Temporal changes in soil moisture (SM) and green vegetation affecting the spectral reflectance can heavily reduce the accuracy of topsoil properties estimation from satellite imaging. To minimize these effects on the soil organic carbon (SOC), sand, silt and clay estimations, an external parameter orthogonalization (EPO) model developed using laboratory based measured spectra was tested on PRISMA hyperspectral satellite data. The estimation of soil properties was performed using different machine learning algorithms. The results show that as compared to the uncorrected spectra, removing the effects of both green vegetation and SM (EPOSM+GV) from the reflectance spectra leads to 18%, 13%, 10%, and 24% improvement in the R2for clay, silt, sand and SOC retrieval, respectively. The Gaussian Process Regression (GPR) algorithm provides the best results for all of the soil properties with an RMSE of 9.5%, 14.2%, 6.9% and 0.68% for clay, silt, sand and SOC retrievals, respectively.

The project "EO AFRICA EXPLORERS—PRISMA 4 AFRICA", funded by the ESA, aims at combining products derived from hyperspectral (e.g., PRISMA, EnMAP, DESIS) and thermal data (e.g., ECOSTRESS, Landsat) to detect vegetation anomalies and identify whether they may be related to biotic or abiotic stress factors. In this context, water stress as a main abiotic stress was considered. At this aim we investigated the possibility to exploit PRISMA combined with the ECOSTRESS data to derive evapotranspiration (ET). Even though LST products are available and appear of good quality, the lack of ancillary data prevents ET products to be generated timely. 30-m ET map was produced by combining of ECOSTRESS and PRISMA data and used for water stress estimation. The resulting products proved to be sufficiently accurate to describe the crop water stress at the field scale.

Land cover, or the biophysical cover of the earth's surface, plays an essential role in climate and environmental dynamics. Processes involving land cover change, are among the factors that most threaten the ecosystems sustainability and services. The objective of the work is to explore the potential of the PRISMA multi-temporal hyperspectral imagery in generating new EO products to complement/improve the products provided by Copernicus' Land Monitoring Service for the analysis and monitoring of complex and fragile ecosystems such as the coastal Metaponto (Southern Italy) by estimating of the land biological and economic productivity loss and land degradation vulnerability. Preliminary results showed that an improvement in ecosystem mapping is supported by the use of Artificial Neural Networks (ANN), k-Nearest Neighbors (KNN) and Support Vector Machines (SVM) and a hybrid approach to define the vegetation trait, leads to significant improvement in the damage assessment and land degradation assessment.

Soil salinization and calcification in arid lands facilitate the desertification process, reduce the nutrient availability for plants, and disturb ecological equilibrium. The achievement of the perspectives of spatial variation in electrical conductivity (EC) and calcium carbonate equivalent (CCE) and monitoring based on imaging spectroscopy sensors ensures sustainable and reliable management over large areas. In this study, to evaluate the capability of the PRISMA imagery to predict spatial variations of EC and CCE using machine learning (ML) regression algorithms, 252 soil samples from the Sirjan region in Iran, which is mainly under pistachio cultivation, were used. The highest accuracy for CCE estimation using PRISMA imagery was acquired by the Gaussian Process Regression (GPR) algorithm (R2=0.75, RMSE=4.09), and for EC, it was acquired by the partial least squares regression (PLSR) algorithm (R2=0.64, RMSE=44.8). Moreover, results demonstrate that gypsum abundance is the main confounding parameter impacting the capability of PRISMA for EC estimation.

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.

PRISMA is a hyperspectral pushbroom sensor, launched by the Italian Space Agency in 2019. PRISMA collects the reflected Earth signal from VNIR to the SWIR with 230 spectral bands with a variable FWHM according to the prism dispersion element. This work intends to develop a procedure suitable to monitor the consistency of photon and thermal noise components across a times series of L1 radiance images collected on different Mediterranean scenarios (i.e. rural and coastal). To improve the retrieval of the useful signal and the random noise on PRISMA images the spatial variability of the scenes has been considered in the new version of the HYperspectral Noise Parameters Estimation (HYNPE) algorithm. The procedure, tested on two PRISMA time series, has assessed quite stable and coherent values for the retrieved noise coefficients, not significantly affected by seasonal radiance variations and scene characteristics

