Our study indicates a clear dependence of backscattering coefficients on soil moisture with an average sensitivity of 0.25 dB/gr/cm3. In a subsequent step these data sets are utilised to validate an inversion procedure based on a Bayesian algorithm aimed at extracting soil moisture information from backscattering coefficients. After a first run, a priori soil moisture information deriving from the simulation of a hydrological model is introduced leading to an improvement both in extracted soil moisture values and in their uncertainties.

A novel multi-temporal approach to wet snow retrieval with Sentinel-1 images (Conference Presentation)

Snow is one of the most relevant natural water resources present in nature. It stores water in winter and releases it in spring during the melting season. Monitoring snow cover and its variability is thus of great importance for a proactive management of water-resources. Of particular interest is the identification of snowmelt processes, which could significantly support water administration, flood prediction and prevention. In the past years, remote sensing has demonstrated to be an essential tool for providing accurate inputs to hydrological models concerning the spatial and temporal variability of snow. Even though the analysis of snow pack can be conducted in the visible, near-infrared and short-wave infrared spectrum, the presence of clouds during the melting season, which may be pervasive in some parts of the World (e.g., polar regions), renders impossible the regular acquisition of information needed for the operational purposes. Therefore, the use of the microwave sensors, which signal can penetrate the clouds, can be an asset for the detection of snow proprieties. In particular, the SAR images have demonstrated to be effective and robust measurements to identify the wet snow. Among the several methods presented in the literature, the best results in wet snow mapping have been achieved by the bi-temporal change detection approach proposed by Nagler and Rott [1], or its slight improvements presented afterwards (e.g., [2]). Nonetheless, with the introduction of the Sentinel-1 by ESA, which provides free-of-charge SAR images every 6 days over the same geographical area with a resolution of 20m, the scientists have the opportunity to better investigate and improve the state-of-the-art methods for wet snow detection. In this work, we propose a novel method based on a supervised learning approach able to exploit both the experience of the state-of-the-art algorithms and the high multi-temporal information provided by the Sentinel-1 data. In detail, this is done by training the proposed method with examples extracted by [1] and refine this information by deriving additional training for the complex cases where the state-of-the-art algorithm fails. In addition, the multi-temporal information is fully exploited by modelling it as a series of statistical moments. Indeed, with a proper time sampling, statistical moments can describe the shape of the probability density function (pdf) of the backscattering time series ([3-4]). Given the description of the shape of the multi-temporal VV and VH backscattering pdfs, it is not necessary to explicitly identify which time instants in the time series are to be assigned to the reference image as done in the bi-temporal approach. This information is implicit in the shape of the pdf and it is used in the training procedure for solving the wet snow detection problem based on the available training samples. The proposed approach is designed to work in an alpine environment and it is validated considering ground truth measurements provided by automatic weather stations that record snow depth and snow temperature over 10 sites deployed in the South Tyrol region in northern Italy. References: [1] Nagler, T.; Rott, H., “Retrieval of wet snow by means of multitemporal SAR data,” in Geoscience and Remote Sensing, IEEE Transactions on , vol.38, no.2, pp.754-765, Mar 2000. [2] Storvold, R., Malnes, E., and Lauknes, I., “Using ENVISAT ASAR wideswath data to retrieve snow covered area in mountainous regions”, EARSeL eProceedings 4, 2/2006 [3] Inglada, J and Mercier, G., “A New Statistical Similarity Measure for Change Detection in Multitemporal SAR Images and Its Extension to Multiscale Change Analysis,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 45, no. 5, pp. 1432-1445, May 2007. [4] Bujor, F., Trouve, E., Valet, L., Nicolas J. M., and Rudant, J. P., “Application of log-cumulants to the detection of spatiotemporal discontinuities in multitemporal SAR images,” in IEEE Transactions on Geoscience and Remote Sensing, vol. 42, no. 10, pp. 2073-2084, Oct. 2004.

Temporal and spatial soil moisture dynamics in mountain meadows by integrating Radarsat 2 images and ground data

In mountain areas, soil moisture is a key parameter for both agricultural management and natural hazard support. This paper presents an approach for retrieval of soil moisture content (SMC) from different satellite sensors with a specific focus on mountain areas. The experimental analysis was carried out on images acquired over the Südtirol/Alto Adige Province (Italy) during 2010-2011 from the RADARSAT2 in quad-pol mode and Envisat ASAR in Wide Swath mode in VV polarization. The methodology for soil moisture retrieval is based on the Support Vector Regression (SVR) method specifically trained to be able to consider topographic effects of the mountain areas. The comparison with ground measurements collected during field campaigns indicates an RMSE value of around 5% of SMC% while the comparison with fixed ground stations reports an error of around 9% of SMC%. Comparing RADARSAT2 and ASAR SMC, both datasets reveal very similar distributions of SMC values. The cumulative histogram curve for the two datasets shows a slight underestimation of SMC in the ASAR product. This could be ascribed to the reduced resolution of ASAR WS and the use of VV polarization.

Multi-source and multi-scale soil moisture dynamic modelling in mountain meadows

A comparison among multi-source data with the main aim to detect soil moisture dynamics in an Alpine catchment is presented. The data sources are: ground measurements derived from field campaigns and meteorological stations, simulations from a hydrological model and estimates derived from SAR images. The test site is located in the north-western part of South Tyrol, mainly covered by pastures and meadows. The analysis indicates that the diverse sources are able to detect soil moisture dynamics at different spatial and temporal scales. Remote-sensing observations show consistent patterns through the summer season. Major control is land-use, with irrigated meadows in the bottom of the valley being the moister areas, and pastures along the upper hillslope the driest areas. Secondary control is topography, with increased moisture in convergent locations. Model simulations better reproduce the temporal trends as also detected by the ground stations; however, spatial patterns are quite different, with models results showing a much more uniform distribution.

Environmental changes induced by dump pollution analyzed through historical orthophotos and multispectral images

A series of analyses has been carried out in a landscape where for three decades a dump was located by using three orthophotos and a multispectral image from ASTER sensor. The orthophotos acquisition time spans from 1953 to 2002. The aim of the analyses is twofold. On one side the analysis detects some paleao structures that were visible in 1953 and that under the pressure of human activities have disappeared. The change detection analysis indicates two main phases of changes. The first main change from 1953 to 1973 interests mainly woodland and wild vegetation while the second one is driven by agricultural practices. On the other side, the analysis allows finding some useful indices that are able to reveal degraded areas. The combination of some spatial indices, such as Shannons diversity index (SHDI) ad contiguity index, has been found suitable to distinguish the characteristic texture of a dump from that of other fields. Furthermore, by using thermal infrared bands from the ASTER image, a thermal anomaly is found in correspondence of the dump location.

Effect of prior information on Bayesian estimates of dielectric constant from remotely sensed data

Bayesian inference has been proved to be a valuable tool in inversion processes. In this study, it is applied in two cases, both aimed at estimating dielectric constant from radar measurements. The first case is devoted to merge point measurements deriving from radiometer and scatterometer data on bare soils. The second case uses, in the inversion process, only active scatterometer data, but introduces supplementary information from the simulations of a hydrological model. In this Bayesian algorithm the key point is the evaluation of a joint probability density function based on the knowledge of data sets consisting of soil parameters measurements and the corresponding remote sensed data. It is obtained by applying the maximum likelihood procedure. As a further step, the influence of prior information about roughness is assessed within the context of the dielectric constant retrieval. At the beginning, a prior uniform distribution is assumed for all surface parameters. Subsequently, a non-uniform prior distribution, based on field measurements, is introduced in order to verify its impact on the estimates and the relative errors.