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.

We investigate in this work the soil moisture retrieval from P-band polarimetric Synthetic Aperture Radar (SAR) data. In particular, the analyzed SAR data were collected during a helicopter-borne campaign conducted over the Apulian Tavoliere plain, Southern Italy, on July 2021. The soil moisture estimates, obtained through the Polarimetric Two-Scale Model (PTSM), are then synthetically compared with in-situ measurements collected at the same time of the radar survey.

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.

PRISMA (PRecursore IperSpettrale della Missione Applicativa) is a demonstrative spaceborne mission, fully deployed by the Italian Space Agency (ASI). To support the calibration/validation activities of the PRISMA hyperspectral mission, ASI and the National Research Council (CNR) started in 2019 the PRISCAV project (Scientific CAL/VAL of PRISMA mission). The main objective of PRISCAV is the comprehensive characterization of the performances of the PRISMA payload in orbit in different operational scenarios and the verification of the durability in time of the performances. To this end, PRISCAV created a network of 12 instrumented sites showing different land-use and surface settings (Snow; Sea; Inland and Coastal Water; Forest and Cropland) to obtain independent and traceable in-situ and airborne Fiducial Reference Measurements (FRM) simultaneous to PRISMA acquisitions in order to assess the required performance of sensor, data products, and processors at the different levels (i.e. Top-of-Atmosphere Level 1 Radiances and Bottom-of-Atmosphere Level 2 Reflectance standard products). Moreover, on some of these sites, simultaneous PRISMA and airborne AVIRISNG acquisitions were made coupling remote sensing with in-situ observations to support new mission development and in particular the Copernicus Hyperspectral Imaging Mission for the Environment (CHIME). Recent updates on CAL/VAL activities and on AVIRSNG campaigns are presented in this contribution.

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.

The upcoming launch of the L-band Synthetic Aperture Radar (SAR) satellite mission Radar Observing System for Europe L-band SAR (ROSE-L) will enable multi-frequency SAR observations when combined with existing C-band satellite missions (e.g., Sentinel-1). Due to the different penetration depths of the SAR signals, multi-frequency SAR offers great potential for field-scale agricultural monitoring and the estimation of soil and plant parameters. The SARSense campaign, conducted between June and August 2019 at the Selhausen agricultural test site near Jülich, Germany, has yielded a comprehensive dataset that includes both air- and space-borne C- and L-band SAR data, extensive in-situ field measurements of soil and plant parameters as well as unmanned aerial systems (UAS)-based multispectral and thermal infrared measurements and cosmic neutron sensing observations. The study provides both, an insight into the strengths and limitations of the acquired dataset as well as an analysis of the different behaviour of C- and L-band backscattering on changing soil moisture and plant parameters for taproot crops and cereals.

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.

In summer 2019 the SARSense campaign was held in Jülich, Germany, to provide insights into the potentials and specifications of the ESA Copernicus candidate mission ROSE-L (Radar Observation System for Europe). ROSE-L will consist of two satellites that carry a polarimetric L-band SAR. Since the L-band signal can penetrate through many natural materials such as vegetation, dry snow and ice, the mission will provide additional information that cannot be gathered by the Copernicus Sentinel-1 C-band SAR mission. The overall objective of the SARSense 2019 campaign is to analyze the mission design concerning its potential for agricultural monitoring services including target applications such as soil moisture monitoring, irrigation management, crop type discrimination, food security and precision farming. The SARSense in situ measurements of soil moisture, soil temperature, vegetation properties, UAS-based multispectral and thermal mapping, as well as the airborne SAR observations are presented as well as strategies for soil moisture retrieval and first analysis.

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.

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%.

In this study, LAI maps derived from SPOT and MERIS data have been compared. The analysis has been conducted over an agricultural site located in Southern Italy, where temporal series of ground and SPOT and MERIS data have been acquired during the 2006-2008 growing seasons. LAI retrieved from SPOT data has been firstly validated by using in situ measurements. Then, LAI derived from SPOT and MERIS over large fields of wheat, sugar beet and tomato has been compared. Results show that LAI retrieved from MERIS data is underestimated as compared with LAI retrieved from SPOT data. However, for wheat and beet crops, the root mean square error for LAI-MERIS tested over a large number of fields is about 1 m2/m2, whereas is higher for tomato crop.

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.

