Comparison of PRISMA Data with Model Simulations, Hyperion Reflectance and Field Spectrometer Measurements on ‘Piano delle Concazze’ (Mt. Etna, Italy)

Abstract

In this work, we compare first acquisitions from the ASI-PRISMA (Agenzia Spaziale Italiana-PRecursore IperSpettrale della Missione Applicativa) space mission with model simulations, past data acquired by the Hyperion sensor and field spectrometer measurements. The test site is ‘Piano delle Concazze’ (Mt. Etna, Italy), suitable for calibration purposes due to its homogeneity characteristics. The area measures at about 0.2 km2 and is composed of very homogeneous trachybasalt rich in plagioclase and olivine. Three PRISMA acquisitions, achieved on 31 July and 8 and 17 August 2019, are analyzed. Firstly, spectral profiles of PRISMA top of atmosphere (TOA) radiance are compared with MODerate resolution atmospheric TRANsmission (MODTRAN) simulations. The Pearson correlation coefficient is equal to 0.998 and 0.994 for VNIR (Visible and Near InfraRed) and SWIR (Short-Wave InfraRed) spectral ranges, respectively. PRISMA radiance overestimates values simulated by MODTRAN for all considered days, showing a mean bias of +5.22 and of +0.91 Wm−2sr−1µm−1 for VNIR and SWIR, respectively. The relative mean difference between reflectance values estimated by PRISMA and Hyperion, on the test area, is around +19%, despite the great difference in time acquisition (up to 19 years); PRISMA slightly overestimates Hyperion reflectance with an absolute mean difference of about +0.0083, within the variability of Hyperion acquisitions of ±0.0250 (corresponding to ±2 standard deviation). Finally, FieldSpec measurements also confirm the great quality of PRISMA reflectance estimations. The absolute mean difference results are around +0.0089 (corresponding to a relative error of about +21%). In the study, we investigate only the lower values of reflectance characterizing the test site. A more complete evaluation of PRISMA performances needs to consider other test sites with different optical characteristics.

Joint Use of in-Scene Background Radiance Estimation and Optimal Estimation Methods for Quantifying Methane Emissions Using PRISMA Hyperspectral Satellite Data: Application to the Korpezhe Industrial Site

“…This quasi-monochromatic absorption is then convolved with the PRISMA spectral response function (red line). In this spectral range, the PRISMA satellite has a spectral sampling distance of approximately 9 nm and a spectral resolution (Full Width of Half Maximum) between 9 and 15 nm [54]. The hyperspectral camera's spatial resolution is approximately 30 m (ground sampling distance) and an image covers an area of 30 km × 30 km.…”

PRISMA Hyperspectral Remote Sensing Data for Mapping Alteration Minerals in Sar-e-Châh-e-Shur Region, Birjand, Iran

“…Consequently, various minerals might exhibit similar spectral attributes Remote Sens. 2024, 16, 1277 2 of 31 using multispectral imagery. Thus, hyperspectral imagery, encompassing contiguous and comprehensive spectral capabilities, provides a more precise characterization of alteration minerals compared to multispectral remote sensing [2].…”

“…The Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), Independent Component Analysis (ICA), Adaptive Coherence Estimator (ACE), Random Forest (RF), XGboost (XGB), Support Vector Machine (SVM) and many other machine-learningbased classification algorithms were used for processing PRISMA data in mapping alteration minerals, identifying economic mineralization prospects, delineating dolomitization, obtaining high-quality reflectance estimations and achieving high-accuracy lithological mapping [2,[7][8][9][10][14][15][16][17][18][19]. Additionally, novel approaches and algorithms such as the spectral hourglass, iterative informed spectral unmixing technique, fuzzy logic approach, GIS-based algorithm, and informed linear mixing model were implemented to PRISMA data to determine the potential of the dataset for automated alteration mineral identification in diverse environments [20][21][22][23][24][25].…”

The Weight of Hyperion and PRISMA Hyperspectral Sensor Characteristics on Image Capability to Retrieve Urban Surface Materials in the City of Venice

“…With regard to the first approach, because spectral unmixing analysis is the most widely applied method to hyperspectral images (e.g., [ 32 , 33 , 34 ]), some authors proposed methods to solve the unmixing problem (e.g., pixel purity index [ 35 ], N-FINDR [ 36 ], interactive error analysis [ 37 ]), or to estimate the endmember fractional abundances (e.g., [ 38 ]), whereas other authors developed methods based on spatial analysis (e.g., Spectral Angle Mapping—SAM [ 39 ] and Spectral Information Divergence—SID [ 40 ]). With regard to the second approach, the results obtained from hyperspectral data were compared with those obtained from other hyperspectral data (e.g., Hyperion images were compared with CHRIS [ 41 ], Hyperspectral Satellite TianGong-1 [ 42 ], and PRISMA [ 43 ] hyperspectral data), from multispectral data (e.g., CASI and MIVIS hyperspectral images were compared with ATM multispectral data [ 44 ], and PRISMA hyperspectral images were compared with Sentinel-2A multispectral data [ 45 ]), and from other data (e.g., AHSI hyperspectral data were compared with the GlobalLand30 land cover data set [ 46 ]; MIVIS hyperspectral image was merged with DEM [ 47 ]). However, there are many sources of error as the capability evaluated from real image is due to both the characteristics of the sensor and each step of image pre-processing and processing (i.e., calibration [ 7 , 48 ]; atmospheric [ 49 , 50 ] and geometric [ 51 ] corrections; dimension reduction [ 30 ]; selected method [ 52 ]; etc.).…”