This paper reports on the European Space Agency (ESA) SARSimHT-NG experiment conducted in 2022 over a well-researched location in southern Italy. The experiment involved extensive ground data gathering coordinated with the German Aerospace Center’s (DLR) F-SAR airborne acquisitions in C- and L-bands. The campaign aimed to simulate and analyse geostationary Synthetic Aperture Radar (GeoSAR) measurements over an agricultural area. Besides, it explored the synergy between GeoSAR systems and other Low Earth Orbit (LEO) satellite missions, namely Sentinel-1 Next Generation (NG) and the Radar Observation System for Europe in L-band (ROSE-L). The hyper-temporal repeat cycle of GEoSAR systems is instrumental in investigating the rapid processes of the water cycle. The concept was developed for the Hydroterra mission proposal submitted to the ESA Earth Explorer 10 call and studied in phase 0. The paper illustrates the soil and vegetation in situ data collected together with the L- and C-band fully polarimetric SAR time series acquired at a very high revisit. Moreover, insights into the retrieval of the surface dynamics of the water cycle are also provided.

This paper provides an in-depth exploration of the Crop Module within the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project, specifically addressing challenges in precision agriculture. The study unfolds in the "Fortore" irrigation district (Southern Italy), focusing in particular on the 6/B district. The Crop Module, rooted in AquaCrop crop model architecture, emerges as a pivotal component in simulating and predicting crop growth, development, and water dynamics. It operates across leaf development, crop growth and productivity, and water balance levels, ensuring adaptability to daily temperature variations for real-time simulations. In interaction with the Soil Water Balance Module (SWB) and leveraging insights from satellite imagery, the Crop Module undergoes meticulous calibration and validation. The expected outcomes encompass increased precision in irrigation scheduling, early anticipation of water demand, and improved seasonal forecasting. This comprehensive approach positions stakeholders for informed decision-making, fostering sustainability and efficiency in agricultural practices.

This paper reports on a Spatial Decision Support System (SDSS) for the early, medium, and short-term forecast of irrigation needs in a semi-arid Mediterranean environment. The SDSS is developed in the context of the "EarTH Observation for the Early forecasT of Irrigation needS (THETIS)" project supported by the Italian Space Agency (ASI). THETIS integrates hydrologic and crop growth models with advanced Earth Observation (EO) products, Artificial Intelligence (AI) and a WEBGIS interface to provide basin-scale information for efficient planning of irrigation resources. The study describes initial results concerning the irrigated area of the Apulian Tavoliere (AT) served by the Reclamation Consortium of the Capitanata, Foggia, Italy.

An algorithm to identify and monitor tillage practices, using Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) data, is presented. The technique operates on agricultural fields that are either bare or sparsely vegetated. These fields are first segmented using the Normalized Difference Vegetation Index (NDVI), obtained from S-2, or the S-1 VH/VV ratio in overcast conditions. Then, a change detection approach is applied both to S-1 cross-polarized backscatter and copolarized interferometric coherence. To decouple the impact of tillage from that of moisture change on radar measurements, a two-scale strategy is used. The premise is that whereas soil moisture is primarily influenced by precipitation events happening at the medium (1.0-10 km) scale, tillage changes occur at the local, i.e., field (~0.1 km) scale. The algorithm was assessed against a multi-year ground data set collected at three sites. It includes conventional tillage change and no-tilled events. Results achieve an overall accuracy of 81%.

The objective of this paper was to assess the use of multi-frequency SAR data for the mapping and monitoring of the spatial and temporal variability of land surface parameters and agricultural practices. In particular, the focus was on the retrieval of surface soil moisture (SSM) and vegetation water content (VWC) and on the classification and monitoring of irrigation extent and tillage practices at high resolution. The paper illustrates the data basis collected over three European sites, namely Apulian Tavoliere (Southern Italy), Jolanda di Savoia (Northern Italy), and Castilla y Leon (Spain), and the main results.

The retrieval of (almost) daily soil moisture from C-band Sentinel-1 Synthetic Aperture Radar (SAR) records is still influenced by incidence angle effects and vegetation dynamics. In this study we present a method to reduce the effects of both methods on the alpha approximation methods, a time series approach assuming changes in backscattering signals between two consecutive observations are related to a change in soil moisture. By implementing a Fourier series for incidence angle normalization and a linear regression to co-polarized backscatter for a vegetation adaption, the alpha approximation method has been extended to gain high temporal resolution soil moisture time series. The approach was tested in the Rur catchment, Germany and the Apulian Tavoliere, Italy.