The purpose of this paper is to assess the use of C-band HH/VV backscatter ratio for mapping winter wheat. This paper analyzes two temporal series of images acquired in 2006 and 2007 by the Advanced Synthetic Aperture Radar (ASAR) system in alternating polarization (AP) mode, over an agricultural site located in southern Italy. Results on test data show that classification accuracies between 75% and 80% can be achieved by using a single ASAR image, acquired during the peak of the wheat-growing season. To achieve accuracies close to 90%, a spatial averaging at field scale is necessary.

In this study, the use of ASAR HH/VV backscatter ratio for winter wheat classification has been assessed. The experimental analysis has been conducted over an agricultural site located in Southern Italy and mostly dedicated to durum-wheat cultivation. Two temporal series of ASAR AP data acquired during the 2006 and 2007 growing seasons have been analyzed. The obtained results show that a wheat/non-wheat classification accuracy of 75-80% can be achieved by using a single ASAR AP image, acquired during the peak of the wheat growing season. Higher classification accuracies, approximately equal to 90%, can be obtained when a priori information on field boundaries is available and speckle filtering is carried out at field scale.

The objective of this work is to assess the accuracy of LAI maps retrieved from ENVISAT MERIS data over wheat fields. The method consists of comparing, at catchment scale, the LAI maps retrieved from MERIS data to those retrieved from ASAR AP data. The latter were preliminary validated, at field scale, by means of in situ data. The experimental site is an agricultural area, mainly devoted to wheat cultivation, located in the Basilicata region, close to Matera city (Italy). On this area ENVISAT MERIS, ASAR AP and ground data were intensively acquired during the 2004 growing season. Results indicate that errors affecting wheat LAI estimations derived from optical and radar data are comparable.

This paper presents a retrieval algorithm that estimates spatial and temporal distribution of volumetric soil moisture content, at an approximate depth of 5 cm, using multitemporal ENVISAT Advanced Synthetic Aperture Radar (ASAR) alternating polarization images, acquired at low incidence angles (i.e., from 15/spl deg/ to 31/spl deg/). The algorithm appropriately assimilates a priori information on soil moisture content and surface roughness in order to constrain the inversion of theoretical direct models, such as the integral equation method model and the geometric optics model. The a priori information on soil moisture content is obtained through simple lumped water balance models, whereas that on soil roughness is derived by means of an empirical approach. To update prior estimates of surface parameters, when no reliable a priori information is available, a technique based solely on the use of multitemporal SAR information is proposed. The developed retrieval algorithm is assessed on the Matera site (Italy) where multitemporal ground and ASAR data were simultaneously acquired in 2003. Simulated and experimental results indicate the possibility of attaining an accuracy of approximately 5% in the retrieved volumetric soil moisture content, provided that sufficiently accurate a priori information on surface parameters (i.e., within 20% of their whole variability range) is available. As an example, multitemporal soil moisture maps at watershed scale, characterized by a spatial resolution of approximately 150 m, are derived and illustrated in the paper.

Several efforts, aimed at developing and refining crop backscattering models, have been done during the last years. Although important advances have been achieved, it is recognized that further work is required, both in the electromagnetic characterization of single scatterers and in the combination of contributions. This work is focused on the description of leaf geometry and of the internal structure of stems. Recently developed routines, able to model the scattering cross sections of curved sheets and hollow cylinders, are adopted for this purpose and run within the multiple-scattering model developed at the University of Rome "Tor Vergata". Input parameters are taken from experimental campaigns. In particular, ground data collected over a maize field at the Central Plain site in 1988, over wheat and maize fields at the Loamy site in 2003, and over wheat fields at the Matera site in 2001 and 2003 are considered. The multitemporal backscattering coefficients at C-band are simulated. The results obtained under different assumptions are compared to each other, and with C-band radar signatures collected over the same fields. The influence of some critical factors, affecting crop backscattering, is discussed. It is demonstrated that a more detailed scatterer characterization may improve the model accuracy, especially in the case of hollow stems.

The objective of this work is to investigate the synergistic use of leaf area index (LAI) retrieved by ENVISAT ASAR data and crop growth models, such as CERES-Wheat, to improve the accuracy of wheat yield predictions. The estimate reliability of CERES-Wheat strongly depends on the accuracy of its numerous inputs, which are not always available or accurate. As a consequence, the model would largely benefit from using updated information on the wheat status, provided by remote sensing at field scale. This work shows that the assimilation of ENVISAT ASAR AP data into the model lead to significant improvements in the wheat dry biomass and the grain yield model predictions.

