The EnMAP imaging spectroscopy mission towards operations

Storch, Tobias; Honold, Hans‐Peter; Chabrillat, Sabine; Habermeyer, Martin; Tucker, Paul; Brell, Maximilian; Ohndorf, Andreas; Wirth, Karl; Betz, Matthias Johannes; Kuchler, Michael; Mühle, Helmut; Carmona, E.; Baur, Simon; Mücke, Martin; Löw, Sebastian; Schulze, Daniel; Zimmermann, Steffen; Lenzen, Christoph; Wiesner, Sebastian; Aida, Saika; Kahle, Ralph; Willburger, Peter; Hartung, Sebastian; Dietrich, Daniele; Plesia, Nicolae; Tegler, Mirco; Schork, Katharina; Alonso, Kevin; Marshall, David; Gerasch, Birgit; Schwind, Peter; Pato, Miguel; Schneider, Mathias; Lopez, R. de los Reyes; Langheinrich, Maximilian; Wenzel, Julian; Bachmann, Martin; Holzwarth, Stefanie; Pinnel, Nicole; Guanter, Luis; Segl, Karl; Scheffler, Daniel; Foerster, Saskia; Bohn, Niklas; Bracher, Astrid; Soppa, Mariana Altenburg; Gascon, Ferran; Green, Rob; Kokaly, Raymond F.; Moreno, José; Ong, Cindy; Sornig, M.; Wernitz, Ricarda; Bagschik, Klaus; Reintsema, Detlef; Porta, L. La; Schickling, Anke; Fischer, Sebastian

doi:10.1016/j.rse.2023.113632

Cited by 45 publications

(19 citation statements)

References 30 publications

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“…The reasons are mainly due to the difficulties in accessing and managing the data. However, future improvements are expected in both data availability, partly due to new hyperspectral missions such as EnMAP [23], and data quality, using higher-accuracy DEMs for orthorectification. This would improve the reflectance accuracy, and thus even more accurate classifications are likely.…”

Section: Discussionmentioning

confidence: 99%

“…Among the latest EO hyperspectral missions are PRISMA (Hyperspectral Precursor of the Application Mission) and EnMAP (Environmental Mapping and Analysis Program). EnMAP is a German imaging spectroscopy mission that was launched in April 2022 and recently completed its commissioning phase [22,23]. PRISMA is developed by the ASI and has been in orbit since March 2019, with commissioning completed in January 2020 [24].…”

Section: Introductionmentioning

confidence: 99%

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Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support

Delogu,

Caputi,

Perretta

et al. 2023

Sustainability

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Hyperspectral satellite missions, such as PRISMA of the Italian Space Agency (ASI), have opened up new research opportunities. Using PRISMA data in land cover classification has yet to be fully explored, and it is the main focus of this paper. Historically, the main purposes of remote sensing have been to identify land cover types, to detect changes, and to determine the vegetation status of forest canopies or agricultural crops. The ability to achieve these goals can be improved by increasing spectral resolution. At the same time, improved AI algorithms open up new classification possibilities. This paper compares three supervised classification techniques for agricultural crop recognition using PRISMA data: random forest (RF), artificial neural network (ANN), and convolutional neural network (CNN). The study was carried out over an area of 900 km2 in the province of Caserta, Italy. The PRISMA HDF5 file, pre-processed by the ASI at the reflectance level (L2d), was converted to GeoTiff using a custom Python script to facilitate its management in Qgis. The Qgis plugin AVHYAS was used for classification tests. The results show that CNN gives better results in terms of overall accuracy (0.973), K coefficient (0.968), and F1 score (0.842).

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support

Delogu,

Caputi,

Perretta

et al. 2023

Sustainability

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“…These variables are obtained on a global scale in a routine and consistent way. EnMAP L2 products (version v01.04.00) can be obtained through two individual sub-processors for the atmospheric correction of orthorectified TOA radiance both over land (PACO) and water surfaces (Modular Inversion Program, MIP [69]) [70]. Overall, a total of 13 hyperspectral data with cloud coverage of less than 10% (apart from PRISMA acquired on 3 June 2020) were available for Lake Trasimeno, as presented in Table 2.…”

Section: Spaceborne Datamentioning

confidence: 99%

Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery

Fabbretto,

Bresciani,

Pellegrino

et al. 2024

Remote Sensing

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This work aims to show the potential of imaging spectroscopy in assessing water quality and aquatic vegetation in Lake Trasimeno, Italy. Hyperspectral reflectance data from the PRISMA, DESIS and EnMAP missions (2019–2022, summer periods) were compared with in situ measurements from WISPStation and used as inputs for water quality product generation algorithms. The bio-optical model BOMBER was run to simultaneously retrieve water quality parameters (Chlorophyll-a (Chl-a) and Total Suspended Matter, (TSM)) and the coverage of submerged and emergent macrophytes (SM, EM); value-added products, such as Phycocyanin concentration maps, were generated through a machine learning approach. The results showed radiometric agreement between satellite and in situ data, with R2 > 0.9, a Spectral Angle < 10° and water quality mapping errors < 30%. Both SM and EM coverage varied significantly from 2019 (135 ha, 0 ha, respectively) to 2022 (2672 ha, 343 ha), likely influenced by changes in rainfall and lake levels. The areas of greatest variability in Chl-a and TSM were identified in the littoral zones in the western side of the lake, while the highest variation in the fractional cover of SM and density of EM were observed in the south-eastern region; this information could support the water authorities’ monitoring activities. To this end, further developments to improve the reference field data for the validation of water quality products are recommended.

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“…The German Aerospace Center (Deutsches Zentrum für Luftund Raumfahrt; DLR) launched the DLR-EnMAP (Environmental Mapping and Analysis values has been conducted as detailed in [14]. The German Aerospace Center (Deutsches Zentrum für Luft-und Raumfahrt; DLR) launched the DLR-EnMAP (Environmental Mapping and Analysis Program) [15,16] in 2022 and its performance is here compared with the Italian sensor. To evaluate satellite acquisitions, proximal spectral measurements have been conducted in quasi-coincidence with the collection of space data on the Sale 'e Porcus pond using field hyperspectral radiometers [17]; indeed, the use of ground truth data is necessary to distinguish different materials on the Earth's surface.…”

Section: Introductionmentioning

confidence: 99%

Comparison of ASI-PRISMA Data, DLR-EnMAP Data, and Field Spectrometer Measurements on “Sale ‘e Porcus”, a Salty Pond (Sardinia, Italy)

Musacchio,

Silvestri,

Romaniello

et al. 2024

Remote Sensing

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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.

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The EnMAP imaging spectroscopy mission towards operations

Cited by 45 publications

References 30 publications

Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support

Using PRISMA Hyperspectral Data for Land Cover Classification with Artificial Intelligence Support

Tracking Water Quality and Macrophyte Changes in Lake Trasimeno (Italy) from Spaceborne Hyperspectral Imagery

Comparison of ASI-PRISMA Data, DLR-EnMAP Data, and Field Spectrometer Measurements on “Sale ‘e Porcus”, a Salty Pond (Sardinia, Italy)

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