Correcting atmospheric effects on hyperspectral optical satellite scenes is paramount to ensuring the accuracy of derived bio-geophysical products. The open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was first initiated in 2016 and has now been extended to provide a comprehensive assessment of atmospheric processors of space-borne imaging spectroscopy missions (EnMAP and PRISMA) over land surfaces. The exercise contains 90 scenes, covering stations of the Aerosol Robotic Network (AERONET) for assessing aerosol optical depth (AOD) and water vapour (WV) retrievals, as well as stationary networks (RadCalNet and HYPERNETS) and ad hoc campaigns for surface reflectance (SR) validation. AOD, WV, and SR retrievals were assessed using accuracy, precision, and uncertainty metrics. For AOD retrieval, processors showed a range of uncertainties, with half showing overall uncertainties of <0.1 but going up to uncertainties of almost 0.4. WV retrievals showed consistent offsets for almost all processors, with uncertainty values between 0.171 and 0.875 g/cm2. Average uncertainties for SR retrievals depend on wavelength, processor, and sensor (uncertainties are slightly higher for PRISMA), showing average values between 0.02 and 0.04. Although results are biased towards a limited selection of ground measurements over arid regions with low AOD, this study shows a detailed analysis of similarities and differences of seven processors. This work provides critical insights for understanding the current capabilities and limitations of atmospheric correction algorithms for imaging spectroscopy, offering both a foundation for future improvements and a first practical guide to support users in selecting the most suitable processor for their application needs.

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.

Despite its high importance for crop yield prediction and monitoring, early-season crop mapping is severely hampered by the absence of timely ground truth. To cope with this issue, this study aims at evaluating the capability of PRISMA hyperspectral satellite images compared with Sentinel-2 multispectral imagery to produce early- and in-season crop maps using consolidated machine and deep learning algorithms. Results show that the accuracy of crop type classification using Sentinel-2 images is meaningfully poor compared with PRISMA (14% in overall accuracy (OA)). The 1D-CNN algorithm, with 89%, 91%, and 92% OA for winter, summer, and perennial cultivations, respectively, shows for the PRISMA images the highest accuracy in the in-season crop mapping and the fastest algorithm that achieves acceptable accuracy (OA 80%) for the winter, summer, and perennial cultivations early-season mapping using PRISMA images. Moreover, the 1D-CNN algorithm shows a limited reduction (6%) in performance, appearing to be the best algorithm for crop mapping within operational use in cross-farm applications. Machine/deep learning classification algorithms applied on the test fields cross-scene demonstrate that PRISMA hyperspectral time series images can provide good results for early- and in-season crop mapping.

In March 2019, the PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral satellite was launched by the Italian Space Agency (ASI), and it is currently operational on a global basis. The mission includes the hyperspectral imager PRISMA working in the 400–2500 nm spectral range with 237 bands and a panchromatic (PAN) camera (400–750 nm). This paper presents an evaluation of the PRISMA top-of-atmosphere (TOA) L1 products using different in situ measurements acquired over a fragmented rural area in Southern Italy (Pignola) between October 2019 and July 2021. L1 radiance values were compared with the TOA radiances simulated with a radiative transfer code configured using measurements of the atmospheric profile and the surface spectral characteristics. The L2 reflectance products were also compared with the data obtained by using the ImACor code atmospheric correction tool. A preliminary assessment to identify PRISMA noise characteristics was also conducted. The results showed that: (i) the PRISMA performance, as measured at the Pignola site over different seasons, is characterized by relative mean absolute differences (RMAD) of about 5–7% up to 1800 nm, while a decrease in accuracy was observed in the SWIR; (ii) a coherent noise could be observed in all the analyzed images below the 630th scan line, with a frequency of about 0.3–0.4 cycles/pixel; (iii) the most recent version of the standard reflectance L2 product (i.e., Version 2.05) matched well the reflectance values obtained by using the ImACor atmospheric correction tool. All these preliminary results confirm that PRISMA imagery is suitable for an accurate retrieval of the bio-geochemical variables pertaining to a complex fragmented ecosystem such as that of the Southern Apennines. Further studies are needed to confirm and monitor PRISMA data performance on different land-cover areas and on the Radiometric Calibration Network (RadCalNet) targets.