In this work, the accuracy of soil moisture retrieved from ASAR data over bare or sparsely vegetated surfaces is investigated by means of a simulation study. The soil moisture retrieval method is based on an optimization algorithm that appropriately inverts theoretical direct models by assimilating a priori information on surface parameters. In order to account for a large variability of roughness conditions, two complementary models have been used, namely the integral equation method model and the geometrical optics model. The performance of the inversion method has been assessed on simulated noisy ASAR data, as a function of different a priori information quality level.

In this work, unsupervised change detection techniques, based on three different way to compare images, are presented. Two Landsat TM registered and corrected multi-spectral images, acquired on the same geographical area on 18 May 1996 and 21 May 1997, have been used. In the first comparison technique, for each pair of corresponding pixels, the spectral change vector has been computed as the squared difference in the features vectors at the two times. In the second method, the difference image has been computed using, pixel by pixel, a chi square transformation. The third technique is based on the application of a Self-Organizing Map (SOM) neural network to clusterize the two images before comparison. The three obtained difference images has been then analyzed by using a fully automatic thresholding method exploiting the expectation-maximization (EM) algorithm. The experimental results obtained for the three difference images are comparable, showing a reliable robustness of the unsupervised approach, and only few change are detected on the analyzed scene. Moreover, the experimental results have been compared with a change detection map computed by using a supervised technique, obtaining a good agreement between unsupervised and supervised results that confirms the reliability of the considered approach. The encouraging obtained results allow to use the so-computed percentage value of changes as probability of class transitions in input to a Bayesian supervised change detection method, as presented in a companion paper by the same authors. In this framework, the unsupervised approach may be used to support supervised techniques, providing land cover transitions that can be used as guess values

Based on recent experimental studies which have found a strong correlation between a multitemporal series of C-band HH/W backscatter ratios acquired at 40deg incidence angle and wheat biomass, this work investigates the effect of the assimilation of the radar retrieved information into CERES-Wheat crop model. A sensitivity analysis has shown that an inaccurate knowledge of some model inputs, concerning soil properties and crop management, can lead to erroneous predictions. However adopting a reinitialisation assimilation strategy, significant improvements in the model estimations have been obtained

In this work, the effects of SAR measurement errors as well as direct model errors on soil moisture retrieval from SAR data are investigated. In particular, the attention is focused on understanding under which conditions it is more convenient: a) feed the retrieval algorithm with accurate backscattering values (i.e. estimated at "field scale") then retrieve soil moisture estimate directly at "field" scale; b) use relatively noisy backscattering values, estimated at smaller scales (i.e. "segment scale"), to retrieve soil moisture estimates at "segment" scale and subsequently average the obtained soil moisture estimates at "field" scale. The adopted soil moisture retrieval algorithm is based on a regularized Neural Networks appropriately trained by IEM model. The SAR synthetic data simulates SAR data acquired by ERS and ENVISAT satellites. The performance of the inversion method is -given as a function of the SAR configuration and noise level.

In this paper, two model-based methods for the soil moisture retrieval from SAR data are investigated. These methods implicitly consider the physical theory relating the direct relationships between geophysical parameters and SAR measurements and, moreover, can incorporate a priori information to make the parameter estimation more accurate. Given that the inverse problem of recovering soil moisture from SAR observations doesn't have a unique solution, the proposed methods perform a probabilistic estimation of such parameter, finding solutions representative of an unknown probabilistic distribution such as the mean or the most probable solutions. The methods are a Neural Network based-methods and a Mixture Model method. The difference of the solution found by these methods are discussed. Moreover, simulations about soil moisture estimations from ERS and ENVISAT ASAR data are presented.

