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WASI-AI: Synergistic Integration of AI and Physics for Retrieving Water Quality and Benthic Parameters from Multi- and Hyperspectral Images

Niroumand-Jadidi, Milad und Gege, Peter (2025) WASI-AI: Synergistic Integration of AI and Physics for Retrieving Water Quality and Benthic Parameters from Multi- and Hyperspectral Images. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 18, Seiten 22832-22846. IEEE - Institute of Electrical and Electronics Engineers. doi: 10.1109/JSTARS.2025.3605061. ISSN 1939-1404.

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Kurzfassung

Artificial intelligence (AI) has spurred significant progress in the remote sensing of water constituents through physical models that rely on a predefined database of simulations, e.g., Case-2 Regional/Coast Colour (C2RCC) processor. However, these models are sensor-specific, not applicable in optically shallow waters, and incapable of adapting to different bio-optical conditions. This study introduces a novel approach that synergistically integrates AI with physics-based modeling. The developed method, termed WASI-AI, is implemented as a new module into the Water Colour Simulator (WASI) software. WASI-AI uses the physics-based WASI-2D module of WASI to retrieve the unknown biophysical parameters for a small subset of image pixels selected at random. A portion of the inverted samples is used to train neural networks (NNs). The trained NNs are then applied to predict the unknown biophysical parameters for all water pixels of the image. The remaining portion of the samples is used to assess the agreement between WASI-AI and WASI-2D retrievals. WASI-AI maintains the advantages of WASI-2D regarding sensor independence and flexibility in bio-optical adaptation. The correlation plots of WASI-AI vs. WASI-2D allow recognizing spectral ambiguity and optimizing inverse modeling parametrization. The integration of AI significantly speeds up the inversion, reducing the processing time of a single image from hours/days to mere minutes. We applied WASI-AI to hyperspectral (EnMAP, DESIS) and multispectral (Sentinel-2, Landsat-8/9, Planet SuperDove) imagery in optically deep and shallow waters. After handeling spectral ambiguities, the results indicate a strong correspondence between WASI-AI and WASI-2D inversions, with WASI-AI exhibiting lower noises on the maps.

elib-URL des Eintrags:https://elib.dlr.de/216248/
Dokumentart:Zeitschriftenbeitrag
Titel:WASI-AI: Synergistic Integration of AI and Physics for Retrieving Water Quality and Benthic Parameters from Multi- and Hyperspectral Images
Autoren:
AutorenInstitution oder E-Mail-AdresseAutoren-ORCID-iDORCID Put Code
Niroumand-Jadidi, Miladmilad.niroumand (at) unibo.ithttps://orcid.org/0000-0002-9432-3032NICHT SPEZIFIZIERT
Gege, Peterpeter.gege (at) dlr.dehttps://orcid.org/0000-0003-0939-5267NICHT SPEZIFIZIERT
Datum:1 September 2025
Erschienen in:IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing
Referierte Publikation:Ja
Open Access:Ja
Gold Open Access:Ja
In SCOPUS:Ja
In ISI Web of Science:Ja
Band:18
DOI:10.1109/JSTARS.2025.3605061
Seitenbereich:Seiten 22832-22846
Verlag:IEEE - Institute of Electrical and Electronics Engineers
ISSN:1939-1404
Status:veröffentlicht
Stichwörter:artificial intelligence, physics-based inversion, model integration, aquatic remote sensing, water quality, bathymetry, benthic properties, spectral ambiguities, WASI, WASI-AI
HGF - Forschungsbereich:Luftfahrt, Raumfahrt und Verkehr
HGF - Programm:Raumfahrt
HGF - Programmthema:Erdbeobachtung
DLR - Schwerpunkt:Raumfahrt
DLR - Forschungsgebiet:R EO - Erdbeobachtung
DLR - Teilgebiet (Projekt, Vorhaben):R - Optische Fernerkundung
Standort: Oberpfaffenhofen
Institute & Einrichtungen:Institut für Methodik der Fernerkundung > Abbildende Spektroskopie
Hinterlegt von: Gege, Dr.rer.nat. Peter
Hinterlegt am:22 Sep 2025 11:48
Letzte Änderung:22 Sep 2025 11:48

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