Over the past decades, remote sensing satellite sensors have significantly increased their performance and, at the same time, differed in their characteristics. Therefore, making the data repeatable over time and uniform with respect to different platforms has become one of the most challenging issues to obtain a representation of the intrinsic characteristics of the observed target. In this context, atmospheric correction has the role of cleaning the signal from unwanted contributions and moving from the sensor radiance to a quantity more closely related to the intrinsic properties of the target, such as ground reflectance. To this end, atmospheric correction procedures must consider a number of factors, closely related to the specific scene acquired and to the characteristics of the sensor. In mountainous environments, atmospheric correction must include a topographic correction level to compensate for the topographic effects that heavily affect the remote signal. In this paper, we want to estimate the impact of topographic correction on remote sensing images based on a statistical analysis, using data acquired under different illumination conditions with different sensors. We also want to show the benefits of introducing this level of correction in second level products such as PRISMA L2C reflectance, which currently do not implement it.

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 −2 sr −1 nm −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 authors wish to make the following corrections to this paper [...]

In the framework of the INAL/BRIC research contract #ID57 (2016) different remote sensing technologies, from proximal to remote (from airborne to satellite), and processing classification technique have been exploited to detect both manmade materials containing asbestos and natural occurring asbestos (NOA) formations. Asbestos minerals show characteristic spectral features in the LWIR spectral regions centered at about 9.6 µm. The VNIR-SWIR spectral region was well explored by multi and hyperspectral airborne, while the LWIR spectral range, at present, is still less explored for the detection and identification of the NOA. The LWIR range should have a high potential as asbestos minerals absorption feature are far from the ones of the other minerals commonly associated with them (e.g., carbonates). The area surveyed by the multispectral LWIR airborne TASI-600 corresponds to a peridotitic ophiolite of great thickness and extension referring to the ophiolitic complex (i.e. including Roccamurata) along the banks of the Taro river [1], [3]. The ultramafic rock outcrops occurring in the Taro Valley (Italy), belong to the External Ligurid Units of the Northern Apennines within Cretaceous-Eocene sedimentary formation [2]. These ultramafic rocks formations include natural asbestos minerals that have a high potential hazard to human health if inhaled [3]. The airborne survey has been carried out using the airborne hyperspectral TASI-600 sensor acquiring 32 spectral bands in the 8.0 - 11.5 µm spectral range with a spectral resolution of 100 nm. The airborne survey was performed on a test area NW to the Borgo Val di Taro town along the Taro Valley for about 50 km2 at an altitude of about 1000 m a.s.l.. The survey covers two quarries of massive ophiolites (almost serpentine) on which samples have been collected in view of a further spectral and chemical analysis. This communication will present the preliminary results of multispectral LWIR TASI survey performed on the Roccamurata study area in terms of: (i) radiometric and geometric correction; (ii) LST, by using a split window technique, and emissivity calculation by using a TES algorithm (iii) a preliminary result of the serpentine mapping compared with the available 2016 geological map (http://www.isprambiente.gov.it/Media/carg/note_illustrative/216_Borgo_Val_di_Taro.pdf). * [1] Boschetti, T., & Toscani, L. (2008). Springs and streams of the Taro–Ceno Valleys (Northern Apennine, Italy): reaction path modeling of waters interacting with serpentinized ultramafic rocks. Chemical Geology, 257(1-2), 76-91. * [2] Marroni, M., Molli, G., Montanini, A., Ottria, G., Pandolfi, L., & Tribuzio, R. (2002). The external Ligurian units (Northern Apennine, Italy); from rifting to convergence of a fossil ocean-continent transition zone. Ofioliti, 27(2), 119-131. * [3] Gaggero, L., Crispini, L., Isola, E., & Marescotti, P. (2013). Asbestos in natural and anthropic ophiolitic environments: a case study of geohazards related to the Northern Apennine ophiolites (Eastern Liguria, Italy). Ofioliti, 38(1), 29-40. * [4] Beghè, D., Dall’Asta, L., Garavelli, C., Pastorelli, A. A., Muscarella, M., Saccani, G., ... & Chetta, A. (2017). Sarcoidosis in an Italian province. Prevalence and environmental risk factors.PloS one,12(5), e0176859.