In this contribution, the application of an algorithm based on Sentinel-1 and Sentinel-2 data to identify tillage changes over agricultural fields at approximately ∼100m resolution is shown.The aims is to asses the capability of the tool to detect on a large spatial scale tillage events and their temporal repetitiveness. In this respect, this tool can be employed for monitoring fields where the Conservation Agriculture – that has in the no-tillage a main base principle – is applied.The methodology employs a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements.The good accuracy level (better than 80%) derived from a comparison with ground truth data acquired over the Apulian Tavoliere agricultural site, fosters to further improve the tool for practical applications.

The study aims to consolidate and validate a suite of Earth Observation algorithms of interest for applications in agriculture. The algorithms are at different levels of maturity. Still, they share the objective of contributing to sustainable water management and food security. They deal with monitoring the soil moisture, the vegetation water content, the extent of irrigated areas and the changes in the surface roughness of agricultural fields. The paper introduces the data sets, the algorithms and discusses some examples of initial results.

This study proposes a hybrid incoherent–coherent change detection (CD) approach to retrieve surface soil moisture (SSM) from Sentinel-1 data. It combines time-series observations of synthetic aperture radar (SAR) backscatter and interferometric closure phase to deliver a method that does not require external calibration. A proof-of-concept assessment based on synthetic and experimental data is presented. Sentinel-1 andin situdata over a study site in Southern Italy during the Winter–Spring season 2017 that covered both bare and vegetated soil conditions have been acquired and analyzed. For bare soils, results indicate good performance, that is, Pearson correlation ≈0.8 and root mean square error (RMSE) ≈0.05 m3/m3. Conversely, over vegetated surfaces, poor results are found.

Approximately, 30% of the Sentinel-1 (S-1) swath over land is imaged with incidence angles higher than 40°. Still, the interplay among the scattering mechanisms taking place at such a high incidence and their implications on the backscatter information content is often disregarded. This article investigates, through an experimental and numerical study, the S-1 sensitivity to the surface soil moisture (SSM) over agricultural fields observed at low (~33°) and high (~43°) incidence angles and quantifies the impact of the incidence angle on the SSM retrieval accuracy. The study sites are the Apulian Tavoliere (Italy) and REd de MEDición de la HUmedad del Suelo (REMEDHUS) (Spain), which are both instrumented with a hydrologic network continuously measuring SSM. At low incidence angles, results confirm that for crops such as wheat and barley, dominated in C-band by surface scattering, there exists a good sensitivity of S-1 VV to SSM. At high incidence angles, the sensitivity to SSM holds through the combination of the soil attenuated and double bounce scattering. Conversely, over crops dominated by volume scattering, such as sugar beet, the S-1 VV signal is not correlated with the in situ SSM observations, neither at low nor at high incidence. For all the crops, the sensitivity of S-1 to SSM in VH is found significantly lower than in VV. The impact of the incidence angle on the SSM retrieval has been studied with a recursive algorithm based on a short-term change detection approach. An upper and lower bounds for the worsening of the S-1 VV retrieval performance at far versus near range observations have been estimated. In the worst-case scenario, the root mean square error (RMSE) increases from ~0.056 m3/m3, at low incidence, to ~0.071 m3/m3, at high incidence. The mechanism that lowers the retrieval accuracy at high incidence angles is further investigated in the synthetic experiment and its impact on the RMSE is estimated in terms of the volume scattering contribution.

This paper takes stock of a Sentinel-1 (S-1) surface soil moisture (SSM) product, developed in the ESA SEOM project “Exploitation of Sentinel-1 for Surface Soil Moisture Retrieval at High Resolution” (Exploit-S-1). The characteristics of the product are illustrated and the benefits of the synergy with the future L-band Radar Observation System for Europe (ROSE-L) mission are discussed.

Over the Apulian Tavoliere (southern Italy), an activity of ground data collection for the validation of Earth Observation (EO) products is ongoing since 2014. The site is a large agricultural area (about 4000 km2) in the Apulian region (Italy). Over the area, measurements of the main soil and vegetation parameters, relevant for agricultural applications, have been carried out according to international protocols. This article describes the test site, the measurement campaigns and the related research projects. Moreover, examples of the obtained products and services are also given.