The project "Topsoil properties Estimation from Hyperspectral Remote sensing for Agriculture" (TEHRA), funded by the Italian Space Agency (ASI), aims at developing methods and algorithms for the estimation of soil properties of agronomic and environmental interest from PRISMA satellite hyperspectral data, that could support: 1) the adoption of more sustainable and climate-smart farming practices, e.g. through the implementation of precision agriculture applications; 2) monitoring in support of agricultural and environmental policies, e.g. related to climate change and for the encouragement of the adoption of practices preserving soil health.In this paper, some results of the first year of the project are illustrated. They concern: 1) a scenario definition study; 2) studies on the confounding effect of soil moisture and crop residues; 3) exploitation of multi-temporal PRISMA data and 4) data fusion with proximal soil sensing.

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is a new ESA Earth Observation mission which consists in developing a hyperspectral satellite to support EU policies on the management of natural resources, ultimately helping to address the global issue of food security. One of the mission activities is associated to the development of the CHIME-E2E (End-to-End) Performance Simulator that shall be used to evaluate the sensor design and future processing modules provided by the partners by simulating future CHIME images and thematic products. In the frame of this activity, the CHIME Mission Advisory Group (MAG) has identified a collection of five core high priority products (HPP) that includes the retrieval of canopy nitrogen, leaf nitrogen content, leaf mass/area, soil organic carbon content (SOC) and kaolinite abundance. In this paper, we present the first results of applying the L2B prototype processing to hyperspectral airborne and spatial imagery used to simulate realistic CHIME data, to derive soil and mineral maps. The obtained results demonstrate the potential of the next generation of Copernicus missions with high spectral resolution and wide swath imaging satellite for geoscience research and applications.

The unique spectral content provided by PRISMA's hyperspectral sensor gives the possibility to study the Earth's surface and environment from space in unprecedented detail. In this respect, our work presents the results of an Italian Space Agency-funded project aiming to develop eight prototypes for providing Value Added products based on such data. Prototypes focus on vegetation, urban areas, water quality, material detection, and natural hazards, combining multiple state-of-the-art techniques based on Machine Learning, physical models, and index-based algorithms. This is particularly relevant given the increasing demand for accurate information to address sustainable policies and support decision-making processes. Through a series of case studies, we highlight the versatility and utility of PRISMA's hyperspectral data for various scientific and operational applications.

The PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral satellite, launched by the Italian Space Agency (ASI) is presently operational on a global scale. The mission includes the hyperspectral imager PRISMA working in the 400–2500 nm spectral range with 234 bands and a panchromatic (PAN) camera (400–750 nm). In the context of this work, we intend to determine the two noise components (photon and thermal noise) and assess SNR with an image based approach. Results show that the SNR evaluation assessed through the collected images is coherent with the mission requirements and that the PRISMA noise components, derived on the fragmented Pignola test site, in Southern Italy, are comparable to the ones derived on the Rail Road Valley calibration site.

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.

In this research the potential of the PRISMA hyperspectral sensor in comparison with multispectral data (Sentinel-2 MSI and Landsat 8 OLI) was assessed for predicting soil moisture. To this aim, PRISMA, Sentinel-2 and Landsat 8 spectra, resampled according to the spectral bands of each sensor, were simulated from a laboratory soil spectral library. The soil samples used to create the spectral library were collected from different agricultural areas in Central and Southern Italy. Partial Least Square Regression (PLSR), the Normalized Soil Moisture Index (NSMI) and the Soil Moisture Gaussian Model (SMGM) were employed to calibrate soil moisture (SM) estimation models from the resampled spectra. The prediction accuracy of SM estimation was assessed from statistical metrics. The best accuracies in retrieving SM were obtained by PLSR using data resampled at PRISMA spectral resolution. A preliminary test of the application of the calibrated models was carried out using real PRISMA and Sentinel-2 data.