We introduce a multi-platform portable implementation of the NonLocal Means methodology aimed at noise removal from remotely sensed images. It is particularly suited for hyperspectral sensors for which real-time applications are not possible with only CPU based algorithms. In the last decades computational devices have usually been a compound of cross-vendor sets of specifications (heterogeneous system architecture) that bring together integrated central processing (CPUs) and graphics processor (GPUs) units. However, the lack of standardization resulted in most implementations being too specific to a given architecture, eliminating (or making extremely difficult) code re-usability across different platforms. In order to address this issue, we implement a multi option NonLocal Means algorithm developed using the Open Computing Language (OpenCL) applied to Hyperion hyperspectral images. Experimental results demonstrate the dramatic speed-up reached by the algorithm on GPU with respect to conventional serial algorithms on CPU and portability across different platforms. This makes accurate real time denoising of hyperspectral images feasible.

ImaACor is a tool for the simultaneous correction of remotely sensed data from atmospheric and topographic effects, including second-order corrections, such as adjacency effects. The implemented approach is physically based and uses MODTRAN for computation of the main radiometric quantities. A user-friendly, comprehensive, and exhaustive graphic interface allows the user to choose from different correction levels. The various panels allow one to set all the parameters to correctly characterize the atmosphere and define the acquisition and illumination geometries. The tool provides a number of facilities to easily manage the correction process for a wide range of sensor data, including the ability to process multiple data in batch mode, which is very useful for dealing with temporal series. Under the inclusion of topographic correction, this tool allows the user to select a digital elevation model that is automatically resampled to the image resolution and processed to obtain the parameters for radiometric transfer modeling and the subsequent harmonization of the surface with the model inversion. This tool also includes utilities for the pre-processing of PRISMA data.

The enhanced spectral and spatial resolutions of the remote sensors have increased the need for highly performing preprocessing procedures. In this paper, a comprehensive approach, which simultaneously performs atmospheric and topographic corrections and includes second order corrections such as adjacency effects, was presented. The method, developed under the assumption of Lambertian surfaces, is physically based and uses MODTRAN 4 radiative transfer model. The use of MODTRAN 4 for the estimates of the radiative quantities was widely discussed in the paper and the impact on remote sensing applications was shown through a series of test cases.

Soil moisture (SM) plays a fundamental role in the terrestrial water cycle and in agriculture, with key applications such as the monitoring of crop growing and hydrogeological management. In this study, a calibration procedure was applied to estimate SM based on the integration of in situ and airborne thermal remote sensing data. To this aim, on April 2018, two airborne campaigns were carried out with the TASI-600 multispectral thermal sensor on the Petacciato (Molise, Italy) area. Simultaneously, soil samples were collected in different agricultural fields of the study area to determine their moisture content and the granulometric composition. A WorldView 2 high-resolution visible-near infrared (VNIR) multispectral satellite image was acquired to calculate the albedo of the study area to be used together with the TASI images for the estimation of the apparent thermal inertia (ATI). Results show a good correlation (R2 = 0.62) between the estimated ATI and the SM of the soil samples measured in the laboratory. The proposed methodology has allowed us to obtain a SM map for bare and scarcely vegetated soils in a wide agricultural area in Italy which concerns cyclical hydrogeological instability phenomena.