This paper investigates the sensitivity of Sentinel-1 (S-1) interferometric coherence to crop structure and near surface soil moisture (SSM) content. The study analyzes a data set collected in 2017 over the Apulian Tavoliere agricultural site (Southern Italy). The data set includes: i) in situ data over more than 600 agricultural fields monitored during the 2017 winter and spring growing seasons; ii) time-series of S-1 IW VV & VH backscatter & interferometric coherence; iii) time series of S-1 SSM maps. The temporal behavior of S-1 coherence and VH backscatter has been assessed over the monitored agricultural fields. Initial results indicate a stronger sensitivity of S-1 coherence than VH backscatter to crop geometric structure. In addition, an analysis at site scale, conducted before and after an important rain event, indicates a change of SSM from 0.18 to 0.30 m3/m3along with a change of S-1 coherence from 0.61 to 0.53.

Time-series retrievals of soil moisture obtained from the Cyclone Global Navigation Satellite System (CYGNSS) constellation are presented. The retrieval approach assumes that vegetation and roughness changes occur on timescales longer than those associated with soil moisture changes to allow soil moisture sensing in the presence of vegetation and surface roughness contributions as well as the varying incidence angles associated with spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) systems. The approach is focused on incoherent scattering from land surfaces due to the expectation that coherent land surface returns arise primarily from inland water body contributions that are not directly representative of soil moisture. An approach for discarding coherent CYGNSS measurements is therefore developed and described. Because the approach requires the retrieval of N temporal soil moisture samples at a given location but uses only N-1 ratios of CYGNSS measured quantities, ancillary information is incorporated in the retrieval through the use of maximum and minimum monthly soil moisture maps obtained from the Soil Moisture Active Passive (SMAP) mission. Retrieved soil moistures are presented for the 6-month period December 2017-May 2018 and are compared against values reported by the SMAP mission. The comparisons suggest that there exists the potential for using spaceborne GNSS-R systems for global soil moisture retrievals with an rms error on the order of 0.04 cm3/cm3over varied terrain.

Soil moisture plays a pivotal role in a wide range of hydrological and geophysical processes. The ability to estimate soil moisture is key to improved characterization of precipitation cycles, soil erosion as well as weather forecasts. However, world-wide in situ monitoring of soil moisture is impractical thus necessitating the need for its remote sensing. Sensitivity to land surface properties using Global Navigation Satellite System Reflectometry (GNSS-R) is well documented suggesting potential use for soil moisture retrieval. This study will attempt to elucidate the physical mechanisms through which GNSS land surface returns will exhibit dependence on soil moisture levels. Furthermore, it will describe a simulation study through which land surface returns, namely the specular Normalized Radar Cross-Section (NRCS), will be modelled and a time series retrieval approach demonstrated through which soil moisture may be derived. Particular emphasis is placed on complications due to surface roughness, land cover, and angle effects.

The objective of this study is to cross-compare three algorithms for retrieving surface soil moisture (SSM) from ESA's Sentinel-1 (S-1) data. The context is provided by the large scientific and application interest in SSM products at high resolution and regional/continental scale that can be retrieved from S-l data alone or in combination with other missions such as NASA/SMAP and ESA/SMOS. Of the three investigated algorithms, one inverts a scattering model exploiting a Bayesian approach, whereas the other two are change detection approaches. The cross-comparison is carried out by using both simulated and experimental data. Strengths and weaknesses of the three algorithms are identified and discussed.

Soil moisture content is an essential climate variable that is operationally delivered at low resolution (e.g. 36-9 km) by earth observation missions, such as ESA/SMOS, NASA/SMAP and EUMETSAT/ASCAT. However numerous land applications would benefit from the availability of soil moisture maps at higher resolution. For this reason, there is a large research effort to develop soil moisture products at higher resolution using, for instance, data acquired by the new ESA's Sentinel missions. The objective of this study is twofold. First, it presents the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution. Second, it assesses the possibility of integrating Sentinel-2 data and additional ancillary information, such as parcel borders and high resolution soil texture maps, in order to obtain soil moisture maps at “field scale” resolution, i.e. ~0.1 km. Case studies concerning agricultural sites located in Europe are presented.

