Empirical fitting of forward backscattering models for multitemporal retrieval of soil moisture from radar data at L-band

A multitemporal algorithm, originally conceived for the C-band radar aboard the Sentinel-1 satellite, has been updated to retrieve soil moisture from L-band radar data, such as those provided by the National Aeronautics and Space Administration Soil Moisture Active/Passive (SMAP) mission. This type of algorithm may deliver more accurate soil moisture maps that mitigate the effect of roughness and vegetation changes. Within the multitemporal inversion scheme based on the Bayesian maximum a posteriori probability (MAP) criterion, a dense time series of radar measurements is integrated to invert a forward backscattering model. The model calibration and validation tasks have been accomplished using the data collected during the SMAP validation experiment 12 spanning several soil conditions (pasture, wheat, corn, and soybean). The data have been used to update the forward model for bare soil scattering at L-band and to tune a simple vegetation scattering model considering 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 algorithm retrievals showed a root mean square difference (RMSD) around 5% over bare soil, soybean, and cornfields. As for wheat, a bias was observed; when removed, the RMSD went down from 7.7% to 5%

Neural Network Emulation of the Integral Equation Model with Multiple Scattering

The Integral Equation Model with multiple scattering (IEMM) represents a well-established method that provides a theoretical framework for the scattering of electromagnetic waves from rough surfaces. A critical aspect is the long computational time required to run such a complex model. To deal with this problem, a neural network technique is proposed in this work. In particular, we have adopted neural networks to reproduce the backscattering coefficients predicted by IEMM at L- and C-bands, thus making reference to presently operative satellite radar sensors, i.e., that aboard ERS-2, ASAR on board ENVISAT (C-band), and PALSAR aboard ALOS (L-band). The neural network-based model has been designed for radar observations of both flat and tilted surfaces, in order to make it applicable for hilly terrains too. The assessment of the proposed approach has been carried out by comparing neural network-derived backscattering coefficients with IEMM-derived ones. Different databases with respect to those employed to train the networks have been used for this purpose. The outcomes seem to prove the feasibility of relying on a neural network approach to efficiently and reliably approximate an electromagnetic model of surface scattering

Defining a Trade-Off Between Spatial and Temporal Resolution of a Geosynchronous SAR mission for Soil Moisture Monitoring

The next generation of synthetic aperture radar (SAR) systems could foresee satellite missions based on a geosynchronous orbit (GEO SAR). These systems are able to provide radar images with an unprecedented combination of spatial ( 641 km) and temporal ( 6412 h) resolutions. This paper investigates the GEO SAR potentialities for soil moisture (SM) mapping finalized to hydrological applications, and defines the best compromise, in terms of image spatio-temporal resolution, for SM monitoring. A synthetic soil moisture\u2013data assimilation (SM-DA) experiment was thus set up to evaluate the impact of the hydrological assimilation of different GEO SAR-like SM products, characterized by diverse spatio-temporal resolutions. The experiment was also designed to understand if GEO SAR-like SM maps could provide an added value with respect to SM products retrieved from SAR images acquired from satellites flying on a quasi-polar orbit, like Sentinel-1 (POLAR SAR). Findings showed that GEO SAR systems provide a valuable contribution for hydrological applications, especially if the possibility to generate many sub-daily observations is sacrificed in favor of higher spatial resolution. In the experiment, it was found that the assimilation of two GEO SAR-like observations a day, with a spatial resolution of 100 m, maximized the performances of the hydrological predictions, for both streamflow and SM state forecasts. Such improvements of the model performances were found to be 45% higher than the ones obtained by assimilating POLAR SAR-like SM maps

Use of Satellite Radar Bistatic Measurements for Crop Monitoring: A Simulation Study on Corn Fields

This paper presents a theoretical study of microwave remote sensing of vegetated surfaces. The purpose of this study is to find out if satellite bistatic radar systems can provide a performance, in terms of sensitivity to vegetation geophysical parameters, equal to or greater than the performance of monostatic systems. Up to now, no suitable bistatic data collected over land surfaces are available from satellite, so that the electromagnetic model developed at Tor Vergata University has been used to perform simulations of the scattering coefficient of corn, over a wide range of observation angles at L- and C-band. According to the electromagnetic model, the most promising configuration is the one which measures the VV or HH bistatic scattering coefficient on the plane that lies at the azimuth angle orthogonal with respect to the incidence plane. At this scattering angle, the soil contribution is minimized, and the effects of vegetation growth are highlighted

analysis of two years of ascat and smos derived soil moisture estimates over europe and north africa

