The STEAM (SaTellite Earth observation for Atmospheric Modelling) project, funded by the European Space Agency, aims at investigating new areas of synergy between high-resolution numerical weather prediction (NWP) models and data from spaceborne remote sensing sensors. An example of synergy is the incorporation of high-resolution remote sensing data products in NWP models. The rationale is that NWP models are presently able to produce forecasts with a spatial resolution in the order of 1 km, but unreliable surface information or poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. It is expected that forecast inaccuracies could be reduced by ingesting high resolution Earth Observation derived products into models operated at cloud resolving grid spacing. In this context, the Copernicus Sentinel satellites represent an important source of data, because they provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed, columnar water vapor) used NWP models runs. This paper presents the first results of the experiments carried out in the framework of the STEAM project, regarding the ingestion/assimilation of surface information derived from Sentinel data into a NWP model. The experiments concern a flood event occurred in Tuscany (Central Italy) in September 2017. Moreover, in view of the assimilation of water vapor maps obtained by applying the SAR Interferometry technique to Sentinel-1 data, the results of the assimilation of Zenith total delay data derived from global navigation satellite system (GNSS) are also presented.
In this work, a comparison between soil moisture products derived from satellite and land model data was performed; in particular, the soil moisture retrievals of SMOS and ASCAT were compared with those of the ERA-Interim/Land model, produced by the ECMWF in a timeframe of 3 years. Subsequently, for a limited period of time, the product from the SMAP radiometer was joined to SMOS, ASCAT and ERA-Interim model data as a fourth dataset. In both cases, the whole H-SAF region of interest, which includes Northern Africa and Europe, was analysed. In order to validate the products, the Triple Collocation technique was applied to estimate the independent error standard deviation of three systems that observe the same target parameter. When more than three datasets were available, the Quadruple Collocation technique was used to jointly estimate the error standard deviation of four sources. Moreover, when the SMOS and SMAP radiometer products were considered, the Extended Collocation was adopted in order to evaluate the error variances of the systems, taking into account the possible presence of an error cross-correlation between the radiometer retrievals.
Nowadays a well-established tool for Earth remote sensing is represented by Spaceborne synthetic aperture radars (SARs) operating at L-band and above that offers a microwave perspective at very high spatial resolution in almost all-weather conditions. Nevertheless, atmospheric precipitating clouds can significantly affect the signal backscattered from the ground surface on both amplitude and phase, as assessed by numerous recent works analyzing data collected by COSMO-SkyMed (CSK) and TerraSAR-X (TSX) missions. On the other hand, such sensitivity could allow detecting and quantifying precipitations through SARs. In this work, we propose an innovative processing framework aiming at producing X-SARs precipitation maps and cloud masks. While clouds masks allow the user to detect areas interested by precipitations, precipitation maps offer the unique opportunity to ingest within flood forecasting model precipitation data at the catchment scale. Indeed, several issues still need to be fully addressed. The proposed approach allows distinguishing flooded areas, precipitating clouds together with permanent water bodies. The detection procedure uses image segmentation techniques, fuzzy logic and ancillary data such as local incident angle map and land cover; an improved regression empirical algorithm gives the precipitation estimation. We have applied the proposed methodology to 16 study cases, acquired within TSX and CSK missions over Italy and United States. This choice allows analysing different typologies of events, and verifying the proposed methodology through the available local weather radar networks. In this work, we will discuss the results obtained until now in terms of improved rain cell localization and precipitation quantification.
In this work, a multitemporal algorithm (MLTA), originally conceived for the C-band radar aboard the Sentinel-1 satellite, has been updated in order to retrieve soil moisture from L-Band radar data, such as those provided by the NASA Soil Moisture Active Passive (SMAP) mission. Such type of algorithm may deliver frequent and more accurate soil moisture maps mitigating the effect of roughness and vegetation changes, which are assumed to occur at longer temporal scales with respect to the soil moisture changes. Within the multitemporal inversion scheme based on the Bayesian Maximum A Priori (MAP) criterion, a dense time series of radar measurements is integrated to invert a forward backscattering model which includes the contribution from vegetation. The calibration and validation tasks have been accomplished by using the data collected during the SMAP Validation Experiment 12.The SMAPVEX12 campaign consists of L-Band images collected by the UAVSAR sensor, in situ soil moisture data and measurements of vegetation parameters, collected during the growing season of several crops (pasture, wheat, soybean, corn, etc.). They have been used to update the forward model for bare soil scattering at L-band with respect to the Oh and Sarabandi model previously used at C band. Moreover, the SMAPVEX12 data have been also used to tune a simple vegetation scattering model which considers two different classes of vegetation: those producing mainly single scattering effects, and those characterized by a significant multiple scattering involving terrain surface and vegetation elements interaction.
The availability of the data provided by present and future constellations of Synthetic Aperture Radar (SAR) sensors and
the development of reliable flood mapping algorithms allows producing frequent flood maps characterized by high
spatial resolution. Progresses have been also achieved in flood modeling, so that a joint use of SAR-derived and modelderived
inundation maps seems to be very promising. This paper presents the major outcomes of a combined use of a
multi-temporal series of COSMO-SkyMed observations and of a hydrodynamic model, accomplished within the
framework of an activity aiming at the interpretation of the dynamics of the flood that hit Albania in January 2010. By
calibrating the model with the COSMO-SkyMed derived maps, a number of products such as water depths, and flow
directions were generated. Results show a good agreement between SAR-derived and model-derived flood extents.
