In this paper the potential of integrating optical and microwave data to monitoring vegetation features has been exploited by using experimental data and models. The general idea was to cope the high sensitivity of radar data to water content of vegetation with the high sensitivity of optical data to pigments, thus producing more in-depth information on vegetation status. Two sorghum fields located close to Florence was taken under observation during summers 2022 and 2023, by gathering soil and vegetation parameters and collecting Sentinel-1 and Sentinel-2 images. Backscattering coefficient and some optical indices have been experimentally related to soil and vegetation water content and plant water status. The use of a simple e.m. model allowed estimating the plant water content in the canopy. The obtained results confirmed the validity of the followed approach, although further investigation is needed.

This study aims at improving the accuracy of the Strong Fluctuation Theory (SFT) in simulating the backscattering from Alpine snowpacks, by simulating the roughness effect of the snow-soil interface through suitable models, as the Oh model and the Advanced Integral Equation Model (AIEM). As conceived in the original form indeed, SFT considers the air-snow and snow-soil interfaces as flat surfaces: such approximation can lead to inaccurate results under some observed conditions. The reappraised SFT was validated against Dense Media Radiative Transfer (DMRT) model simulations and experimental data available from Sentinel-1 (S-1) C-band and COSMO-SkyMed (CSK) X-band SAR in two alpine test sites located in the Northern Italy. The inclusion of rough soil contribution was found effective in improving significantly the SFT simulation in dry and wet snow conditions, with a significant improvement of correlation with SAR data: as an example, R2increased from 0.05 to 0.57 in the comparison with CSK. The comparison with DMRT pointed out a very good agreement between the two models, (R2=0.88 at C-band and 0.91 at X-band) with the not negligible advantage of an extremely reduced computational cost of the reappraised SFT with respect to DMRT.

In this study, a method for mapping the crop biomass, expressed as Plant Water Content (PWC in kg/m2), at high resolution is proposed. The method is based on SAR data at C and X bands and machine learning algorithms, and it is composed of some steps, including crop classification, soil moisture (SMC) retrieval and finally PWC retrieval. It has been developed and validated in an agricultural area located in Tuscany (Central Italy), for which timeseries of Sentinel-1 and COSMO-SkyMed images were available, along with in situ measurements of the main soil and vegetation parameters.The retrieval, so far limited to the wheat crops, resulted in correlation coefficient R=0.92 and RMSE=0.5 (kg/m2) between estimated and target PWC, by confirming the feasibility of using SAR for monitoring vegetation biomass at high resolution.

This paper deals with the analysis of the backscattering sensitivity at C and X bands to the agricultural crop characteristics and the implementation of a method for estimating crop biomass. The study areas were located in Tuscany (Central Italy) close to Florence. Series of Sentinel-1 and COSMO-SkyMed images have been collected for several years. An accurate crop classification method was first realized in order to separate crops characterized by different scattering behaviors, namely broad- and narrow-leaf crops. The backscattering trends have been simulated by using electromagnetic models based on radiative transfer theory. Algorithms based on Neural Network approaches have been implemented for estimating the crop biomass by using multi-frequency and multi-polarization SAR data at C and Xband.

The research results described in this paper have been obtained in the framework of the 2019–2022 ALGORITMI project between the Italian Space Agency (ASI) and the Institute of Applied Physics of the National Research Council (CNR-IFAC). The focus of the research was the development of innovative algorithms for the estimation of geophysical parameters of soil, snow, and vegetation with the aim of monitoring soil, snow cover and agricultural crop conditions. The estimation of soil moisture, vegetation biomass, snow water equivalent, and crop classification was improved by using retrieval algorithms based on machine- learning approaches and temporal series of SAR images from COSMO-SkyMed (CSK) and Sentinel-1 (S-1) missions, along with optical images from Sentinel-2. This paper provides an overview of the most recent and valuable results obtained during the project. In particular, the validation of soil moisture provided R=0.89 and RMSE=0.025 m3/m3by integrating data from S-1 and CSK and that one of snow water equivalent gave R=0.85 with RMSE=86.24 mm (CSK HIMAGE) and R=0.86 with RMSE=71.59 mm (CSK PP). Early mapping results showed an almost monotonic progression in overall accuracy over time higher than 90% by increasing the available images.