On the 22 March 2019, the Italian Space Agency (ASI) launched the PRISMA satellite, having onboard a hyperspectral imager covering the 400–2500 nm range with 234 spectral bands and about 10 nm of bandwidth. The ground spatial resolution is 30 m, plus a panchromatic camera with 5 m spatial resolution. One of the potential application areas of this scientific mission is for precision agriculture applications, among which the mapping of field-scale variability of topsoil properties is of particular interest. PRISMA clear-sky hyperspectral images were acquired in autumn and spring 2019 over two agricultural areas, Maccarese (Central Italy), and Pignola (Southern Italy). An intensive soil sampling campaign was performed, using a ground sampling scheme adapted to PRISMA spatial 30 and 5 m (PAN) resolutions, in the fields where bare soil was exposed at the satellite acquisition dates. Soil texture (clay, silt, sand) and soil organic carbon (SOC) for the collected soil samples were then determined in the laboratory. The dataset was then used to test calibration and validation of PLS (Partial Least Squares) and Random Forest (RF) regressions, developed using PRISMA surface reflectance data. To this aim, several pre-treatment tests were performed. The results show that good results could be obtained especially for clay estimation.

The Italian Space Agency (ASI) is supporting the calibration/validation (CAL/VAL) activities of the PRISMA hyperspectral mission with the PRISMA Calibration Validation project (PRISCAV). PRISCAV provides, a network of reference test sites to support the PRISMA validation of the L1 and L2 processing chain performance. Among the PRISCAV test sites, representing the different Italian territory, the Pignola test site depicts an agricultural scenario pertaining to the Southern Apennines in the Basilicata Region (Italy). On this site, contemporary to PRISMA acquisitions, a set of ground measurements have been collected from October 2019 to December 2020 to characterize the atmosphere and the ground optical properties and validate the PRISMA radiometry and the L2 reflectance products. Measures are still ongoing on the base of the PRISMA acquisition plan. The comparison of the Modtran simulated radiance with the PRISMA L1 radiance data show the same magnitude and shape. RMSE for the full range of wavelengths vary from 0.000153 to 0.000995 [W/m−2sr−1nm−1]. Further analyses will include the new PRISMA acquisitions and the possible matchups with Sentinel-2, to assure the full exploitation of the PRISMA data for the agricultural monitoring in the Southern Apennines.

The sensitivity of the estimation accuracy of topsoil properties to spatial resolution was explored in the context of the assessment of the forthcoming ESA Copernicus hyperspectral mission CHIME. A methodology was developed to allow a fair comparison across spatial resolution scales of the retrieval accuracy, considering the spatial support size and calibration/validation set size. Under these conditions, the prediction accuracy was found not degraded when decreasing the spatial resolution from 3.6 m (AVIRIS NG) to 20 m (Sentinel 2) or 30 m (CHIME). The best results were obtained for clay estimation, whereas for sand, silt, and soil organic carbon (SOC) the retrieval accuracy was slightly worse, although comparable or even better to what previously reported in the literature. When the spatial correlation between the estimated clay map and the spatialized (block kriging) ground truth map was examined, using the co-dispersion coefficient, the best results were obtained for the 30 m spatial resolution.

In this work, we present the calibration and validation method we have applied in order to retrieve the split window (SW) coefficients for land surface temperature (LST) estimations from Thermal Airborne Spectrographic imager (TASI). For calibration and validation two different datasets has been used, both extracted from SeeBor V5.0 training dataset. The coefficients have been retrieved by a multiple regression analysis and MODTRAN simulations. For the radiative transfer experiment, we considered seven different viewing angles in a range between 0° and 60° with a step of 10°. Simulations have been performed considering all TASI channel combinations and the sensor spectral response functions. Preliminary results are presented for best band combinations suitable for SW algorithm application; these are channel 19 (10.034 gm) with 28 (11.024 gm), and channel 29 (11.134 gm) with 31 (11.354 gm). Finally, validation of the LST retrievals presents a RMSE lower than 0.6 K for both band combinations.

