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.

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.

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%.

In this paper the results of an additional campaign in the context of Flashing Fields are presented showing the impact of soil surface roughness on the directional backscattering. For the characterization of soil surface roughness a photogrametric measurement device was used and roughness was measured simultaneous to SAR observation made by ERS-2 and TerraSAR-X. As this study mainly confirmed and consolidated the findings of the previous Flashing Fields studies, major progress was made in in the understanding of the impact of soil surface roughness on the flashing phenomenon. It could be concluded, that besides the row orientation, certain roughness conditions could significantly alter the flashing effect. © 2012 IEEE.

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.

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.

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.

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.