More than two years of soil moisture data derived from the Advanced SCATterometer (ASCAT) and from the Soil Moisture and Ocean Salinity (SMOS) radiometer are analysed and compared. The comparison has been performed within the framework of an activity aiming at validating the EUMETSAT Hydrology Satellite Application Facility (H-SAF) soil moisture product derived from ASCAT. The available database covers a large part of the SMOS mission lifetime (2010, 2011 and partially 2012) and both Europe and North Africa are considered. A specific strategy has been set up in order to enable the comparison between products representing a volumetric soil moisture content, as those derived from SMOS, and a relative saturation index, as those derived from ASCAT. Results demonstrate that the two products show a fairly good degree of correlation. Their consistency has some dependence on season, geographical zone and surface land cover. Additional factors, such as spatial property features, are also preliminary investigated

Bistatic Radar Configuration for Soil Moisture Retrieval: Analysis of the Spatial Coverage

Some outcomes of a feasibility analysis of a spaceborne bistatic radar mission for soil moisture retrieval are presented in this paper. The study starts from the orbital design of the configuration suitable for soil moisture estimation identified in a previous study. This configuration is refined according to the results of an analysis of the spatial resolution. The paper focuses on the assessment of the spatial coverage i.e., on the verification that an adequate overlap between the footprints of the antennas is ensured and on the duty cycle, that is the fraction of orbital period during which the bistatic data are acquired. A non-cooperating system is considered, in which the transmitter is the C-band Advanced Synthetic Aperture Radar aboard Envisat. The best performances in terms of duty cycle are achieved if the transmitter operates in Wide Swath Mode. The higher resolution Image Swath Modes that comply with the selected configuration have a duty cycle that is never less than 12% and can exceed 21%. When Envisat operates in Wide Swath Mode, the bistatic system covers a wide latitude range across the equator, while in some of the Image Swath Modes, the bistatic measurements, collected from the same orbit, cover mid-latitude areas. In the latter case, it might be possible to achieve full coverage in an Envisat orbit repeat cycle, while, for a very large latitude range such as that covered in Wide Swath Mode, bistatic acquisitions could be obtained over about 65% of the area

An evaluation of the potential of Sentinel 1 for improving flash flood predictions via soil moisture–data assimilation

open8siThe assimilation of satellite-derived soil moisture estimates (soil moisture–data assimilation, SM–DA) into hydrological models has the potential to reduce the uncertainty of streamflow simulations. The improved capacity to moni- tor the closeness to saturation of small catchments, such as those characterizing the Mediterranean region, can be exploited to enhance flash flood predictions. When compared to other microwave sensors that have been exploited for SM– DA in recent years (e.g. the Advanced SCATterometer – AS- CAT), characterized by low spatial/high temporal resolution, the Sentinel 1 (S1) mission provides an excellent opportu- nity to monitor systematically soil moisture (SM) at high spatial resolution and moderate temporal resolution. The aim of this research was thus to evaluate the impact of S1-based SM–DA for enhancing flash flood predictions of a hydro- logical model (Continuum) that is currently exploited for civil protection applications in Italy. The analysis was car- ried out in a representative Mediterranean catchment prone to flash floods, located in north-western Italy, during the time period October 2014–February 2015. It provided some important findings: (i) revealing the potential provided by S1- based SM–DA for improving discharge predictions, espe- cially for higher flows; (ii) suggesting a more appropriate pre-processing technique to be applied to S1 data before the assimilation; and (iii) highlighting that even though high spa- tial resolution does provide an important contribution in a SM–DA system, the temporal resolution has the most crucial role. S1-derived SM maps are still a relatively new product and, to our knowledge, this is the first work published in an international journal dealing with their assimilation within a hydrological model to improve continuous streamflow simulations and flash flood predictions. Even though the reported results were obtained by analysing a relatively short time pe- riod, and thus should be supported by further research activ- ities, we believe this research is timely in order to enhance our understanding of the potential contribution of the S1 data within the SM–DA framework for flash flood risk mitigation.openCenci, Luca; Pulvirenti, Luca; Boni, Giorgio; Chini, Marco; Matgen, Patrick; Gabellani, Simone; Squicciarino, Giuseppe; Pierdicca, NazzarenoCenci, Luca; Pulvirenti, Luca; Boni, Giorgio; Chini, Marco; Matgen, Patrick; Gabellani, Simone; Squicciarino, Giuseppe; Pierdicca, Nazzaren

