Arctic observations and sustainable development goals – Contributions and examples from ERA-PLANET iCUPE data

Noe, Steffen M.; Tabakova, Ksenia; Mahura, Alexander; Lappalainen, Hanna; Kosmale, Miriam; Heilimo, Jyri; Salzano, Roberto; Santoro, Mattia; Salvatori, Rosamaria; Spolaor, Andrea; Cairns, Warren Rl; Barbante, Carlo; Pankratov, Fidel; Humbert, Angelika; Sonke, Jeroen E.; Law, Kathy S.; Onishi, Tatsuo; Paris, Jean-Daniel; Skov, Henrik; Maßling, Andreas; Dommergue, Aurélien; Arshinov, Mikhail; Davydov, Denis; Belan, B. D.; Petäj̈ä, Tuukka

doi:10.1016/j.envsci.2022.02.034

Cited by 8 publications

(1 citation statement)

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“…SRS has risen in prominence as a near real-time (NRT) monitoring tool, addressing both natural and societal challenges. Its significance spans regional to global scales, supporting several global initiatives such as the Sendai Framework, Paris Agreement, and Sustainable Development Goals [21,36,46]. This paper utilizes publicly available satellite remote sensing (SRS) data from the European Copernicus Marine Service (CMS).…”

Section: Satellite Remote Sensing Datamentioning

confidence: 99%

Enhancing shallow water quality monitoring efficiency with deep learning and remote sensing: A case study in Mar Menor

Giménez,

González,

Martínez-España

et al. 2024

AIS

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Satellite remote sensing technology has proven effective in monitoring various environmental parameters, but its efficiency in assessing shallow lakes has been limited. This study applies state-of-the-art machine and deep learning algorithms supported by classical statistic methods to analyze remote sensing data to measure chlorophyll-a (Chl-a) concentration levels. Focused on a shallow coastal lagoon, Mar Menor, this work analyzes statistically daily Sentinel 3 information behaviour and compares Machine Learning and Deep Learning techniques to enhance efficiency and accuracy data of this satellite. Convolutional Neural Networks (CNNs) stand out as a robust choice, capable of delivering excellent results even in the presence of anomalous events. Our findings demonstrate that the CNN-based approach directly utilizing satellite data yields promising results in monitoring shallow lakes, offering enhanced efficiency and robustness. This research contributes to optimizing remote sensing data to and produce a continuous information flow addressed to monitoring shallow aquatic ecosystems with potential environmental management and conservation applications.

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Section: Satellite Remote Sensing Datamentioning

confidence: 99%