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 research aims to exploit the potentialities of multi-mission SAR data at X-, C- and L-band for the monitoring of snowpack and alpine soils. The snow parameters as snow water equivalent, snow liquid water content and snow metamorphism have been monitored and different methods are proposed for their retrieval. In order to gather consistent datasets, experimental activities have been conducted in two selected sites in Northern Italy, which are covered by alpine snow during winter and spring periods and are in some cases characterized by the presence of permafrost. Microwave responses of snow and soil have been then simulated by using electromagnetic (i.e., AIEM, Oh, SFT and DMRT-QCA), and physical models (SNOWPACK). Finally, machine learning approaches, as Artificial Neural Networks and Random Forest, were implemented for retrieving snow parameters; whereas interferometric techniques were used in case of snow and soil displacement as rock glaciers. Preliminary and consistent results have been obtained in terms of estimate of snow parameters and soil displacement. This multi-frequency/multi-mission approach enhances the ability of SAR sensors to monitor and analyze snow dynamics, contributing to improved decision-making in various domains.

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.

In this paper the integration between the Dense Media Radiative Transfer (DMRT-QMS) model and the SNOWPACK model was investigated in order to simulate snow parameters and the backscattering at X band (9.6 GHz) from nivo-meteorological data. The role of the stickiness parameter ($\tau$) in DMRT-QMS was analyzed by using experimental data of backscattering collected from COSMO-SkyMed (CSK) and snow data generated by SNOWPACK. The relationships between$\tau$and both ice volume fraction ($\phi$) and coordination number ($n_{c}$) were assessed. The DMRT and SNOWPACK simulations were compared with CSK backscattering measurements showing a significant agreement, although for a limited dataset.

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.

In this letter, hypersharpening is analyzed in depth by investigating some weaknesses present in its formulation. It is shown that the key formula of the synthesized band variant can be simplified under certain circumstances. In addition, a novel fusion schema is proposed. As a result, the gain factor adopted to weight the injected detail is computed in a different way. This schema can be applied to fuse a wide range of hyperspectral and multispectral data. In this letter, its effectiveness is demonstrated by taking into account the characteristics of WorldView-3 data.

In this letter, we propose a simple yet robust procedure to combat the residual local misalignment between the image data sets that are usually processed for pansharpening: a higher resolution panchromatic (Pan) image and a series of lower resolution multispectral (MS) bands, preliminarily interpolated to the pixel size of Pan. Unlike a conventional coregistration, which requires a preliminary orthorectification enforced by an accurate digital surface model, the proposed method automatically exploits the characteristics of the Pan image to alleviate the effects of misalignment on fusion products, whichever is the method chosen for pansharpening. More specifically, the space-varying residue of the multivariate regression between resampled MS bands and low-pass-filtered Pan image, which locally measures the extent of MS-to-Pan misalignments, is injected into the MS bands after being weighted by the projection coefficients of each band. Tests on simulated Pléiades images demonstrate that global shifts up to five pixels along each direction are perfectly compensated. Tests on a true GeoEye-1 image, whose shifts are space-varying and of unknown extent, highlight the attained improvement in spatial alignment.

This letter discusses whether the expanded multispectral image, i.e., the original multispectral image upsampled to the panchromatic scale, can be used during the assessment of the quality of pansharpened multispectral images. By considering Wald's protocol, the authors demonstrate that the adoption of the expanded image as the reference is erroneous and brings a quality assessment of the fused images that is misleading. In addition, some recommendations about the valid role of the expanded image are provided. The discussion is supported by a quantitative analysis and visual comparisons.

In this work, the authors investigate the behaviors of the two main classes of pansharpening methods: those based on component substitution (CS) or spectral methods and those based on multiresolution analysis (MRA) or spatial methods, in the presence of temporal and/or instrumental misalignments between the multispectral (MS) and panchromatic (Pan) data sets, that is, whenever MS and Pan are not jointly acquired at the same time and/or from the same platform. Starting from the mathematical formulation of CS and MRA pansharpening and from the spectral model between the Pan and MS channels, estimated through the multivariate linear regression between MS and spatially degraded Pan, it is proven that both CS and MRA methods may lose geometric sharpness in the case of a spectral mismatch, but spatial methods preserve the spectral diversity of the original MS data set regardless of the date or instrument of Pan image acquisition. Conversely, spectral methods also suffer from a loss of spectral fidelity to the original MS data set that is inversely related to the success of the spectral match between MS and Pan, measured by the coefficient of determination of the multivariate regression. An experimental setup exploiting GeoEye-1 and QuickBird data sets demonstrates the validity and intrinsic limitations of the proposed theoretical models. Depending of the target application, one class of methods may be preferred to another: Whenever spectral fidelity of pansharpened products to the original MS data sets is crucial, spectral methods should be avoided, and spatial methods are to be preferred.

Pansharpening techniques aim at improving the spatial resolution of a multispectral data set (MS) by using a panchromatic image (Pan) acquired on the same scene with a greater spatial resolution and consequently a lower ground sample distance (GSD). Usually, a quantitative assessment of the fused products cannot be directly performed because of the lack of a reference MS data set with the same GSD of the Pan. A well-known solution is Wald's protocol: The original MS and Pan are spatially degraded, the reducing factor being the ratio between their GSDs. Pansharpening is then performed between the reduced MS and Pan data sets, and the fused products are compared with the original MS, which can be used as reference. In this protocol, fusion performances are assumed to be independent of the scale so that the results at the reduced scale are an estimation of those at the original resolution. This hypothesis can be more or less reliable, depending on the sensor and/or the scene content. The objective of this paper is to propose a new methodology to infer the unknown performances of a pansharpening method at full scale. For this purpose, multiple sets of fused images are computed at degraded scales by downsampling Pan and MS data sets by means of the sensor modulation transfer function. Multiscale quality/distortion measurements are fitted by linear and quadratic polynomials in order to extrapolate their full-scale values. Once the proposed protocol has been assessed in the presence of reference originals, the obtained results are extended to the case where the reference image is not available.