This paper presents an experimental sensitivity analysis of Sentinel-1 (S-1) backscatter to soil moisture (SM) content observed at low (i.e., ~33°) and high (i.e., ~45°) incidence angles over five agricultural fields of an experimental farm located in the Puglia region (Italy). The analysis focuses on the period from March to June 2017 during which 38 S-1 images along ascending orbits were acquired over the site. Results indicate a slight decrease in the radar sensitivity to SM going from low to high incidence with an impact on SM retrieval error that increases from ~5.65 m3/m3% to ~7.63 m3/m3%.

In this paper, an algorithm using Sentinel-1 (S-1) and Sentinel-2 (S-2) data to identify changes of tillage over agricultural fields at approximately ~100m resolution is presented. The methodology implements a multiscale temporal change detection on S-1 VH backscatter in order to single out VH changes due to agricultural practices only. The algorithm can be applied over bare or scarcely vegetated agricultural fields, which are identified from S-2 NDVI measurements. An initial assessment at farm scale using in situ and S-1 and SPOT5-Take5 data, acquired over the Apulian Tavoliere in southern Italy in 2015, is illustrated. A full validation of the approach is in progress over three European agricultural areas located in Italy, Spain and France. Results will be further reported in the paper.

The systematic retrieval of near surface soil moisture (SSM) fields at high resolution (e.g., 0.1-1.0 km) is a challenging task that requires the exploitation of new retrieval algorithms and SAR data with advanced observational capabilities (in terms of spatial/temporal resolution, radiometric accuracy, very large swath, long-term continuity and rapid data dissemination). The launch of the Sentinel-1 (S-1) constellation provides these capabilities and calls for the development and validation of pre-operational SSM products at high resolution. The objective of this paper is to present and initially assess a SSM retrieval algorithm developed in view of S-1 data exploitation. The activity is supported by a large scientific community engaged in fostering a more effective interaction between researchers working in the field of high and low resolution SSM retrieval.

Many previous studies have shown the sensitivity of radar backscatter to surface soil moisture content, particularly at L-band. Moreover, the estimation of soil moisture from radar for bare soil surfaces is well-documented, but estimation underneath a vegetation canopy remains unsolved. Vegetation significantly increases the complexity of modeling the electromagnetic scattering in the observed scene, and can even obstruct the contributions from the underlying soil surface. Existing approaches to estimating soil moisture under vegetation using radar typically rely on a forward model to describe the backscattered signal and often require that the vegetation characteristics of the observed scene be provided by an ancillary data source. However, such information may not be reliable or available during the radar overpass of the observed scene (e.g., due to cloud coverage if derived from an optical sensor). Thus, the approach described herein is an extension of a change-detection method for soil moisture estimation, which does not require ancillary vegetation information, nor does it make use of a complicated forward scattering model. Novel modifications to the original algorithm include extension to multiple polarizations and a new technique for bounding the radar-derived soil moisture product using radiometer-based soil moisture estimates. Soil moisture estimates are generated using data from the Soil Moisture Active/Passive (SMAP) satellite-borne radar and radiometer data, and are compared with up-scaled data from a selection ofin situnetworks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m3/m3over a variety land cover types.

The main objective of this study is to assess the use of Sentinel-1 (S-1) data for surface soil moisture (SSM) retrieval and wheat mapping (WM) at high spatial resolution (e.g. 100–500m), which constitute valuable information for improving crop yield forecast at large scale. A knowledge based classification method and a SSM retrieval algorithm, developed in view of the European Space Agency Sentinel-1 mission, have been applied to a time series of S-1A data collected from October 2014 to April 2015 over a well-documented agricultural site in southern Italy. In particular, observations of SSM content recorded by a network of ground stations deployed in an experimental farm have been used to test the accuracy of the retrieved SSM values. First results indicate an rms error between 5% and 6%. However, the range of observed SSM values is still quite limited and, therefore, longer time series are needed to investigate the retrieval performance over the full range of SSM values.

