The objective of this study is to retrieve and compare Leaf Area Index (LAI) maps from temporal series of SPOT, IKONOS and MERIS images acquired, from 2006 to 2008, over an agricultural site in Southern Italy. Results show that the root mean square error (RMSE) of LAI derived from MERIS data is approximately 1 m2 m-2, slightly larger than the one obtained by using SPOT and IKONOS data. In addition, LAI retrieved from MERIS data tends to underestimate LAI retrieved from SPOT and IKONOS data, particularly at low LAI values. Nevertheless, the paper gives examples highlighting the strength of MERIS with respect to SPOT and IKONOS data in providing long and dense temporal series of LAI maps suitable to feature the temporal evolution of vegetation growth at regional scale.

MOIST will exploit and combine optical, thermal infrared and SAR data from Sentinel-1, Sentinel-2 andSentinel-3, each with distinct information content, which enables us to map and update crop water needs in duetime before the crop has been damaged by water stress. From satellite data, spatially distributed information aboutvariation within an agricultural field with frequent updates is provided giving timely information to the farmer.The project focuses on algorithms for deriving satellite products crucial for irrigation management: vegetation,water stress, evapotranspiration and soil moisture, based on the fusion of optical, thermal infrared and radar data,and field experiments over selected crops in different climate zones in Denmark, Spain and Italy.

L-band radar remote sensing of soil moisture has been the subject of research for several decades and has seen increasing attention in recent years. Soil moisture inversion from radar backscatter measurements in the presence of vegetation is a complex and difficult task. Several methods for deriving soil moisture estimates from such measurements have been explored, but these methods typically require the availability of a robust forward model and/or the use of ancillary data such as a vegetation estimator. This study presents a retrieval algorithm which requires neither an advanced forward scattering model nor ancillary vegetation data.

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 of in situ networks used in SMAP validation activities. These results show that the new algorithm can consistently achieve rms errors less than 0.07 m³/m³ over a variety land cover types.

AQUATER is a Decision Support System (DSS) developed to drive crop management decisions at district level in a Mediterranean area; it integrates information from soil and climatic databases with a crop growth simulation model and provides estimates of crop yield at regional scale. AQUATER can assimilate LAI maps derived from Earth observation data in order to mitigate the risk of erroneous model predictions over large areas. In this study, time-series of LAI maps derived from COSMO-SkyMed SAR images, acquired over the Capitanata plain (Puglia region) in 2010 and 2011, have been assimilated by a forcing procedure in AQUATER and the improvements of its predictions have been assessed. Results indicate that the LAI assimilation leads to significant improvements in the yield forecast of sugar beet and tomato crops, whereas in the case of wheat the improvements are marginal.

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 Gormin (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%.

The use of COSMO-SkyMed alone or in synergy with sentinel-1 (S-1) for crop mapping, fresh biomass retrieval and tillage change identification is investigated. For crop mapping Maximum Likelihood (ML) and knowledgebased (KB) have been adopted. Fresh biomass retrieval is carried out using semi-empirical approaches, whereas for the tillage change mapping, a change detection approach is proposed. The experimental basis of the study includes time series of ground, CSK and S-1 data collected over the Apulian Tavoliere (Italy) and the Yanco agricultural (Australia) areas in the framework of ASI and ESA projects.

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-1 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.

The paper investigates the potential of the co-polarized HH/VV backscatter ratio at C-bandand at high incidence angle to discriminate agricultural canopies characterized by smallleaves and vertical stem structure (i.e. cereal crops) from those having a more branchinggeometry (e.g. tomato) or large leaves (e.g. sugar beet). The analysed data set consists ofmulti-temporal C-band HH and VV backscatter data acquired in 2006 and 2007 by theAdvanced Synthetic Aperture Radar (ASAR) system over an agricultural site located inSouthern Italy. The adopted classification scheme is based on a threshold approach, whichis firstly assessed on selected fields and then extended over the entire study area. In addition,the analysis assesses the impact on the classification accuracy of two speckle filteringtechniques, i.e. spatial and combined temporal-spatial speckle filtering. On test data, resultsshow that the classification accuracy of cereal fields is equal to about 80%. This figure canreach up to 90% if a spatial averaging at field scale is applied.

