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Special Issue "AI for Marine, Ocean and Climate Change Monitoring"

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "AI Remote Sensing".

Deadline for manuscript submissions: closed (15 June 2023) | Viewed by 19303

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Special Issue Editors

Dr. Veronica Nieves

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Guest Editor

Image Processing Laboratory, University of Valencia, 46980 Valencia, Spain
Interests: physical oceanography and global climate change via artificial intelligence; observation and integrated Earth-system modeling

Dr. Ana B. Ruescas

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Guest Editor

University of Valencia, Brockamnn Consult GmbH, Valencia, Spain
Interests: ocean colour; machine learning; SNAP
Special Issues, Collections and Topics in MDPI journals

Dr. Raphaëlle Sauzède

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Guest Editor

Laboratoire d'Océanographie de Villefranche, Institut de la Mer de Villefranche, 06230 Villefranche-sur-Mer, France
Interests: bio-optical and biogeochemical remote sensing; phytoplankton communities; neural networks

Special Issue Information

Dear Colleagues,

The oceans’ huge ability to absorb excess heat has important implications for the Earth's climate system. Higher sea levels, changes in ocean circulation and ocean biogeochemistry are some of the major consequences of ocean warming and melting glaciers. Due to complex feedbacks and climate connections, there is a growing interest in observing these ocean processes from space and modeling them by integrating new advanced statistical/machine learning and AI techniques, which may aid in the identification and prediction of such mechanisms.

Within this context, we invite submissions focusing on the skillful analysis and prediction of ocean-related processes through artificial neural networks and other machine learning approaches or their combination in hybrid dynamical–statistical methods. Contributions on remote sensing and modeling approaches designed to improve forecasting across various temporal scales and understanding sources of uncertainty/error in the model predictions are also welcome.

This Special Issue includes, but is not limited to, the following topics of interest:

Global and regional climate change monitoring;
Remote sensing and modeling of the oceans;
Data-driven and machine learning algorithms;
Data analysis, explainability, and prediction methods;
Process-based modeling, integration, and fusion.

Dr. Veronica Nieves
Dr. Ana B. Ruescas
Dr. Raphaëlle Sauzède
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

machine learning and hybrid models
ocean remote sensing techniques
climate change and feedbacks
physical oceanography
marine biogeochemistry

Published Papers (10 papers)

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Research

Open AccessArticle

A Graph Memory Neural Network for Sea Surface Temperature Prediction

and

Remote Sens. 2023, 15(14), 3539; https://doi.org/10.3390/rs15143539 - 14 Jul 2023

Viewed by 512

Abstract

Sea surface temperature (SST) is a key factor in the marine environment, and its accurate forecasting is important for climatic research, ecological preservation, and economic progression. Existing methods mostly rely on convolutional networks, which encounter difficulties in encoding irregular data. In this paper, allowing for comprehensive encoding of irregular data containing land and islands, we construct a graph structure to represent SST data and propose a graph memory neural network (GMNN). The GMNN includes a graph encoder built upon the iterative graph neural network (GNN) idea to extract spatial relationships within SST data. It not only considers node but also edge information, thereby adequately characterizing spatial correlations. Then, a long short-term memory (LSTM) network is used to capture temporal dynamics in the SST variation process. We choose the data from the Northwest Pacific Ocean to validate GMNN’s effectiveness for SST prediction in different partitions, time scales, and prediction steps. The results show that our model has better performance for both complete and incomplete sea areas compared to other models. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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Open AccessArticle

Detection of Sargassum from Sentinel Satellite Sensors Using Deep Learning Approach