Moreover, the maximum water depths were found in the areas where floodwater was present for the longest period of
time, according to COSMO-SkyMed observations.
A multitemporal algorithm (MLTA) to retrieve soil moisture from radar data, already developed and preliminarily
validated for Sentinel 11, has been modified/updated in order to ingest data provided by the future SMAP (Soil Moisture
Active and Passive) mission. Moreover, the MLTA has been tested using actual EO data at C-band and in situ data
considering a sort of worst case i.e., under well-developed vegetation conditions. The implemented MLT approach
consists of integrating a dense time series of radar backscatter measurements within a multitemporal inversion scheme
based on the Bayesian Maximum A Posteriori (MAP) criterion. The MAP estimator maximizes the probability density
function of the vector of soil parameters (soil moisture and roughness) conditioned to the measurement vector. To correct
the vegetation effects, the water cloud model has been modified in order to better account for the effect of the volume
scattering. Preliminary results have assessed the potential of the algorithm at L-band, whilst the SAR C-band data turned
out to be sensitive to soil moisture even when vegetation was developed.
KEYWORDS: Synthetic aperture radar, Atmospheric modeling, Device simulation, Polarimetry, 3D modeling, Ka band, Clouds, X band, Scattering, Systems modeling
Spaceborne synthetic aperture radars (SARs) operating at X-band and above allow observations of Earth surface at very
high spatial resolution. Moreover, recent polarimetric SARs enable the complete characterization of target scattering and
extinction properties. Nowadays several spaceborne X-band SAR systems are operative, and plans exist for systems
operating at higher frequency bands (i.e. Ku, Ka and W). Although higher frequencies may have interesting and
distinctive applications, atmospheric effects, especially in precipitating conditions, may affect the surface SAR response
in both the signal amplitude and its phase, as assessed by numerous works in the last years. A valid tool to analyze and
characterize the SAR response in these conditions is represented by forward modeling, where a known synthetic
scenario, which is described by user-selected surface and atmospheric conditions, is considered. Thus, the SAR echoes
corresponding to the synthetic scenarios are simulated using electromagnetic models. In this work a 3-D realistic
polarimetric SAR response numerical simulator is presented. The proposed model framework accounts for the SAR slant
observing geometry and it is able to characterize the polarimetric response both in amplitude and phase. In this work we
have considered both X and Ka bands, thus exploring the atmospheric effects for the present and future polarimetric
systems. The atmospheric conditions are simulated using the System for Atmospheric Modeling (SAM) which is an
high-resolution mesoscale model. SAM is used to define the three-dimensional distribution of hydrometeors which are
among the inputs used in the Hydrometeor Ensemble Scattering Simulator (HESS) T-Matrix which allow simulating the
SAR signal due to the atmospheric component. The SAR surface component is, instead, simulated by a Semi Empirical
Model (SEM) for bare-soils conditions and SEAWIND2 two-scale model for ocean surfaces. The proposed methodology
has been applied in this work to assess the sensitivity of the considered frequency bands to different hydrometeor spatial
distributions above some examples surface backgrounds.
The latest generation synthetic aperture radar (SAR) systems allows providing emergency managers with near real time
flood maps characterized by a very high spatial resolution. Near real time flood detection algorithms generally search for
regions of low backscatter, thus assuming that floodwater appears dark in a SAR image. It is well known that this
assumption is not always valid. For instance, vegetation emerging from floodwater may produce high radar returns
because of the double bounce effect involving water surface and vertical stems. However, even mapping bare or scarcely
vegetated inundated terrains, or crops totally submerged by water can turn out to be a difficult task. In the presence of
wind that roughens the water surface, floodwater can appear bright in SAR images. Moreover, if X-band radars as
TerraSAR-X or COSMO-SkyMed are used to map inundation, not only missed detection, but also false alarms may
occur because of artifacts caused by heavy precipitating clouds that attenuate the radar signal. This paper proposes
possible strategies to cope with flood mapping using SAR data in the presence of wind or heavy precipitation.
The Sentinel-1 mission will offer the opportunity to obtain C-band radar data characterized by short revisit time, thus allowing the generation of frequent soil moisture maps. This paper presents a multitemporal algorithm that exploits such a short revisit time to perform an operational soil moisture mapping. The procedure assumes the availability of a time series of SAR images that is integrated within a retrieval algorithm based on the Bayesian maximum posterior probability statistical criterion. Preliminary results show that the performances of the multitemporal algorithm are better than those provided by a standard monotemporal one. Its implementation can be demanding in terms of computer resource, but a pipeline processing can be implemented in order to fulfill the temporal requirements of the users.