In this study the capabilities of seven multispectral and hyperspectral satellite imagers to estimate soil variables (clay, sand, silt and organic carbon content) were investigated using data from soil spectral libraries. Four current (EO-1 ALI and Hyperion, Landsat 8 OLI, Sentinel-2 MSI) and three forthcoming (EnMAP, PRISMA and HyspIRI) satellite imagers were compared. To this aim, two soil spectra datasets that simulated each imager were obtained: (i) resampled spectra according to the specific spectral response and resolution of each satellite imager and (ii) resampled spectra with declared or actual noise (radiometric and atmospheric) added. Compared with those using full spectral resolution data, the accuracy of Partial Least Square Regression (PLSR) predictive models generally decreased when using resampled spectra. In the absence of noise, the performances of hyperspectral imagers, in terms of Ratio of Performance to Interquartile Range (RPIQ), were generally significantly better than those of multispectral imagers. For instance the best RPIQ for sand estimation was obtained using EnMAP simulated data (2.56), whereas the outcomes gained using multispectral imagers varied from 1.56 and 2.28. The addition of noise to the simulated spectra brought about a decrease of statistical accuracy in all estimation models, especially for Hyperion data. Although the addition of noise reduced the performance differences between multispectral and hyperspectral imagers, the forthcoming hyperspectral imagers nonetheless provided the best RPIQ values for clay (2.16–2.33), sand (2.10–2.17), silt (2.77–2.85) and organic carbon (2.48–2.51) estimation. To better understand the impact of spectral resolution and signal to noise ratio (SNR) on the estimation of soil variables, PLSR models were applied to resampled and simulated spectra, iteratively increasing the bandwidth to: 10, 20, 40, 80 and 160nm. Results showed that, for a bandwidth of 40nm, i.e., a spectral resolution lower than that of current and forthcoming imagers, the estimation accuracy was very similar to that obtained with a higher spectral resolution.Forthcoming hyperspectral imagers will therefore improve the accuracy of soil variables estimation from bare soil imagery with respect to the results achievable by current hyperspectral and multispectral imagers, however this improvement is still too limited, to allow an accurate quantitative estimation of soil texture and SOC. This work provides useful indications about what could be expected, for the estimation of the most important soil variables, from the next generation of hyperspectral satellite imagers.

Soil moisture hampers the estimation of soil variables such as clay content from remote and proximal sensing data, reducing the strength of the relevant spectral absorption features. In the present study, two different strategies have been evaluated for their ability to minimize the influence of soil moisture on clay estimation by using soil spectra acquired in a laboratory and by simulating satellite hyperspectral data. Simulated satellite data were obtained according to the spectral characteristics of the forthcoming hyperspectral imager on board of the Italian PRISMA satellite mission. The soil datasets were split into four groups according to the water content. For each soil moisture level a prediction model was applied, using either spectral indices or partial least squares regression (PLSR). Prediction models were either specifically developed for the soil moisture level or calibrated using synthetically dry soil spectra, generated from wet soil data. Synthetically dry spectra were obtained using a new technique based on the effects caused by soil moisture on the optical spectrum from 400 to 2400 nm. The estimation of soil clay content, when using different prediction models according to soil moisture, was slightly more accurate as compared to the use of synthetically dry soil spectra, both employing clay indices and PLSR models. The results obtained in this study demonstrate that the a priori knowledge of the soil moisture class can reduce the error of clay estimation when using hyperspectral remote sensing data, such as those that will be provided by the PRISMA satellite mission in the near future.

The needs of useful tools for detecting and monitoring complex urban environments could benefit of new high spatial and spectral resolution thermal remote sensing data. In this sense and to face the most challenging future research themes in EO systems, the CNR IMAA has been upgraded its observing facilities, already composed by in situ hyperspectral and IR broadband sensors, airborne VSWIR hyperspectral sensors and multi-mission satellite receiving stations, with the TASI-600 hyperspectral scanner. The TASI-600 sensor has 32 bands in the LWIR (8.0-11.5 μm) spectral range, with a swath of 600 pixels for a FOV of 40° and an IFOV of 1.2 mrad. This study deals with TASI-600 airborne thermal data preprocessing carried out as follows: (a) radiometric calibration of the raw data and blinking pixels correction; (b) atmospheric correction of the TIR data; (c) separation of temperature and emissivity. Once retrieved emissivities for the complex urban scenario of the proposed study area located in Southern Italy, spectral classification algorithms have been tested and applied in order to retrieve and accurately classify surface materials occurring in this complex site. The paper presents the results of TASI-600 acquisition over this test site highlighting that the high spectral and radiometric resolution enables the accurate mapping of a range of surface materials, thus allowing for a preliminary characterization of urban materials and being an effective means to quantitatively monitor urban materials status and their possible alteration.