The objective of this work is to assess the retrieval of above ground dry biomass (ABG) of wheat fields from dual polarized X- and C-band SAR data. A linear regression between the ratio of cross- and co-polarized backscatter (i.e. PQ/PP) and AGB measured during three past (i.e. the TerraSARSIM'03, AgriSAR'06, COSMOLAND'10-11) and one ongoing campaign over the Apulian Tavoliere has been sought and then validated. Results indicate that both at C- and X-band AGB is well correlated with the PQ/PP ratio up to a AGB value of approximately 3 kg/m2; the estimated AGB error is approximately 0.6 kg/m2. Based on the obtained regression functions, AGB maps of the Apulian Tavoliere have been derived from COSMO-SkyMed Ping Pong and Sentinel-1A IW images. The relationship between the observed AGB spatial patterns are in agreement with the spatial distribution of soil fertility and, with some exceptions due to late drought conditions, with wheat grain yield productivity.

A ground network of 12 stations continuously monitoring soil moisture and temperature at various depths has been recently set up over an experimental site of 4km2in the Capitanata plain (Southern Italy). The calibration of the instrumentation is in progress. The long-term high resolution ground observations will be well-suited for SAR-derived soil moisture product validation. Moreover, the ground network will be also associated with hydrologic and agricultural model activities, with the aim of combining land process models with Earth Observation for improving land applications, such as flood/drought and crop yield monitoring and forecast. Indeed, the Capitanata plain is a crucial area in the Mediterranean basin for studying the impact of climate changes and anthropogenic pressure on water availability/demand and wheat production.

In this letter, a C-band SAR classification algorithm mapping agricultural crops dominated by surface or volume scattering is derived and assessed. The algorithm is an adaptive thresholding method based on the iterative solution of the Kittler-Illingworth method applied to exploit temporal series of cross-polarized SAR data. The performances of the classification algorithm have been assessed on ENVISAT ASAR data acquired over Görmin (Germany) during the AgriSAR'06 campaign and on RADARSAT-2 data acquired over Flevoland (The Netherlands) and Indian Head (Canada) during the ESA AgriSAR'09 campaign. The results indicate that the classification method improves the accuracy with respect to the one obtained by the threshold method based on a constant value, unless the data distributions are mono-modal. The algorithm is fast and robust versus changes of site location and it is expected to achieve an average overall accuracy better than 80%.

This paper investigates the use of quasi-dense time-series of L-band SAR images for retrieving soil moisture (mv) maps at a spatial resolution below 1km2. 23 WB1 PALSAR-1 products, acquired from 2008 to 2009 with an average revisit time of 11 days, have been used to retrieve mvmaps over an agricultural area, in Southern Australia, hydrologically monitored with a network of ground stations continuously measuring mvprofiles. The retrieval approach is based on the SMOSAR algorithm inverting temporal changes of radar backscatter. Results indicate an rms error of approximately 6.0% v/v.

The objective of this paper is to investigate the sensitivity of cross-polarized backscatter to soil moisture content (mv) using experimental and simulated data. The experimental data set consists of co- and cross- C- and L band radar and ground data collected over bare fields by the University of Michigan in 1992 and during the Italian ENVISAT 2003 campaign over Matera (Italy), and over wheat fields during the European Space Agency AgriSAR 2006 and 2009 campaigns over DEMMIN (Germany) and Flevoland (The Netherlands), respectively. The simulated data set has been generated by merging a first-order Radiative Transfer model with a second-order Small Slope Approximation model. Preliminary results show that the model can reproduce the observed sensitivity of cross-polarized backscatter to mv. However, the modelled cross-polarized backscatter is biased with respect to the observations, suggesting that a single scale soil roughness is not sufficient to reproduce the backscatter level observed over fairly smooth surfaces.

This paper describes and assesses the quality of the algorithm, “Soil MOisture retrieval from multi-temporal SAR data” (SMOSAR), developed in view of the forthcoming European Space Agency (ESA) Sentinel-1 (S-1) mission. SMOSAR retrieves soil moisture (mv) products at high spatial resolution (i.e. less than 1km) from dense time series of either single (i.e. HH or VV) or dual polarized (i.e. HH+HV or VV+VH) S-1 data. The assessment of the algorithm performance is based on time series of ENVISAT/ASAR data collected over the DEMMIN site (Germany) in 2006 and over the Matera site (Italy) in 2003 and 2005 and RADARSAT-2 data acquired over the Flevoland site (The Netherlands) in 2009. Results indicate that mvcan be retrieved with an accuracy of 5% at HH polarization, whereas at VV polarization more investigations are required to provide reliable figure for the retrievable accuracy.