The objective of this study is to describe a new technique, based on dense time series of Copernicus Sentinel-1 (S-1) and Sentinel-2 (S-2) data, aimed to identify tillage changes at regional/continental scale and at approximately 100 m resolution.

The objective of this paper is to report on a first assessment of superficial soil moisture (SSM) products retrieved from Sentinel-1A (S-1A) data. The SSM retrieval is obtained by applying the SMOSAR1 algorithm [1-2], which has been devised to exploit the advanced observational capabilities of S-1 mission, particularly its frequent revisit (i.e. exact revisit with 1 satellite of 12 days & 6 days with 2 satellites). The algorithm inverts the temporal changes of radar backscatter rather than the "single date" backscatter values, as it was usually performed on SAR data acquired by past spaceborne systems, whose revisit was too long to enable an effective time series approach.

A comparison between superficial soil moisture content, m(v), values predicted by the DREAM hydrologic model and those retrieved from time-series of ALOS/PALSAR and COSMO-SkyMed SAR data acquired in 2007 and 2010-2011 is presented. The area investigated is part of the Celone at Ponte Foggia-S. Severo river basin, which is a tributary of the Candelaro river, downstream of the S. Giusto Dam, in Puglia (Southern Italy). Results show a good agreement in terms of bias and rmse between the hydrologic modeled and SAR-retrieved m(v)-values, and open new opportunities for the use of SAR-derived m(v)-values to calibrate/validate hydrologic models in semi-arid areas.

Vegetation indices obtained from remote sensed data can be used to characterize crop canopy on a large scale us-ing a non-destructive method. With the recent launch of the IKONOS satellite, very high spatial resolution (1 me-ter) images are available for the detailed monitoring of ecosystems as well as for precision agriculture.The aim of this study is to evaluate the accuracy of leaf area index (LAI) retrieval over agricultural area that canbe obtained by empirical relationships between different spectral vegetation indices (VI) and LAI measured onthree different dates over the spring-summer period of 2008, in the Capitanata plain (Southern Italy).All the VIs used (NDVI, RDVI, WDVI, MSAVI and GEMI) were related to the LAI through exponential regres-sion functions, either global or crop-dependent. In the first case, LAI was estimated with comparable accuracies forall VIs employed, with a slightly higher accuracy for GEMI, which determination coefficient achieved the value of0.697. Whereas the LAI regression functions were calculated separately for each crop, the WDVI, GEMI and RD-VI vegetation indices provided the highest determination coefficients with values close to 0.90 for wheat and sug-ar beet, and with values close to 0.70 for tomatoes. A validation of the models was carried out with a selection ofindependent sampling data. The validation confirmed that WDVI and GEMI were the VIs that provided the highestLAI retrieval accuracies, with RMSE values of about to 1.1 m2 m-2. The exponential functions, calibrated and vali-dated to calculate LAI from GEMI, were used to derive LAI maps from IKONOS high-resolution remote sensingimages with good accuracy. These maps can be used as input variables for crop growth models, obtaining relevantinformation that can be useful in agricultural management strategies (in particular irrigation and fertilization), aswell as in the application of precision farming.

L'osservazione della Terra da piattaforme spaziali, integrata con misure in situ e con acquisizione da piattaforme aeree, è una tecnologia di riferimento per il monitoraggio di ampie zone del Territorio con una elevata frequenza spaziale. Queste ca-ratteristiche sono essenziali per investigare, da un lato, l'effetto di modifiche indotte da cambiamenti climatici, dall'altro la presenza di situazioni che siano precursori di cambiamenti, in un'ottica di previsione ed allerta. Nel presente lavoro vengono presentati alcuni esempi applicativi in questo scenario.