Synthetic Aperture Radar (SAR) systems represent the most powerful tool to monitor flood events because of their allweather capability that allows them to collect suitable images even in cloudy conditions. The quality of flood monitoring using SAR is increasing thanks to the improved spatial resolution of the new generation of instruments and to the short revisit time of the present and future satellite constellations. In particular, the COSMO-SkyMed mission offers a unique opportunity to obtain all weather radar images characterized by short revisit time. To fully exploit these technological advances, the methods to interpret images and produce flood maps must be upgraded, so that an accurate interpretation of the multitemporal radar signature, accounting for system parameters (frequency, polarization, incidence angle) and land cover, becomes very important. The COSMO-SkyMed system has been activated several times in the last few years in consequence of the occurrence of flood events all over the world in order to provide very high resolution X-band SAR images useful for flood detection purposes. This paper discusses the major outcomes of the experiences gained from using COSMO-SkyMed data for the purpose of near real time generation of flood maps. A review of the mechanisms which determine the imprints of the inundation on the radar images is provided and the approach designed to process the data and to generate the flood maps is also summarized. Then, the paper illustrates a number of significant case studies in which flood events have been monitored through COSMO-SkyMed images. These examples demonstrate the potential of the COSMO-SkyMed system and the suitability of the approach developed for generating the final products, but they also highlight some critical aspects that require further investigations to improve the reliability of the flood maps.
The main objective of this research is to develop, test and validate a soil moisture (SMC)) algorithm for the GMES
Sentinel-1 characteristics, within the framework of an ESA project. The SMC product, to be generated from Sentinel-1
data, requires an algorithm able to process operationally in near-real-time and deliver the product to the GMES services
within 3 hours from observations. Two different complementary approaches have been proposed: an Artificial Neural
Network (ANN), which represented the best compromise between retrieval accuracy and processing time, thus allowing
compliance with the timeliness requirements and a Bayesian Multi-temporal approach, allowing an increase of the
retrieval accuracy, especially in case where little ancillary data are available, at the cost of computational efficiency,
taking advantage of the frequent revisit time achieved by Sentinel-1. The algorithm was validated in several test areas in
Italy, US and Australia, and finally in Spain with a 'blind' validation. The Multi-temporal Bayesian algorithm was
validated in Central Italy. The validation results are in all cases very much in line with the requirements. However, the
blind validation results were penalized by the availability of only VV polarization SAR images and MODIS lowresolution
NDVI, although the RMS is slightly > 4%.
Synthetic Aperture Radar (SAR) systems represent the most powerful tool to monitor flood events because of their all-weather
capability that allows them to collect suitable images even in cloudy conditions. The quality of the flood
monitoring using SAR is increasing thanks to the improved spatial resolution of the new generation of instruments and to
the short revisit time of the present and future satellite constellations. To fully exploit these technological advances, the
methods to interpret images and produce flood maps must be upgraded, so that an accurate interpretation of the
multitemporal radar signature, accounting for system parameters (frequency, polarization, incidence angle) and land
cover, becomes very important. The images collected by the COSMO-SkyMed constellation of X-band radars represent
an example of the aforesaid technological advances. This paper presents a case study regarding a flood occurred in
Tuscany (Central Italy) in 2009 monitored using COSMO-SkyMed data. It is shown that the interpretation of the radar
data is not straightforward, especially in the presence of vegetation and should rely on the knowledge about the radar
scattering mechanisms implemented into electromagnetic models. The paper discusses the multitemporal radar
signatures observed during the event and describes the approach we have followed to account for the electromagnetic
background into a semi-automatic data processing system.
The problem of soil moisture retrieval by means of a bistatic radar system, for the purpose of investigating a candidate
bistatic mission, is addressed in this paper. The optimal geometric configuration of measurement in terms of incidence
and observation (scattering) directions is identified. Such a configuration should ensure good sensitivity to soil moisture
and good spatial coverage. We use a theoretical scattering model to simulate the bistatic scattering coefficient of bare
soil for analyzing the sensitivity to soil moisture. The error variance of a linear regression estimator is employed for this
purpose. This index is computed as function of elevation and azimuth scattering angles. Results shows that the best
performances in terms of soil moisture retrieval can be achieved by complementing the bistatic measurements with the
monostatic ones, which are supposed to be available through already operating spaceborne radars. The problem of the
feasibility of the bistatic configuration identified through the sensitivity study is also addressed focusing on the duty
cycle and the spatial coverage.
The COSMO-SkyMed mission offers a unique opportunity to obtain radar images useful for flood mapping, being
characterized by high revisit time, thanks to the four satellites that form its constellation. To study the potentiality of
Cosmo-SkyMed radar data for this purpose, two inundation events are analyzed in this paper, namely the flood occurred
in Myanmar in May 2008 and the event that took place in the city of Alessandria (Northern Italy) in April 2009. For the
first event, two radar images were considered, one temporally close to the peak of the event, and the other one that was
acquired one week later. As for the Alessandria overflow, a time series of images was available. While most of the
literature algorithms are based on fixed thresholds applied on an image temporarily close to the event, our method
accounts both for specular reflection, typical bare flooded soils, and for double bounce backscattering often occurring on
forested and urban inundated areas. Such a model-based approach is expected to improve the accuracy of flood mapping.
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.