Photo interpretation by human experts has been the major data source for urban planning and monitoring applications. However, images collected from a space based sensor which combines reasonably good spectral and spatial resolution, could provide an useful tool for automatic monitoring of urban area changes. With this aim, in this paper, a data fusion technique, based on a RGB - HIS transformation, has been adopted for combining high spatial resolution panchromatic satellite with multi-spectral low resolution IKONOS II images. Moreover , textural information, which characterizes urban area, has been extracted with the use of a filter for the edge extraction. An MLP classifier has been trained to produce a labelled image with great accuracy in test even if a limited training set has been used. For a photo interpreter, the results reveal a good feasibility of the classified image for monitoring the presence of changes in urban areas, useful for a cartographic updating. different years. The percentage of correctly classified objects was 89%, whereas the percentage of correct object changes was equal to 83 %, with a false alarm rate of 5%. In (3) a parallelepiped supervised classification algorithm is used to obtain a land cover map characterized by seven classes from two pan-sharpened multi-spectral images at 1m resolution. Overall accuracy values of 75-83% are obtained on test data by using a PS-MS / RGB band composition and an RGB / NIR band composition, respectively. In (4) a neuro-fuzzy classifier based on a set of IF- Then-rules is compared with a Back Propagation neural network and a Maximum Likelihood (ML) classifier to produce a land cover map from IKONOS data on the Korean peninsula, characterized by mixed composition areas. The neuro-fuzzy classifier was more accurate than other classifiers on mixed composition areas, whereas the maximum likelihood performed better on areas such as roads. As input features, the authors considered the four multi-spectral band at low spatial resolution. The usefulness of IKONOS imagery for classification of urban and suburban scenes was also investigated in (5), where a hierarchical fuzzy classification techniques is proposed to improve the classification accuracy obtained by a ML traditional classifier when using only the MS bands of IKONOS data. The ML performance of about 81% was in fact increased to 88%, by using as input to the hierarchical classifier some textural features, extracted from the PAN band, beside the ML classified image. The objective of this work was twofold. First, to validate the feasibility of the RGB-HSI data fusion approach to assimilate the information derived from IKONOS high spatial / low spectral resolution data and the spectral information from low spatial / high spectral resolution. Second, to fully exploit the contextual information of IKONOS PAN data for the automatic classification of urban settlements. Two areas in Southern Italy, characterized by a different typical landscape, were selected for the study. The first is a country area with little villages, tourist facilities and isolated holiday houses

This paper aims to establish the joint roughness statistics for rms height s and correlation length l for agricultural bare soil fields and a variety of tillage conditions. To do so, we make use of a unique pan-European database of profile measurements covering five different sites and containing approximately 1.5 km of profile data. A preliminary assessment of the validity of the derived roughness statistics for electromagnetic scattering models is also carried out by comparing /spl sigma//sup 0/ predictions obtained using the integral equation model with derived roughness statistics and European Remote Sensing 1 and 2 (ERS 1/2) synthetic aperture radar observations. The results are then summarized within the overall context of roughness description and we discuss the implications in terms of forward modeling and inversion. The limitations of s and l parameters as roughness descriptors are also underlined.

This paper investigates the relationship between C-band backscatter measurements and wheat biomass and the underlying soil moisture content. It aims to define strategies for retrieval algorithms with a view to using satellite C-band synthetic aperture radar (SAR) data to monitor wheat growth. The study is based on a ground-based scatterometer experiment conducted on a wheat field at the Matera site in Italy during the 2001 growing season. From March to June 2001, eight C-band scatterometer acquisitions at horizontal-horizontal and vertical-vertical polarization, with incidence angles ranging from 23/spl deg/ to 60/spl deg/, were taken. At the same time, soil moisture, wheat biomass, and canopy structure were collected. The paper describes the experiment and investigates the radar sensitivity to biophysical parameters at different polarizations and incidence angles, and at different wheat phenological stages. Based on the experimental results, the retrieval of wheat biomass and soil moisture content using Advanced Synthetic Aperture Radar data is discussed.

Microwave sensors are of primary importance in mapping surface states and measuring some significant quantities which affect the hydrological cycle. A space mission aiming at monitoring soil moisture and surface salinity at a global scale is suggested. The mission is based on a combination of polarimetric active and passive microwave sensors.

This paper presents a comparative evaluation between a classification strategy based on the combination of the outputs of a neural (NN) ensemble and the application of Support Vector Machine (SVM) classifiers in the analysis of remotely sensed data. Two sets of experiments have been carried out on a benchmark data set. The first set concerns the application of linear and non linear techniques to the combination of the outputs of a Multilayer Perceptron (MLP) neural network ensemble. In particular, the Bayesian and the error correlation matrix approaches are used for coefficient selection in the linear combination of the network's outputs. A MLP module is used for the non linear outputs combination. The results of linear and non linear combination schemes are compared and discussed versus the performance of SVM classifiers. The comparative analysis evidences that the nonlinear, MLP based, combination provides the best results among the different combination schemes. On the other hand, better performance can be obtained with SVM classifiers. However, the complexity of the SVM training procedure can be considered a limitation for SVMs application to real-world problems.