Scene setting for the ESA hydroGNSS GNSS-Reflectometry scout mission

HydroGNSS is a mission concept selected by ESA as a Scout candidate, and consists of a 40 kg satellite that addresses land hydrological parameters using the technique of GNSS Reflectometry, a form of bistatic L-Band radar using satnav signals as the radar source. The four targeted essential climate variables (ECVs) are of established importance to our understanding of the climate evolution and human interaction, and comprise of soil moisture, inundation / wetlands, freeze /thaw (notably over permafrost) and above ground biomass. The technique of GNSS Reflectometry shows potential over all geophysical surfaces for low cost measurement of ocean winds, ocean roughness, soil moisture, flood & ice mapping, and other climate and operational parameters. SSTL developed and flew the SGR-ReSI GNSS remote sensing instrument on the 160 kg UK TechDemoSat-1 (TDS-1) in July 2014 and, with sponsorship from ESA, collected data until TDS-1’s drag-sail was deployed in May 2019. TDS-1 was a precursor for NASA’s CYGNSS mission which uses the SGR-ReSI on its 8-microsatellite constellation for sensing hurricanes. The datasets from TDS-1 have been released via the MERRByS website, and include ocean wind speed measurements and ice extent maps from National Oceanography Centre’s C-BRE inversion. At the same time, researchers recognised the benefits of GNSS reflectometry over land, including the unique capability to sense rivers under forest canopies to a high resolution. HydroGNSS has been proposed for the ESA Scout mission opportunity by a SSTL and a team of partners with a broad range of experience in GNSS technology, GNSS-Reflectometry modelling and applications, and Earth Observation from GNSS-R measurements. The instrument takes significant steps forward from previous GNSS-R experiments by including capability in dual polarisation, dual frequency and coherent reflected signal reception, that are expected to help separate out ECVs and improve measurement resolution. The satellite platform is the 40 kg SSTL-Micro, which has improved attitude determination and a high data link to support the collection of copious quantities scientific data with a short time delay. HydroGNSS builds upon the growing GNSS-R knowledge gained from UK-DMC, TDS-1, and ORORO / DoT-1, and is anticipated to generate a new research data set in GNSS Earth Observation, specifically targeting land and hydrological applications. State of the art satellites that target soil moisture such as ESA SMOS and NASA SMAP are highly valued by scientists and operational weather forecasters, but will be expensive to replace. As evidenced by TDS-1 and CYGNSS, HydroGNSS will be able to take GNSS-R measurements using GNSS signals as a radar source, reducing the size of the satellite platform required. The forward scatter L-band nature of the measurement means that they are complementary to other techniques, and HydroGNSS brings further new measurement types compared to TDS-1 and CYGNSS. The small size and low recurring cost of the HydroGNSS satellite design opens the door to a larger constellation that can further improve spatial and temporal global hydrological measurements to an unprecedented resolution, invaluable to the better understanding of our climate

Effect of the ingestion in the WRF model of different Sentinel-derived and GNSS-derived products: analysis of the forecasts of a high impact weather event

This paper presents the first experimental results of a study on the ingestion in the Weather Research and Forecasting (WRF) model, of Sentinel satellites and Global Navigation Satellite Systems (GNSS) derived products. The experiments concern a flash-floodevent occurred in Tuscany (Central Italy) in September 2017. The rationale is that numerical weather prediction (NWP) models are presently able to produce forecasts with a km scale spatial resolution, but the poor knowledge of the initial state of the atmosphere may imply an inaccurate simulation of the weather phenomena. Hence, to fully exploit the advances in numerical weather modelling, it is necessary to feed them with high spatiotemporal resolution information over the surface boundary and the atmospheric column. In this context, the Copernicus Sentinel satellites represent an important source of data, because they can provide a set of high-resolution observations of physical variables (e.g. soil moisture, land/sea surface temperature, wind speed) used in NWP models runs. The possible availability of a spatially dense network of GNSS stations is also exploited to assimilate water vapour content. Results show that the assimilation of Sentinel-1 derived wind field and GNSS-derivedwater vapour data produce the most positive effects on the performance of the forecast

Cosesimic liquefaction phenomena from DInSAR after the May 20, 2012 Emilia earthquake

In this paper, we have investigated the capability of Differential Interferometry Synthetic Aperture Radar (DInSAR) technique to detect the ground effects induced by liquefaction phenomena occurred during the May 20, 2012 Emilia earthquake. To this aim, a set of COSMO-SkyMed (CSK) SAR images covering the coseismic phase has been used. The detected surface effects have been related to liquefaction of deep sandy layers. Thanks to the geological/geotechnical data in the area and a liquefaction susceptibility analysis of the subsoil, it has been identified a sandy layer between 9 and 13 m in deep, which probably liquefied during the earthquake. The estimated vertical displacements due to liquefaction are comparable with the values measured by DInSAR.Published5-95T. Sismologia, geofisica e geologia per l'ingegneria sismicaN/A or not JC