The paper compares the potential of WorldView-3 (WV-3) and Sentinel-2 (S-2) satellite data for mapping naturally occurring asbestos (NOA) outcrops to be used by geologists in the planning phase of environmental monitoring. The wide distribution as well as the variety and extent of asbestos-bearing rocks make the selected area a significant case study for the evaluation of the feasibility of multispectral VNIR-SWIR (0.425-2.330 μm) remote sensing observations for NOA outcrops mapping, in those areas where the density of vegetation allows their spectral identification. Different classification procedures were used to produce NOA outcrops maps for the study area. In our study, we found in general a good agreement (k > 0.8) between the produced NOA outcrops maps and the extensive available in situ data for the accessible locations.

Monitoring and mapping weeds within agricultural crops is required for the implementation of precision agriculture approaches such as patch spraying. A precise and targeted weed control would bring about positive consequences from both environmental and economic perspectives. Given the small spectral differences between crop species, VNIR hyperspectral data can be a powerful tool to perform an effective weed monitoring and identification when high spatial and spectral resolution data is available (i.e. UAV platforms). This work explores the spectral differences between crops and weeds to evaluate the ability of UAV hyperspectral data to separate maize crop from weeds and to discriminate different types of weeds. To this aim, UAV and field hyperspectral data were acquired in some maize fields in Italy during the 2016 growing season. Results showed that by exploiting leaf chlorophyll and carotenoid contents, retrieved using spectral indices or by inverting PROSAIL, is it possible to discriminate between maize crop and weeds and, moreover, among weed types. The procedure allowed the quantification of crop/weeds relative ground cover, which showed a good relationship with the corresponding measured relative LAI values.

The assimilation of biophysical crop canopy variables retrieved from remotely sensed data into two crop models of differing degree of complexity is assessed in this study, in the context of the development of tools suitable for the estimation of yield losses due to drought. The more complex AQUACROP model, developed by FAO and the simpler SAFY model were employed to estimate wheat grain yield for an area in the Shaanxi Province in China through the assimilation of biophysical variables retrieved from Landsat and HJ1A and HJ1B satellites for three growing seasons (2013 to 2015). Results were validated with ground yield data.

The paper describes the preliminary results of the January-August 2015 multi-frequency EO data acquisition campaign conducted over the Maccarese (Central Italy) farm. From January to May radar Cosmo SkyMed Ping-Pong (HH-VV), RapidEye and ZY-3 multispectral VHR optical images, as well as in situ data, have been acquired to retrieve biophysical and/or bio-chemical characteristics of soil and crops. LAI trend has been analyzed and compared by using both polarimetric and optical retrieval algorithms while soil moisture measurements have been compared with the radar backscattering.

The aim of this work is the development of methods for the assimilation of biophysical variables, estimated from multi-source remote sensing data, into crop growth models, in order to estimate the yield losses due to drought both at the farm and at the regional scale. A methodology to obtain maps of leaf area index (LAI), and fractional canopy cover (CC), from HJ1A and HJ1B Chinese satellite optical data was established, using an algorithm based on the training of artificial neural networks (ANN) on PROSAIL model simulations. Retrieved values of biophysical variables, such as LAI or CC, will be assimilated into crop growth models in order to estimate wheat yield. The present work focused on testing two different approaches using a common dataset gathered in Xiaotangshan (China) with two crop models of different complexity, in order to compare the procedures and analyse the responses of the models, before the subsequent application at a regional scale in Yangling, Shaanxi, Central China.

The SAP4PRISMA project research activities aimed at supporting the Italian hyperspectral PRISMA mission by developing preliminary processing chains suitable for PRISMA to obtain high level hyperspectral data products for agriculture, land degradation, natural and human hazards.

The demand from water resources management authorities has progressively increased in the last decade given the direct impacts by human activities, which may cause an irreversible damage to the local natural water balance in coastal and transition regions. The needs of useful tools for monitoring, understanding and managing the water resource are an important responsibility to local authorities for its sustainable use. Moreover, carbonate aquifers constitute a very important thermal water resource outside of volcanic areas, although there is no detailed and reliable global assessment of thermal water resources. An efficient evaluation and mapping of these resources could provide a valuable supply for their management. Within this context the main aim of this research is to define a processing methodology to assess the suitability of the high resolution thermal airborne sensing for improving the water resource management by correctly identifying and monitoring sea surface thermal anomalies in coastal areas due to karstic and thermal water resources.