In this paper, an investigation on the agricultural management in semi-arid Mediterranean regions is presented. The selected test areas are located in Tunisia, near the Kairouan town. The agricultural fields are mainly cultivated with olive trees together with cereals, fruit trees and vegetables. The possibility to monitor this area by means of COSMO-SkyMed (CSK) data, thanks to the ASI Open Call initiative, is an added value to retrieve information concerning the temporal evolution of crop conditions and the use of water in semi-arid regions. The CSK images have been acquired in the period 2018–2019 and the spring 2021. The objectives of this research concern the use of CSK data to evaluate the correct growth of agricultural crop. The preliminary analysis shows that X -band backscatter is able to follow the seasonal moisture conditions and to identify different types of crops.

In this study, a disaggregation technique based on machine learning is proposed. The technique combines Sentinel 1 and AMSR2 data with the aim of enhancing the spatial resolution of the vegetation biomass, expressed herein as Plant Water Content (PWC), and Soil Moisture (SM) products generated from AMSR2 by the HydroAlgo algorithm developed at IFAC. Validation is still in progress; however, the results obtained so far demonstrated the effectiveness of the proposed disaggregation in mapping both PWC and SM at 100m resolution, thus overcoming the problem of coarse spatial resolution that hampers the potential of satellite microwave radiometers as the AMSR2 for operational applications in small scale basins.

Active microwave remote sensing data at different frequencies can provide crucial information on crop morphology and conditions, thus effectively supporting agronomic management at different scales. Despite the ever-increasing availability of spaceborne platforms and the extensive research developed throughout more than two decades, some knowledge gaps still await to be filled toward operational use, dealing with SAR backscatter response to crop-specific features and seasonal dynamics, including the effects of agronomic practices. In this work, we used variance-based global sensitivity analysis (GSA) as a quantitative framework for investigating the sensitivity of X-band backscattering to agronomic and morphological features typical of two different crops maize and rice. To this end, we jointly exploited empirical data on crop status and growth, high-resolution TerraSAR-X (TSX) data, and microwave radiative transfer model (RTM) simulations. Phenology-informed simulations allowed us to quantify the contributions of different scattering mechanisms for the two crops under varying observation setups, to assess the sensitivity of X-band backscattering to morphostructural crop biophysical parameters (BPs) (and their interactions), and to evaluate the effects of crop biomass on backscatter across growth stages. In particular, multidimensional GSA outputs accounting for model input correlations through Shapley effects provided a comprehensive suite of information on the relative proportion of total backscatter variance explained by a range of parameters and a quantitative description of the different behavior in vertical and horizontal polarization (changing throughout plant growth) in paddy rice, and the mixed contribution of canopy density and leaf angle distribution (depending on the incident angle) in maize.

In this paper, an algorithm based on Convolutional Neural Networks (CNNs) was developed to correctly classify an agricultural area in central Italy, by using SAR images. This preliminary step is vital for mastering the different influence of crop types in SAR data before the implementation of algorithms devoted to estimate of vegetation biomass. In situ data collected on the test site were used for validating the CNN algorithm-based classification. After the agricultural species recognition, a sensitivity analysis between C-band Sentinel-1 and X-band COSMO-SkyMed backscatter coefficients and crop biomass was carried out, laying the foundation for the implementation of algorithms able to estimate the biomass of different crop types.