The Multitemporal Ts-VI (MTVI) method has been proposed as a modified extension of the well known Temperature-Vegetation Dryness Index (TVDI), taking advantage of the self-consistency of TVDI but at the same time trying to overcome its greatest constraint for better characterizing and monitoring surface soil moisture conditions over large and heterogeneous areas and so extending the operational applicability of the Ts-VI relationship. In order to test the performances of the MTVI method, a multitemporal sequence of Landsat TM-ETM+ images, acquired over a wide area in Umbria region (Italy), were selected and processed. The derived MTVI soil moisture maps reveal a better agreement with ground measurements recorded at rain gauge stations, both for a long- (the same month in different years) and a short-term (the same season in different dates) sequence of satellite imagery, than those obtained by the application of the standard Ts-VI method.

Pan-sharpening techniques improve the spatial resolution of a Multispectral image (MS) by using a Panchromatic image (PAN) of the same scene contemporaneously acquired at higher resolution. Usually, a quantitative assessment of the resulting fused MS image cannot be directly performed because of the lack of a reference MS. Wald's protocol offers a possible solution: original Pan and MS are spatially degraded, the reducing factor being the ratio between their resolutions. Pan-sharpening is then performed between the reduced MS and PAN images. The quality of the fused products is then evaluated by comparing them with the original MS used as reference, by assuming the hypothesis that the performances of the pan-sharpening methods are independent from scale. The objective of this work is to propose a methodology to verify this hypothesis. For this aim, pan-sharpening performances when varying spatial resolution are investigated and a viable strategy to devise pansharpening performances at full scale is suggested.

This paper presents a novel scheme for lossless/near-lossless hyperspectral image compression, that exploits a classified spectral prediction. MMSE spectral predictors are calculated for small spatial blocks of each band and are classified (clustered) to yield a user-defined number of prototype predictors for each wavelength, capable of matching the spatial features of different classes of pixel spectra. Unlike most of the literature, the proposed method employs a purely spectral prediction, that is suitable for compressing the data in band-interleaved-by-line (BIL) format, as they are available at the output of the on-board spectrometer. In that case, the training phase, i.e., clustering of predictors for each wavelength, may be moved off-line. Thus, prediction will be slightly less fitting, but the overhead of predictors calculated on-line is saved. Although prediction is purely spectral, hence ID, spatial correlation is removed by the training phase of predictors, aimed at finding statistically homogeneous spatial classes matching the set of prototype spectral predictors. Experimental results on AVIRIS data show improvements over the most advanced methods in the literature, with a computational complexity far lower than that of analogous methods by other authors.

Goal of this work is to investigate lossy compression methodologies from the viewpoint of spectral distortion introduced in hyperspectral pixel vectors, besides that of radiometric distortion. The main result of this analysis is that, for a given compression ratio, near-lossless methods, i.e., with constrained pixel error, either absolute or relative, are more suitable for preserving the spectral discrimination capability among pixel vectors, which is perhaps the main source of spectral information. Therefore, whenever a lossless compression is not practicable, near-lossless compression is recommended in such applications where spectral quality is crucial.

This work reports about an original application concerning lossy compression of multispectral (XS) and panchromatic (Pan) images collected by spaceborne platforms. Generally, the former is a set of three or four narrow-band spectral images, while the latter is a single broadband observation imaged in the visible and near-infrared wavelengths. Since high resolution spectral observations having high SNR are difficult to obtain, and especially to transmit, the Pan image, having resolution typically four times that of XS, but slightly lower SNR, is added to the XS data and used with the main purpose of expediting both visual and automatic identification tasks, possibly through an integration (merge) with the lower resolution XS data. Whenever XS data at the same resolution of the Pan data and with adequate SNR were hypothetically available on board, the bottleneck of downlink to receiving stations would impose severe limitations in the bit rate, so that a lossy compression would be mandatory. The consequence of the loss of information is a distortion, both radiometric and especially spectral, which may be easily quantified.

An insufficient sampling rate can cause the introduction of unwanted effects in the samples of the image: the aliasing phenomenon. The aim of this paper is to investigate the possibility to mitigate the aliasing effects, trying to respect as much as possible the Shannon criteria for the image sampling. A model for the system modulation transfer function (MTF) of a hyperspectral push-broom sensor is developed. Quantitative quality indexes are adopted in order to assess the aliasing effects on the images. By using the MTF and the quality indexes, the relationship between aliasing and parameters of the optical system or of the electronic apparatus will be investigated. An alternative and more effective solution could be to try to respect as much as possible the Shannon criteria. Recent improvements in CCD technology make hexagonal sampling feasible for practical applications and bring a new interest on this topic. In our work, the advantages of hexagonal sampling with respect to conventional rectangular sampling are analysed under general assumptions. Nevertheless, using a hexagonal detector has an impact on the detection and imaging chain. In particular, processing steps must take into account the improved sampling geometry. This leads to adaptation of existing algorithms and even new processing architectures.

The impact of a regularization of motion vectors (MVs) on the performance of a block-DCT based video coder (H.263) is addressed. Postprocessing is accomplished by exploiting both the spatial correlation of the vector field and the confidence of the estimated block vectors. A previously proposed adaptive scheme for MV smoothing, based on the theory of vector median filters, is adjusted and embedded into an H.263 coder. With a bit stream that is perfectly H.263-compatible, results are improved, especially for very low bit rates and complex motion of the scene.