Marine Laval

Abdelbadie Belmouhcine

and

Remote Sens. 2023, 15(4), 1104; https://doi.org/10.3390/rs15041104 - 17 Feb 2023

Cited by 1 | Viewed by 3672

Abstract

Since 2011, the proliferation of brown macro-algae of the genus Sargassum has considerably increased in the North Tropical Atlantic Sea, all the way from the Gulf of Guinea to the Caribbean Sea and the Gulf of Mexico. The large amount of Sargassum aggregations in that area cause major beaching events, which have a significant impact on the local economy and the environment and are starting to present a real threat to public health. In such a context, it is crucial to collect spatial and temporal data of Sargassum aggregations to understand their dynamics and predict stranding. Lately, indexes based on satellite imagery such as the Maximum Chlorophyll Index (MCI) or the Alternative Floating Algae Index (AFAI), have been developed and used to detect these Sargassum aggregations. However, their accuracy is questionable as they tend to detect various non-Sargassum features. To overcome false positive detection biases encountered by the index-thresholding methods, we developed two new deep learning models specific for Sargassum detection based on an encoder–decoder convolutional neural network (CNN). One was tuned to spectral bands from the multispectral instrument (MSI) onboard Sentinel-2 satellites and the other to the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3 satellites. This specific new approach outperformed previous generalist deep learning models, such as ErisNet, UNet, and SegNet, in the detection of Sargassum from satellite images with the same training, with an F1-score of 0.88 using MSI images, and 0.76 using OLCI images. Indeed, the proposed CNN considered neighbor pixels, unlike ErisNet, and had fewer reduction levels than UNet and SegNet, allowing filiform objects such as Sargassum aggregations to be detected. Using both spectral and spatial features, it also yielded a better detection performance compared to algal index-based techniques. The CNN method proposed here recognizes new small aggregations that were previously undetected, provides more complete structures, and has a lower false-positive detection rate. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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Open AccessEditor’s ChoiceArticle

AICCA: AI-Driven Cloud Classification Atlas

Takuya Kurihana

Elisabeth J. Moyer

and

Ian T. Foster

Remote Sens. 2022, 14(22), 5690; https://doi.org/10.3390/rs14225690 - 10 Nov 2022

Cited by 2 | Viewed by 1591

Abstract

Clouds play an important role in the Earth’s energy budget, and their behavior is one of the largest uncertainties in future climate projections. Satellite observations should help in understanding cloud responses, but decades and petabytes of multispectral cloud imagery have to date received only limited use. This study describes a new analysis approach that reduces the dimensionality of satellite cloud observations by grouping them via a novel automated, unsupervised cloud classification technique based on a convolutional autoencoder, an artificial intelligence (AI) method good at identifying patterns in spatial data. Our technique combines a rotation-invariant autoencoder and hierarchical agglomerative clustering to generate cloud clusters that capture meaningful distinctions among cloud textures, using only raw multispectral imagery as input. Cloud classes are therefore defined based on spectral properties and spatial textures without reliance on location, time/season, derived physical properties, or pre-designated class definitions. We use this approach to generate a unique new cloud dataset, the AI-driven cloud classification atlas (AICCA), which clusters 22 years of ocean images from the Moderate Resolution Imaging Spectroradiometer (MODIS) on NASA’s Aqua and Terra instruments—198 million patches, each roughly 100 km × 100 km (128 × 128 pixels)—into 42 AI-generated cloud classes, a number determined via a newly-developed stability protocol that we use to maximize richness of information while ensuring stable groupings of patches. AICCA thereby translates 801 TB of satellite images into 54.2 GB of class labels and cloud top and optical properties, a reduction by a factor of 15,000. The 42 AICCA classes produce meaningful spatio-temporal and physical distinctions and capture a greater variety of cloud types than do the nine International Satellite Cloud Climatology Project (ISCCP) categories—for example, multiple textures in the stratocumulus decks along the West coasts of North and South America. We conclude that our methodology has explanatory power, capturing regionally unique cloud classes and providing rich but tractable information for global analysis. AICCA delivers the information from multi-spectral images in a compact form, enables data-driven diagnosis of patterns of cloud organization, provides insight into cloud evolution on timescales of hours to decades, and helps democratize climate research by facilitating access to core data. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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Open AccessArticle

Applying Deep Learning in the Prediction of Chlorophyll-a in the East China Sea

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Remote Sens. 2022, 14(21), 5461; https://doi.org/10.3390/rs14215461 - 30 Oct 2022

