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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. [ABSTRACT FROM AUTHOR]

Abstract: Over the past few years, the increased spectral and spatial resolution of remote sensing equipment has promoted the investigation of new techniques for inland and coastal water monitoring. The availability of new high-resolution data has allowed improvements in models based on the radiative transfer theory for assessing optical water quality parameters. In this study, we fine-tuned a physical model for the highly turbid Venice lagoon waters and developed an inversion technique based on a two-step optimization procedure appropriate for hyperspectral data processing to retrieve water constituent concentrations from remote data. In the first step, the solution of a linearized analytical formulation of the radiative transfer equations was found. In the second step, this solution was used to provide the initial values in a non-linear least squares-based method. This effort represents a first step in the construction of a feasible and timely methodology for Venice lagoon water quality monitoring by remote sensing, especially in view of the existing experimental hyperspectral satellite (Hyperion) and the future missions such as PRISMA, EnMap and HyspIRI. The optical properties of the water constituents were assessed on the basis of sea/lagoon campaigns and data from the literature. The water light field was shaped by an analytical formulation of radiative transfer equations and the application of numerical simulations (Hydrolight software). Once the optical properties of the Venice lagoon bio-optical model were validated, the inverse procedure was applied to local radiometric spectra to retrieve concentrations of chlorophyll, colored dissolved organic matter and tripton. The inverse procedure was validated by comparing these concentrations with those measured in the laboratory from in situ water samples, then it was applied to airborne (CASI and MIVIS) and satellite (Hyperion) sensors to derive water constituent concentration maps. The consistent results encourage the use of this procedure using future missions satellite (PRISMA, EnMap and HyspIRI). [Copyright &y& Elsevier]

ImaAtCor 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 160 nm. Results showed that, for a bandwidth of 40 nm, 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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Abstract: The aim of this study is to explore the capability of hyperspectral remote sensing to map areas affected by Red Dust (RD) pollution on soil and river water. The chosen study area contains residues from the extraction of aluminum from bauxite in a mud pond (i.e. the red mud), which contain high concentrations of contaminants such as heavy metals, so that special precaution must be taken when disposing to avoid RD pollution of land surface and river water resources. We perform laboratory analysis and in situ measurements of RD polluted samples to recognize the dominant minerals and identify the optical characteristics of the samples and their spectral features. Both an unsupervised methodology and a shape-based spectral analysis technique, using the significant RD reflectance spectral features, were applied to airborne hyperspectral remote sensing data to map the RD distribution on bare soils. Whereas, to assess a tool for the detection of RD in river waters a semi-analytical model for the radiative transfer in water bodies was used. The results of the spectral shape-based analysis and of the semi-analytical model used for this study were consistent with ground truth data and hence support the application of hyperspectral technologies for a rapid detection and mapping of industrial wastes, such as the RD. This information is suitable to support the development of effective intervention policies and monitoring programs. [Copyright &y& Elsevier]

This paper aims to assess the suitability of remote sensing for enhancing the management of water body resources and for providing an inexpensive way to gather, on a wide area, weed infestation extent and optical parameter linked to the water body status. Remotely sensed satellite images and ancillary ground true data were used to produce land cover maps, trough classification techniques, and water compounds maps, applying radiative transfer models. The study proposed within the framework of the cooperation between Italian Foreign Affair Ministry (through the University of Rome) and Kenyan Authorities has been carried out on the Kenyan part of the Lake Victoria. This lake is one of the largest freshwater bodies of the world where, over the last few years environmental challenges and human impact have perturbed the ecological balance affecting the biodiversity. The objective of this research study is to define the thematic products, retrievable from satellite images, like weed abundance maps and water compound concentrations. These products, if provided with an appropriate time frequency, are useful to identify the preconditions for the occurrence of hazard events like abnormal macrophyte proliferation and to develop an up-to-date decision support system devoted to an apprised territory, environment and resource management. [Copyright &y& Elsevier]