The possibility of improving the spatial resolution of Soil Moisture (SM) mapping from microwave satellite radiometers is extremely interesting for hydrological studies in small catchments as well as applications to precision farming. In this study, an algorithm based on Artificial Neural Networks (ANN) is proposed, with the aim of improving significantly the spatial resolution of the Soil Moisture Active Passive (SMAP) Enhanced 9 km Soil Moisture (SMC) product, by integrating SMAP and Sentinel 1 (S1) data. The ANN is trained with data at 9 km resolution, obtained by combining the Sentinel-1 data downsampled to the SMAP resolution and the corresponding SMAP SMC product. After training the ANN is applied pixel by pixel to the Sentinel-1 images at full resolution for generating the enhanced SMC maps. The method has been tested in an agricultural area located in Central Italy, for which in-situ SMC measurements were available: the Active/Passive synergy resulted in an appreciable improvement of both retrieval accuracy and spatial resolution.

Modern agriculture is facing new challenges about food production for a growing population in a sustainable manner. Crop mapping at local and regional scale could provide valuable information in support of agricultural policy. This paper describes a field mapping investigation in a populated area in Tuscany (Italy). Satellite images from Sentinel-1 C-band and COSMO-SkyMed X-band SAR and Sentinel-2 optical sensors are input of classifiers based on deep learning and convolutional neural networks. Results pinpointed that the use of optical images allowed the best overall classification accuracy (99.7%), nevertheless X-band SAR imagery, providing an accuracy of 94.6%, could be a good substitute of optical indices in case of lack of cloud-free multispectral data.

The estimate of forest biomass on a global scale is of great relevance for many purposes related to the carbon cycle and the climate change.In this research work, the capability of GNSS sensors for evaluating forest biomass has been investigated by using data coming from two satellite sensors, i.e. TechDemoSat-1 (TDS-1) mission of Surrey Satellite Technology Ltd. and the NASA’s Cyclone GNSS (CyGNSS).Two reflectivity parameters were identified and compared to global forest biomass values obtained through ALOS2 and SMAP VOD. The sensitivity analysis provided interesting results with correlation coefficients (R) > 0.65, thus allowing the implementation of a retrieval algorithm based on a Neural Network approach. The results have been encouraging, showing R>0.8 and RMSE<0.2 on the area of Manaus.

This study aims at merging SMAP and Sentinel-1 (S-1) data for obtaining a surface soil moisture (SMC) product improved in accuracy, spatial and temporal resolution that can be used for hydrological modelling in small basins. A method based on Artificial Neural Networks has been developed and validated in a test area in central Italy. All the S-1 images available on the area between 2014 and 2017 have been considered for the analysis, along with the corresponding SMAP acquisitions. Distributed SMC values, to be used as reference for implementing and validating the algorithm, have been derived from the available in-situ data by using the well-assessed Soil Water Balance hydrological model (SWBM). The research is still ongoing; however, some preliminary results show that the merged ANN SMC product is improved in resolution and accuracy with respect to the SMC obtainable from a single sensor.The ANN SMC was successfully assimilated in the MISC hydrological model for improving the model predictions in small and medium basins.

This paper describes a rice mapping and growth monitoring project carried out using both optical and SAR data on an agricultural land area in northern Italy. The approach implemented for mapping rice area is based on synthetic features derived from both the optical and SAR C-band multi-temporal dataset and a rule-based algorithm applied on a pixel basis. SAR X-band data were used improving winter crops recognition. Seasonal dynamics were used to identify rice growing season for regression analysis between SAR backscatter and vegetation parameters. Rice green LAI maps have been produced using the equation found during this analysis between C-band HH pol. backscatter and rice LAI.

This paper describes a mapping project carried out using both optical and SAR data on an agricultural area in northern Italy where the main crops are corn, rice and wheat. Temporal trends of backscatter and reflectance, given by the variations in vegetation growth, soil conditions and agricultural practices were analyzed and interpreted thanks to the ground-measured data. Information extracted from both optical and SAR data (vegetation indices, backscatter and texture features) were used to create training sets for implementing three different classification approaches. The work aimed at comparing early crop maps with maps derived at the end of the season. Results show that the classification accuracy obtained using only multispectral optical data is higher than the one reached using only SAR as input. Integrating both optical and SAR multitemporal features provides some advantages in terms of a more reliable crop map, especially during an early temporal stage scenario. Among the supervised algorithms tested, Maximum Likelihood shows the best overall accuracy performances at each thematic level, time step and using both optical and SAR input data.

