Search Results (4)

A comparison between the ASI-PRISMA (Agenzia Spaziale Italiana-PRecursore IperSpettrale della Missione Applicativa) DLR-EnMAP (German Aerospace Center—Environmental Mapping and Analysis Program) data and field spectrometer measurements has been performed. The test site, located at the “Sale ‘e Porcus” pond (hereafter SPp) in Western Sardinia, Italy, offers particularly homogenous characteristics, making it an ideal location not only for experimentation but also for calibration purposes. Three remote-sensed data acquisitions have been performed by these agencies (ASI and DLR) starting on 14 July 2023 and continuing until 22 July 2023. The DLR-EnMAP data acquired on 22 July overestimates both that of the ASI-PRISMA and the 14 July DLR-EnMAP radiance in the VNIR region, while all the datasets are close to each other, up to 2500 nm, for all considered days. The average absolute mean difference between the reflectance values estimated by the ASI-PRISMA and DLR-EnMAP, in the test area, is around 0.015, despite the small difference in their time of acquisition (8 days); their maximum relative difference value occurs at about 2100 nm. In this study, we investigate the relationship between the averaged ground truth value of reflectance, acquired by means of a portable ASD FieldSpec spectoradiometer, characterizing the test site and the EO reflectance data derived from the official datasets. FieldSpec measurements confirm the quality of both the ASI-PRISMA and DLR-EnMAP’s reflectance estimations. Full article

The monitoring of coastal areas using remote sensing techniques is an important issue to determine the bio-optical properties of the water column and the seabed composition. New hyperspectral satellite sensors (e.g., PRISMA, DESIS or EnMap) are developed to periodically observe ecosystems. The uncertainties in the retrieved geophysical products remain a key issue to release reliable data useful for the end-users. In this study, an analytical approach based on Information theory is proposed to investigate the Cramér–Rao lower Bounds (CRB) for the uncertainties in the ocean color parameters. Practically, during the inversion process, an a priori knowledge on the estimated parameters is used since their range of variation is supposed to be known. Here, a Bayesian approach is attempted to handle such a priori knowledge. A Bayesian CRB (BCRB) is derived using the Lee et al. semianalytical radiative transfer model dedicated to shallow waters. Both environmental noise and bio-optical parameters are supposed to be random vectors that follow a Gaussian distibution. The calculation of CRB and BCRB is carried out for two hyperspectral images acquired above the French mediterranean coast. The images were obtained from the recently launched hyperspectral sensors, namely the DESIS sensor (DLR Earth Sensing Imaging Spectrometer, German Aerospace Center), and PRISMA (Precursore IpperSpettrale della Mission Applicativa—ASI, Italian Space Adjency) sensor. The comparison between the usual CRB approach, the proposed BCRB approach and experimental errors obtained for the retrieved bathymetry shows the better ability of the BCRB to determine minimum error bounds. Full article

The hyperspectral (HSR) sensors Earth Surface Mineral Dust Source Investigation (EMIT) of the National Aeronautics and Space Administration (NASA) and Environmental Mapping and Analysis Program (EnMAP) of the German Aerospace Center (DLR) were recently launched. These state-of-the-art sensors have joined the already operational HSR sensors DESIS (DLR), PRISMA (Italian Space Agency), and HISUI (developed by the Japanese Ministry of Economy, Trade, and Industry METI and Japan Aerospace Exploration Agency JAXA). The launching of more HSR sensors is being planned for the near future (e.g., SBG of NASA, and CHIME of the European Space Agency), and the challenge of monitoring and maintaining their calibration accuracy is becoming more relevant. We proposed two test sites: Amiaz Plain (AP) and Makhtesh Ramon (MR) for spectral, radiometric, and geometric calibration/validation (CAL/VAL). The sites are situated in the arid environment of southern Israel and are in the same overpass coverage. Both test sites have already demonstrated favorable results in assessing an HSR sensor’s performance and were chosen to participate in the EMIT and EnMAP validation stage. We first evaluated the feasibility of using AP and MR as CAL/VAL test sites with extensive datasets and sensors, such as the multispectral sensor Landsat (Landsat5 TM and Landsat8 OLI), the airborne HSR sensor AisaFENIX 1K, and the spaceborne HSR sensors DESIS and PRISMA. Field measurements were taken over time. The suggested methodology integrates reflectance and radiometric CAL/VAL test sites into one operational protocol. The method can highlight degradation in the spectral domain early on, help maintain quantitative applications, adjust the sensor’s radiometric calibration during its mission lifetime, and minimize uncertainties of calibration parameters. A PRISMA sensor case study demonstrates the complete operational protocol, i.e., performance evaluation, quality assessment, and cross-calibration between HSR sensors. These CAL/VAL sites are ready to serve as operational sites for other HSR sensors. Full article

The atmospheric correction of satellite images based on radiative transfer calculations is a prerequisite for many remote sensing applications. The software package ATCOR, developed at the German Aerospace Center (DLR), is a versatile atmospheric correction software, capable of processing data acquired by many different optical satellite sensors. Based on this well established algorithm, a new Python-based atmospheric correction software has been developed to generate L2A products of Sentinel-2, Landsat-8, and of new space-based hyperspectral sensors such as DESIS (DLR Earth Sensing Imaging Spectrometer) and EnMAP (Environmental Mapping and Analysis Program). This paper outlines the underlying algorithms of PACO, and presents the validation results by comparing L2A products generated from Sentinel-2 L1C images with in situ (AERONET and RadCalNet) data within VNIR-SWIR spectral wavelengths range. Full article