We analyze the capability of Hyperion spaceborne hyperspectral data for discriminating land cover in a complex natural ecosystem according to the structure of the currently used European standard classification system (CORINE Land Cover 2000). For this purpose, we used Hyperion imagery acquired over Pollino National Park (Italy). Hyperion pre-processed data (30 m spatial resolution) were classified at the pixel level using common parametric supervised classification methods. The algorithms'' performance and class level accuracy were compared with those obtained for the same area using airborne hyperspectral MIVIS data (7 m spatial resolution). Moreover, in selected test areas characterized by heterogeneous land cover (as mapped by MIVIS classification) a Linear Spectral Unmixing (LSU) technique was applied to Hyperion data to derive the abundance fractions of land cover endmembers. The accuracy of the LSU analysis was evaluated using the Residual Error parameter, by comparing Hyperion LSU results with land cover fractional abundances achieved from reference data (i.e., MIVIS and air-photo classification). The results show the potential of Hyperion spaceborne hyperspectral imagery in mapping land cover and vegetation diversity up to the 4th level of the CORINE legend, even at the sub-pixel level, within a fragmented ecosystem such as that of Pollino National Park. Moreover, we defined a criterion for evaluating the Hyperion accuracy in retrieving land cover abundances at the sub-pixel scale. Sub-pixel analysis allowed us to determine the optimal threshold to select the areas on which consistent fractional land cover monitoring can be achieved using the Hyperion sensor. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The mapping of the serpentinites rocks formations could be of extreme interest to the public Authorities because these outcrops contains natural occurring asbestos (NOA) minerals such as chrysotile, tremolite and actinolite, which are minerals with a high potential hazard to human health. The weathering of serpentines outcrops causes the dispersion of asbestos fibers into the environment that, when inhaled, can lead to lung cancer. In Italy, serpentinites and ultramafic rocks are common in the Alps and Apennines Mountains and in particular in the Liguria, Toscana, Piemonte, Calabria and Basilicata regions. In the framework of the BRIC 2016-2018 research activities supported by the Italian "Istituto Nazionale Assicurazione contro gli Infortuni sul Lavoro", the research contract (ID57/2016) deals with the exploitation of the different sensing technologies from proximal to remote (from airborne to satellite) and processing methodologies to detect both manmade materials containing asbestos and NOA. The peculiar asbestos characteristics spectral features can be found at about 1.385 µm, 2.323 µm and at about 9.6 µm in the LWIR spectral range. While the NIR and SWIR spectral regions have been well explored with the available imaging systems both by airborne (configured with either multispectral or hyperspectral payloads) or high-resolution spaceborne sensors, the LWIR spectral range, has been less investigated for the detection of the asbestos minerals group. The LWIR spectral range is very promising because of the lack of the adjacency of other specific spectral features of the minerals normally associated with the building material containing asbestos (e.g., asbestos cement) and minerals (e.g. limestone). In the literature, an example of retrieving materials occurring asbestos (roof shields) with LWIR spectral range is reported in the work of Bassani et al. (2007).Thus, to allow the exploitation of the LWIR spectral range potential for the detection and identification of the asbestos cement material (mainly roof shield) and NOA, an airborne survey has been carried out with the airborne hyperspectral TASI-600 sensor acquiring 32 spectral bands in the LWIR spectra range. The stripes acquired cover the area of San Severino Lucano in the Basilicata Region (Italy), an area of about 80 km2 with 25 flight lines at an altitude of 900-1000 above ground. The survey will acquire images with a spatial resolution of 1m/pixel and 32 channels from 8.0 to 11.5 µm with a spectral resolution of 100nm. The survey covered the ophiolitic rocks and the Plio-quaternary sediments outcrops of the ophiolites, which are associated with fragments of continental crust rocks, which can provide information about the geodynamic evolution of the ophiolites. This communication will present the multispectral LWIR TASI survey performed on the San Severino area in terms of: (i) flight plan and the images quick looks; (ii) data pre-processing for radiometric and geometric correction and its accuracy; (iii) retrieving of LST by using a split window technique and (iv) a preliminary map of the serpentinites outcrops of the area validated with the available geologic map. [ABSTRACT FROM AUTHOR]