This paper reports on the results of an Italian project aimed at investigating the use of X-band COSMO-SkyMed (CSK) SAR data for applications in agriculture and hydrology. Existing classification and retrieval algorithms have been tailored to CSK data and time series of crop, leaf area index and soil moisture maps have been retrieved and assessed through the comparison with in situ data collected over three agricultural sites. In addition, the CSK-derived surface parameters have been integrated into crop growth and hydrologic models and the resulting improvements have been assessed. Results indicate that multi-temporal dual-polarized CSK data are very well-suited for agricultural crop classification and that the integration of maps of SAR-derived surface parameters into crop growth and/or hydrologic models, in general, leads to significant improvements in the model performances.

In this paper, a C-band SAR classification algorithm mapping agricultural crops dominated/non-dominated by volume scattering is described and assessed. The algorithm exploits cross-polarized SAR data and it is a part of the SMOSAR algorithm (“Soil MOisture retrieval from multi-temporal SAR data”) developed in view of the forthcoming Sentinel-1 data. The performances of the classification algorithm have been assessed on RADARSAT-2 data acquired over Flevoland (The Netherlands) and Indian Head (Canada) during the ESA AgriSAR'09 campaign. The results indicate that the selected method is fairly robust versus changes of site location and in average it is expected to achieve an overall accuracy equal or better than 80%.

This paper uses a time-series of COSMO-SkyMed SAR images for land cover classification and soil moisture retrieval over an agricultural area located in Southern Australia. The SAR products analyzed are 11 StripMap Ping Pong images, at HH and HV polarizations, acquired at 21° incidence angle and with a revisiting time of either 8 or 16 days. The classification accuracy has been assessed as a function of the polarization and the number of images analyzed. Results confirm that the temporal information is crucial to improve the classification results. An overall accuracy of approximately 82% was achieved for 10 classes. Moreover, soil moisture (mv) maps over bare or sparsely vegetated areas have been retrieved by means of the SMOSAR-X (“Soil MOisture retrieval from multi-temporal SAR data”) algorithm, developed in view of the forthcoming Sentinel-1 data and then adapted to X-band SAR data. The SMOSAR-X algorithm is shown to produce mvmaps with an rmse of 6.6% v/v.

The objective of this paper is to report on the activities carried out during the first year of the Italian project "Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites" (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains.

This paper investigates the use of dense time series of C-band SAR data (i.e. acquired with revisit time within 1-2 weeks) for the retrieval of volumetric soil moisture content (mv) underneath agricultural crops. Its final aim is to contribute at assessing retrieval strategies for monitoring agricultural areas using near future frequent-revisit SAR missions, such as the forthcoming European Sentinel-1. A recently developed mγ retrieval algorithm is firstly presented and then applied to a time series of ASAR HH and HV data collected in 2006 over the agricultural DEMMIN site (Germany). The assessment of the algorithm is provided by comparing the SAR-derived mvmaps over the DEMMIN site with hydrologically modelled mvmaps. Results indicate that mvcan be retrieved with accuracy ranging between 5% and 6%.

It is widely recognized that synthetic aperture radar (SAR) data are a very valuable source of information for the modeling of the interactions between the land surface and the atmosphere. During the last couple of decades, most of the research on the use of SAR data in hydrologic applications has been focused on the retrieval of land and biogeophysical parameters (e.g., soil moisture contents). One relatively unexplored issue consists of the optimization of soil hydraulic model parameters, such as, for example, hydraulic conductivity values, through remote sensing. This is due to the fact that no direct relationships between the remote-sensing observations, more specifically radar backscatter values, and the parameter values can be derived. However, land surface models can provide these relationships. The objective of this paper is to retrieve a number of soil physical model parameters through a combination of remote sensing and land surface modeling. Spatially distributed and multitemporal SAR-based soil moisture maps are the basis of the study. The surface soil moisture values are used in a parameter estimation procedure based on the extended Kalman filter equations. In fact, the land surface model is, thus, used to determine the relationship between the soil physical parameters and the remote-sensing data. An analysis is then performed, relating the retrieved soil parameters to the soil texture data available over the study area. The results of the study show that there is a potential to retrieve soil physical model parameters through a combination of land surface modeling and remote sensing.