Earth and Planetary Sciences, Signal Processing, Earth-Surface Processes, Agronomy and Crop Science
39
Scopus Publications
Scopus Publications
Atmospheric Correction Inter-Comparison eXercise, ACIX-III Land: An Assessment of Atmospheric Correction Processors for EnMAP and PRISMA over Land Noelle Cremer, Kevin Alonso, Georgia Doxani, Adam Chlus, David R. Thompson, et al. Remote Sensing, 2025 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.
Early-Season Crop Mapping by PRISMA Images Using Machine/Deep Learning Approaches: Italy and Iran Test Cases Saham Mirzaei, Simone Pascucci, Maria Francesca Carfora, Raffaele Casa, Francesco Rossi, et al. Remote Sensing, 2024 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.
Detection of Critical Areas Prone to Land Degradation Using Prisma: The Metaponto Coastal Area in South Italy Test Case S. Pignatti, M. F. Carfora, R. Coluzzi, L. D’Amato, I. De Feis, et al. International Geoscience and Remote Sensing Symposium IGARSS, 2024 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.
Impact of Topographic Correction on PRISMA Sentinel 2 and Landsat 8 Images Federico Santini, Angelo Palombo Remote Sensing, 2022 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.
PRISMA L1 and L2 Performances within the PRISCAV Project: The Pignola Test Site in Southern Italy Stefano Pignatti, Aldo Amodeo, Maria Francesca Carfora, Raffaele Casa, Lucia Mona, et al. Remote Sensing, 2022 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.
EVALUATION OF THE PRISMA HYPERSPECTRAL RADIANCE DATA: THE PRISCAV PROJECT ACTIVITIES IN THE BASILICATA REGION (SOUTHERN ITALY) S. Pignatti, A. Amodeo, L. Mona, A. Palombo, S. Pascucci, et al. International Geoscience and Remote Sensing Symposium IGARSS, 2021 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.
ImaAtCor: A physically based tool for combined atmospheric and topographic corrections of remote sensing images Angelo Palombo, Federico Santini Remote Sensing, 2020 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.
Noise removal from remote sensed images by non local means with OpenCL algorithm Donatella Granata, Angelo Palombo, Federico Santini, Umberto Amato Remote Sensing, 2020 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.
Physically based approach for combined atmospheric and topographic corrections Federico Santini, Angelo Palombo Remote Sensing, 2019 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.
Empirical algorithm for the CDOM assessment: Preliminary results of the Albanian and montenegrin coastal areas Proceedings 33rd International Symposium on Remote Sensing of Environment Isrse 2009, 2009
Discrimination of phanerogam communities through hyperspectral data: Preliminary results of Montenegro coastal areas Proceedings 33rd International Symposium on Remote Sensing of Environment Isrse 2009, 2009
Remote sensing and physical modelling approach for macrophyte and water quality parameters monitoring European Space Agency Special Publication ESA SP, 2006