The objective of this study is to assess the SMOSAR performance using time series of S-1A data acquired over three calibration/validation sites, located in Europe (i.e., Segezia, Italy), USA (Little Washita, Oklahoma) and Australia (Yanco, New SouthWales). Each site is equipped with a hydrologic network continuously measuring SSM values, which will be used to validate the S-1Aretrieved values at high resolution. In addition, a comparison at lower spatial resolution between S-1A, SMOS, ASCAT and SMAP SSMvalues will be carried out in order to better understand the SMOSAR performance at the various spatial scales. Initial results obtained by analysing S-1A time series acquired over the Segezia site in Italy [Mattia et al., 2015] indicate a SSM retrieval error between 0.05 and0.07 cm3/cm3.

The objective of this paper is to report on first results obtained by comparing Sentinel-1A SM products, obtained by using the SMOSAR algorithm, with SM measurements recorded in a Southern Italy site equipped with a ground network of 12 stations continuously monitoring soil moisture and temperature at various depths [Balenzano et al., 2014]. The mean spacing between the stations is 0.5km and additional in situ measurements have been conducted to calibrate and validate the quality and the spatial representativeness of SM measurements provided by the network. A time series of Sentinel-1A images, acquired from late 2014 onward, has been transformed into SM maps and their uncertainties have been assessed. The paper discusses the obtained findings and their implications for future work.

This paper investigates the use of time series of ALOS/PALSAR-1 and COSMO-SkyMed data for the soil moisture retrieval (mv) by means of the SMOSAR algorithm. The application context is the exploitation of mv maps at a moderate spatial and temporal resolution for improving flood/drought monitoring at regional scale. The SAR data were acquired over the Capitanata plain in Southern Italy, over which ground campaigns were carried out in 2007, 2010 and 2011. The analysis shows that the mv retrieval accuracy is 5%-7% m^3/m^3 at L- and X band, although the latter is restricted to a use over nearly bare soil only.

L'obiettivo di questo lavoro è di presentare alcuni risultati iniziali circa la stima di umidità del suolo (SSM) da dati Sentinel-1A (S-1A). La stima è stata compiuta utilizzando l'algoritmo SMOSAR [Balenzano et al., EuJRS, 2013], che è stato sviluppato per sfruttare l'alta risoluzione temporale dei satelliti della missione S-1 (12 giorni con 1 satellite e 6 giorni con 2 satelliti) rispetto alle precedenti missioni SAR, ad esempio ENVISAT/ASAR.

The objective of this work is to assess the retrieval of above ground 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 bserved 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.

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 similar to 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.

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. similar to 0.1 km Case studies concerning agricultural sites located in Europe are presented.

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.

In this work, the objective is to discuss a validation strategy for Sentinel-1 (S-1) Surface Soil Moisture (SSM) Product at 1km resolution

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.

In this work the objective is to illustrate a pre-operational surface soil moisture (SSM) product over the Mediterranean basin at 1 km resolution derived from Sentinel-1 (S-1) observationsto provide a comparison against operational SSM products from SMAP, SMOS and ASCAT missions derived at lower resolution (e.g., 625 km2)

This paper presents an experimental sensitivity analysis of Sentinel-1 (S-1) backscatter to soil moisture (SM) content observed at low (i. e., 33 degrees) and high (i. e., 45 degrees) 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 %.

The objective of this study is to illustrate a pre-operational near surface soil moisture (SSM) product derived, at 1 km resolution, from Sentinel-1 data over the Mediterranean basin. The high resolution is crucial in this area characterized by small to medium size watersheds (e.g. from 500 km2 to 5000 km2). Indeed, it may allow to resolve SSM patterns related to the landscape characteristics of the watersheds and link them to their hydrologic response.

The objecive of this work is to identify tillage changes at high spatial (e.g. 100m) and temporal (i.e. 6 days) resolution by using S-1 & S-2 data.

The objective of this study is to present the validation status of a pre-operational soil moisture product derived from Sentinel-1 at 1 km resolution and at large scale. The product is developed within the context of an ESA SEOM study (seom.esa.int/page_project034.php).