With the availability of space imaging spectroscopy data over snow and ice-covered areas of the planet, new opportunities for snow parameter retrievals were opened. For example, PRISMA mission does not acquire images as continuously as other optical satellite missions (i.e., Sentinel-2, Landsat-8 etc.) but it allows detailed studies of the cryosphere both at mid-latitudes and at the poles in unprecedented way.In this study, we show the retrieval of snow parameters from remote sensing observations, with particular attention to snow density and snow liquid water content.

In this study, the retrieval of snow Liquid Water Content (LWC, %) from C- and X- band SAR data was based on Artificial Neural Network (ANN) and Random Forest (RF). Two approaches were explored for generating a sufficient amount of data to train and test the ANN and RF algorithms: the first strategy was defined as “model-driven”. The second one was defined as “data-driven”. The validation results showed that RF performs better than ANN in terms of correlation coefficient R, regardless of the selected approach ("model driven" RANN= 0.60, RRF= 0.68; “data-driven” RANN= 0.50, RRF= 0.88 at X-band). Moreover, the RF implementation trained with the “data-driven” approach outperformed the “model-driven” approach in terms of correlation coefficient R (RRF= 0.88 at X-band).

The THERESA (THErmal infRarEd SBG Algorithms) project aims to enhance algorithms for processing Thermal InfraRed data from the SBG-TIR (Surface Biology and Geology – Thermal InfraRed) mission. Starting from state-of-the-art algorithms, THERESA takes in account the mission's technical features to develop algorithms exploiting diverse spectral channels. During the two years lifetime of the project, THERESA will contribute to enhance the investigation of terrestrial phenomena by using both visible and thermal images. The thematic areas that will benefit from SBG-TIR data range from the vegetation analysis to the volcanic eruptions and fires monitoring. Several parameters will be achieved such as the estimation of ash and SO2emissions from volcanoes, the surface temperature, the detection of hotspots as well as the FRP (Fire Radiative Power) in case of HTE’s (High Temperature Events). THERESA's innovation lies in algorithms advancements; the project offers a 360-degree support to the SBG-TIR mission.

The main objective of the project SCIA (Sviluppo di algoritmi per lo studio della Criosfera mediante Immagini PrismA) is the development and optimization of methods for generating products related to the cryosphere. The project foresees the development of a robust processing chain of PRISMA hyperspectral data for the estimation of snow and glacier parameters in Alpine areas, through a combined use of satellite images, field data and radiative transfer models (RTMs). The image spectroscopy measurements provided by PRISMA will make possible to investigate radiometrically complex surfaces and obtain geophysical parameters currently only achievable through airborne hyperspectral sensors.

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.

Forest ecosystems cover about one third of the Earth’s land surface and provide invaluable ecosystem services, but their extension and health is threatened by the effects of climate change. Remote sensing has the potential to map the condition and functioning of global forests, however methodological and technical challenges still hamper the quantitative estimation of forest traits from spaceborne observations. The advent of new generation satellites and more advanced retrieval schemes may provide the chance to overcome these limitations, but the potential of both the data and models still needs to be assessed. In this study, we addressed the retrieval of forest traits from PRISMA hyperspectral spaceborne images collected over a temperate forest in Italy using hybrid retrieval schemes. The results obtained evidenced the potential of PRISMA images and hybrid models for the accurate quantification of Leaf Area Index (LAI) (R2=0.79, nRMSE=12.5%) and Canopy Chlorophyll Content (CCC) (R2=0.74, nRMSE=24.2%) in forest ecosystems. The analysis of the retrievals revealed a sharp decrease of both LAI and CCC from June to early September 2022 due to a severe drought that hit Europe in the summer, providing indication of the usefulness of hyperspectral spaceborne imagery for forest monitoring.

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.

PRISMA, in orbit since March the 22nd 2019, opened the user access in spring 2020. After one year, many hundreds of users have gained the capability to program new acquisitions or download image products from the online archive containing more than 67k datasets, under a quasi-open & free data policy and licensing scheme. During this time frame and despite the COVID-19 related difficulties, PRISMA performed normal operations delivering not only nominal verified quality data to users but establishing collaborations with other space agencies, in order to enable and support synergies with other hyperspectral missions. This paper describes the mission and the results achieved in this first period of full operational usage.

Alpine catchments represent a fundamental reservoir of fresh water at midlatitude. Remote sensing offers the opportunity to estimate snow properties in the optical, thermal and microwave domains. In particular, the possibility to estimate snow density from remote sensing is relevant and still represents a great challenge for the remote sensing scientific community. Since changes of snow density and liquid water content occur continuously in the snowpack, spatial and temporal patterns of optical and thermal data can give information about snowmelt processes. The main goal of this study is to evaluate if snow thermal inertia can be an indicator of snowmelt processes and to evaluate its relationship with snow variables, with particular attention to snow density. This study is a first attempt in exploiting thermal inertia for monitoring snow dynamics, and it may open new perspectives for early detection of snowmelt processes and snow parameters from remote sensing observations.