The main objectives of the PRISMA (Hyperspectral Precursor of the Application Mission) mission are: the implementation of an Earth Observation pre-operative payload, the in-orbit demonstration and qualification of an Italian state-of-the-art hyperspectral/panchromatic technology and the validation of end-to-end data processing system able to support the development of new applications based on high spectral resolution images. The aim of the paper is to provide an overview of the PRISMA mission by describing the current status of the program and giving a brief outline of the work done till now in the framework of the SAP4PRISMA project scientific studies in supporting the exploitation of the future PRISMA hyperspectral images for environmental applications.

In this paper we deal with the integrated use of time-series of SAR and MODIS images to derive the temporal behavior, the abundance and the distribution of the floating macrophytes in the Winam Gulf (Kenyan portion of the Lake Victoria). The proliferation of invasive plants and aquatic weeds is of growing concern. Starting from 1989, Lake Victoria has been interested by the highest infestation of water hyacinth with significant socio-economic impact on riparian populations. The information provided by satellite can play an important role in supporting a decision system for the management of the water resources allowing also an easy and inexpensive way of monitoring the environment response to any action that might be undertaken to contrast its degradation. This paper aims at assessing the capability of medium/high resolution (Wideregion and Stripmap) COSMO-SkyMed ScanSAR time series imagery to support/supplement optical data, frequently affected by clouds, in the knowledge of temporal macrophytes growing cycles and sustain the monitor and management of the Lake Victoria waters.

The SAP4PRISMA is a four year research project which aims at developing algorithms and products for the future PRISMA mission. The project started on May 2010 and is now entering his full activities as the ”PRISMA like” data set has been defined and the test areas were selected. The paper describes the main project objectives and the activities realized in the first 9 months of the project.

Aquatic weeds in lakes can cause different problems both to lake ecology and food webs and interfere with human activities. This drove our interest to exploit recursive satellite imagery to retrieve optical parameters suitable to develop an early warning strategy by mapping the aquatic weeds. This paper aims at assessing the capability of satellite-based remotely sensed imagery to provide information suitable for monitoring and managing the Lake Victoria resources. The spectral data collected during a field campaign, carried out for that purpose, were used to map the floating aquatic vegetation. By analyzing the ¿in situ¿ measurements and time series of satellite data we retrieved the distribution of aquatic weeds from 2004 to 2007 and the seasonal aquatic vegetation growth. These maps, when provided with an appropriate time frequency, can be useful to identify the preconditions for the occurrence of hazard events such as aquatic weeds and to develop an up-to-date decision support system.

The red mud dust risk involves the accumulative contamination of land and dwellings in the community with highly alkaline fine particulate containing heavy metals and other pollutants. This paper demonstrates that hyperspectral airborne remote sensing data can provide an effective, rapid and repeatable tool for mapping and monitoring the spread of red dust providing the location of the polluted areas to be checked. We perform field and laboratory analyses of red mud and soil samples collected in the study area and identify the optical characteristics of the samples to characterize the red mud spectral features. Next, we use hyperspectral airborne data covering an aluminium processing plant in Montenegro (EU). The joint use of MIVIS reflectance and emissivities data allowed us to individuate and map those sites on which the red dust is spread by the dominant winds, where a check for reclamation or a neutralization intervention is required.

An experimental method to select the number of principal components in minimum noise fraction (MNF) is proposed to process images measured by imagery sensors onboard aircraft or satellites. The method is based on an experimental measurement by spectrometers in dark conditions from which noise structure can be estimated. To represent typical land conditions and atmospheric variability, a significative data set of synthetic noise-free images based on real Multispectral Infrared and Visible Imaging Spectrometer images is built. To this purpose, a subset of spectra is selected within some public libraries that well represent the simulated images. By coupling these synthetic images and estimated noise, the optimal number of components in MNF can be obtained. In order to have an objective (fully data driven) procedure, some criteria are proposed, and the results are validated to estimate the number of components without relying on ancillary data. The whole procedure is made computationally feasible by some simplifications that are introduced. A comparison with a state-of-the-art algorithm for estimating the optimal number of components is also made.