Assesses the feasibility of retrieving soil moisture content over smooth bare-soil fields using European Remote Sensing synthetic aperture radar (ERS-SAR) data. The roughness conditions considered in this study correspond to those observed in agricultural fields at the time of sowing. Within this context, the retrieval possibilities of a single-parameter ERS-SAR configuration is assessed using appropriately trained neural networks. Three sources of error affecting soil moisture retrieval (inversion, measurement, and model errors) are identified, and their relative influence on retrieval performance is assessed using synthetic datasets as well as a large pan-European database of ground and ERS-1 and ERS-2 measurements. The results from this study indicate that no more than two soil moisture classes can reliably be distinguished using the ERS configuration, even for the restricted roughness range considered.

In this work, the results obtained in the classification of a multi-source - multi-temporal remote sensed data set by means of a distributed neuro-fuzzy system are compared with the results of a traditional centralized neural classification system, based on a single multilayer perceptron (MLP) neural network module. The distributed system is composed by a set of neural classifiers, whose partial results were combined with both Sugeno and Choquet fuzzy integrals. Two classification experiments were carried out with the distributed system. In the first experiment, each neural module of the distributed system used the same learning rule but was trained with a subset of the input features, i.e., a specific spectral band. In the second experiment, the neural modules of the system were trained with the same complete set of input features available for each training pixel, but consisted of MLP networks characterized by different specific topologies or different neural algorithms. The results show that larger improvements can be obtained by combining more independent classifiers. The Choquet fuzzy integral provided better performance than Sugeno fuzzy integral. The centralized system, based on a single MLP module, provided the best classification performance.

The surface roughness parameters commonly used as inputs to electromagnetic surface scattering models (SPM, PO, GO, and IEM) are the root mean square (RMS) height s, and autocorrelation length l. However, soil moisture retrieval studies based on these models have yielded inconsistent results, not so much because of the failure of the models themselves, but because of the complexity of natural surfaces and the difficulty in estimating appropriate input roughness parameters. In this paper, the authors address the issue of soil roughness characterization in the case of agricultural fields having different tillage (roughness) states by making use of an extensive multisite database of surface profiles collected using a novel laser profiler capable of recording profiles up to 25 m long. Using this dataset, the range of RMS height and correlation values associated with each agricultural roughness state is estimated, and the dependence of these estimates on profile length is investigated. The results show that at spatial scales equivalent to those of the SAR resolution cell, agricultural surface roughness characteristics are well described by the superposition of a single scale process related to the tillage state with a multiscale random fractal process related to field topography.

Fuzzy learning vector quantization (FLVQ), also known as the fuzzy Kohonen clustering network, was developed to improve performance and usability of on-line hard-competitive Kohnen's vector quantization and soft-competitive self organizing map (SOM) algorithms. The FLVQ effectiveness seems to depend on the range of change of the weighting exponent m(t). In the first part of this work, extreme m(t) values (1 and 1, respectively) are employed to investigate FLVQ asymptotic behaviors. This analysis shows that when m(t) tends to either one of its extremes, FLVQ is affected by trivial vector quantization, which causes centroids collapse in the grand mean of the input data set. No analytical criterion has been found to improve the heuristic choice of the range of m(t) change. In the second part of this paper, two FLVQ and SOM classification experiments of remote sensed data are presented. In these experiments the two nets are connected in cascade to a supervised second stage, based on the delta rule. Experimental results confirm that FLVQ performance can be greatly affected by the user's definition of the range of change of the weighting exponent. Moreover, FLVQ shows instability when its traditional termination criterion is applied. Empirical recommendations are proposed for the enhancement of FLVQ robustness. Both the analytical and the experimental data reported seem to indicate that the choice of the range of m(t) change is still open to discussion and that alternative clustering neural-network approaches should be developed to pursue during training: 1) monotone reduction of the neurons' learning rate and 2) monotone reduction of the overlap among neuron receptive fields.

Prospective authors are requested to submit new, unpublished manuscripts for inclusion in the upcoming event described in this call for papers.