In preparation for the CoReH2O satellite mission, one of the three missions selected for scientific and technical feasibility studies within the Earth Explorer Programme of the ESA, experimental and theoretical studies have been under way in order to improve methods for the retrieval of snow physical properties from SAR data. The aim of this paper is to investigate the impact of vegetation in the retrieval of snow parameters from microwave backscattering measurements. A RTT model capable of simulating scattering from a snow-covered vegetated terrain was developed and implemented. A sensitivity analysis to snow and vegetation parameters was carried out thus a comparison with real SAR data is presented in the paper.

In this paper an experiment concerning the capabilities of GNSS-R sensors for land applications was described. An airborne campaign was performed in summer and fall 2011 over two areas close to Florence (Italy): an agricultural zone and a forest plot of poplars. A detailed comparison of the GNSS-R signals with ground truth data was performed. Both LR and RR reflection coefficients have been found to be sensitive to changes in the surface soil moisture, with a total variation of about 6 dB between dry and wet conditions. Regarding the sensitivity to vegetation, it was observed that the measured LR coefficients have a moderate power variation due to the presence of woody vegetation. It was observed that the LR coefficient experienced a monotonic decrease with increasing biomass, up to an estimated forest dry biomass of more than 150 t/ha.

The capability of COSMO-SkyMed in estimating vegetation biomass has been investigated in this paper. SAR data from COSMO-SkyMed were collected on two agricultural areas in Italy in 2010 at different dates during the vegetation cycle. The performances of X-band data have been compared with accurate ground truth measurements of soil and vegetation carried out simultaneously to satellite passes. Experimental data have been compared with model simulations obtained with a discrete element radiative transfer model. Moreover, an inversion algorithm, based on an Artificial Neural Network and trained by using AIEM and the radiative transfer model, has been applied to retrieve the plant water content of wheat and sunflower crops and to generate the corresponding plant water content maps.

This paper describes an algorithm for retrieving the snow depth from the data acquired by the microwave radiometers operating from space. The algorithm is based on Artificial Neural Network techniques and has been developed and tested using a large dataset of AMSR-E acquisitions and corresponding direct measurements of snow depth and air temperature collected over Siberia within the framework of the GCOM/AMSR2 mission. The algorithm has been subsequently applied to the AMSR-E acquisitions collected during the winter seasons between 2002 and 2011 on Alpine regions, setting up a procedure for evaluating and correcting the effects of the orography and the forest coverage.

The results of an experiment carried out in Italy for exploiting the capabilities of X-band SAR in the monitoring of soil and vegetation characteristics are summarized in this paper. Data from X-band Cosmo-Skymed mission have been collected in two agricultural areas and compared with C-band data of ENVISAT/ASAR and with ground truth measurements. In general, a certain sensitivity to vegetation biomass and to moisture of bare soils has been found.

In preparation of the satellite mission CoReH2O, one of the three missions selected for scientific and technical feasibility studies within the Earth Explorer Programme of the European Space Agency, experimental and theoretical studies started in order to investigate backscatter properties of snow covered terrain and improve the methods for retrieval of snow physical properties from SAR data. The aim of this paper is to investigate the impact of vegetation in the retrieval of snow parameters from backscattering measurements. First a radiative transfer model, able to simulating scattering from a vegetated snow-covered terrain was developed and implemented. Lastly, a sensitivity analysis on snow and vegetation parameters was conducted for coniferous forest. Results confirm that with increasing biomass the sensitivity to SWE strongly decreases. Moreover when biomass is in the 0-150 m3/ha range a procedure to correct the vegetation effect in the SWE retrieval algorithm is suggested.