The contribution presents the development and testing of a fluorescence retrieval scheme based on the ESA's FLuorescence EXplorer mission concept. The algorithm employs on a coupled surface-atmosphere forward model at oxygen absorption bands: i) the atmospheric effect is computed by MODTRAN5; ii) the surface reflectance and fluorescence are modeled by means of the Spectral Fitting approach. The algorithm, previously tested on numerical simulations, was further implemented and optimized to process real observations collected by the FLEX airborne demonstrator HyPlant. The retrieval scheme has been tested on a number of flight lines collected in several locations, different ecosystems types, atmospheric conditions and instrument observation conditions. For the first time, this work shows the capability of retrieving canopy fluorescence from real airborne observations by means of a physically-based algorithm as envisaged for FLEX. The results achieved on the large core data sets of imageries show the consistency of the physical retrieval algorithm for a wide range of scenarios and fluorescence values are in line with ground observations.

Although traditional remote sensing systems based on spectral reflectance can already provide estimates of the “potential” photosynthetic activity of terrestrial vegetation through the quantification of total canopy chlorophyll content or absorbed photosynthetic radiation, the determination of the “actual” photosynthetic activity of terrestrial vegetation requires information about how the absorbed light is used by plants, such as vegetation fluorescence, using very high spectral resolution spectroscopy in the range 650-800 nm. The Fluorescence Explorer (FLEX) mission, selected in November 2015 as the 8th Earth Explorer by the European Space Agency (ESA), carries the FLORIS spectrometer, with a spectral resolution of 0.3 nm and a spatial resolution of 300 m, with a swath of 150 km. The FLEX mission is designed to fly in tandem with the Copernicus Sentinel-3 satellite, in order to provide all the necessary information to disentangle emitted fluorescence from the background reflected radiance, and to allow proper interpretation of the fluorescence spatial and temporal changes in relation to photosynthesis dynamics, accounting for non-photochemical energy dissipation and canopy temperature effects.

Sun-induced chlorophyll fluorescence (Fs) is a weak signal over imposed to the radiance reflected by vegetation. Several algorithms are currently available in literature to retrieve fluorescence from high spectral resolution data. This contribution shows a comparison of different Fs retrieval techniques exploiting: i) ground-based measurements at fine and ultrafine spectral resolution; ii) airborne hyperspectral imagery. Performance analysis of the different methods is done analyzing the coefficient of variation of diurnal measurements collected over a fixed sampled area. Fraunhofer Line Depth (FLD) and Spectral Fitting Methods (SFM) are evaluated using both fine and ultrafine resolution data. Results show that ultrafine resolution data coupled to advanced retrieval techniques, like SFM, strongly reduce errors in Fs. The Fs values calculated from hyperspectral airborne data are in agreement with ground measurements.

Initiated in 1984, the Committee Earth Observing Satellites' Working Group on Calibration and Validation (CEOS WGCV) pursues activities to coordinate, standardize and advance calibration and validation of civilian satellites and their data. One subgroup of CEOS WGCV, Land Product Validation (LPV), was established in 2000 to define standard validation guidelines and protocols and to foster data and information exchange relevant to the validation of land products. Since then, a number of leaf area index (LAI) products have become available to the science community at both global and regional extents. Having multiple global LAI products and multiple, disparate validation activities related to these products presents the opportunity to realize efficiency through international collaboration. So the LPV subgroup established an international LAI intercomparison validation activity. This paper describes the main components of this international validation effort. The paper documents the current participants, their ground LAI measurements and scaling techniques, and the metadata and infrastructure established to share data. The paper concludes by describing plans for sharing both field data and high-resolution LAI products from each site. Many considerations of this global LAI intercomparison can apply to other products, and this paper presents a framework for such collaboration

A program in BASIC is described which allows accurate quantification of some numerical parameters that can be objectively correlated to biological indexes in sigmoid biological events. Attention was focused on the polymerization process of actin (a muscle protein with a mol. wt of 42,000 daltons) studied as the variation in the OD360 index with time. The experimental points, if plotted, can be well approximated by a rational function of the type delta OD360 = f(t), which passes through the origin and can be represented graphically by a sigmoid curve. The program was very helpful in comparing the experimental curves and in analysing significant parameters, such as maximum velocity and asymptote, that characterize these curves and whose interpretation would otherwise be purely subjective.