Cited by 4 | Viewed by 1668

Abstract

The ocean chlorophyll-a (Chl-a) concentration is an important variable in the marine environment, the abnormal distribution of which is closely related to the hazards of red tides. Thus, the accurate prediction of its concentration in the East China Sea (ECS) is greatly important for preventing water eutrophication and protecting the coastal ecological environment. Processed by two different pre-processing methods, 10-year (2011–2020) satellite-observed chlorophyll-a data and logarithmic data were used as the long short-term memory (LSTM) neural network training datasets in this study. The 2021 data were used for comparison to prediction results. The past 15 days’ data were used to predict the concentration of chlorophyll-a for the five following days. Results showed that the predictions obtained by both pre-processing methods could simulate the seasonal distribution of the Chl-a concentration in the ECS effectively. Moreover, the prediction performance of the model driven by the original values was better in the medium- and low-concentration regions. However, in the high-concentration region, the prediction of extreme concentrations by the two data-driven LSTM models showed underestimation, considering that the prediction performance of the model driven by the original values was better. Results of sensitivity experiments showed that the prediction accuracy of the model decreased considerably when the backward prediction time step increased. In this study, the neural network was driven only by chlorophyll-a, whose concentration in the ECS was forecasted, and the effect of other relevant marine elements on Chl-a was not considered, which is the current weakness of this study. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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Open AccessCommunication

Spatiotemporal Prediction of Monthly Sea Subsurface Temperature Fields Using a 3D U-Net-Based Model

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Remote Sens. 2022, 14(19), 4890; https://doi.org/10.3390/rs14194890 - 30 Sep 2022

Cited by 2 | Viewed by 1130

Abstract

The ability to monitor and predict sea temperature is crucial for determining the likelihood that ocean-related events will occur. However, most studies have focused on predicting sea surface temperature, and less attention has been paid to predicting sea subsurface temperature (SSbT), which can reflect the thermal state of the entire ocean. In this study, we use a 3D U-Net model to predict the SSbT in the upper 400 m of the Pacific Ocean and its adjacent oceans for lead times of 12 months. Two reconstructed SSbT products are added to the training set to solve the problem of insufficient observation data. Experimental results indicate that this method can predict the ocean temperature more accurately than previous methods in most depth layers. The root mean square error and mean absolute error of the predicted SSbT fields for all lead times are within 0.5–0.7 °C and 0.3–0.45 °C, respectively, while the average correlation coefficient scores of the predicted SSbT profiles are above 0.96 for almost all lead times. In addition, a case study qualitatively demonstrates that the 3D U-Net model can predict realistic SSbT variations in the study area and, thus, facilitate understanding of future changes in the thermal state of the subsurface ocean. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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Open AccessArticle

Deep Learning to Near-Surface Humidity Retrieval from Multi-Sensor Remote Sensing Data over the China Seas

Rongwang Zhang

Weihao Guo

and

Xin Wang

Remote Sens. 2022, 14(17), 4353; https://doi.org/10.3390/rs14174353 - 02 Sep 2022

Viewed by 975

Abstract

Near-surface humidity (Q_a) is a key parameter that modulates oceanic evaporation and influences the global water cycle. Remote sensing observations act as feasible sources for long-term and large-scale Q_a monitoring. However, existing satellite Q_a retrieval models are subject to apparent uncertainties due to model errors and insufficient training data. Based on in situ observations collected over the China Seas over the last two decades, a deep learning approach named Ensemble Mean of Target deep neural networks (EMTnet) is proposed to improve the satellite Q_a retrieval over the China Seas for the first time. The EMTnet model outperforms five representative existing models by nearly eliminating the mean bias and significantly reducing the root-mean-square error in satellite Q_a retrieval. According to its target deep neural network selection process, the EMTnet model can obtain more objective learning results when the observational data are divergent. The EMTnet model was subsequently applied to produce 30-year monthly gridded Q_a data over the China Seas. It indicates that the climbing rate of Q_a over the China Seas under the background of global warming is probably underestimated by current products. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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Open AccessArticle

End-to-End Neural Interpolation of Satellite-Derived Sea Surface Suspended Sediment Concentrations

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Remote Sens. 2022, 14(16), 4024; https://doi.org/10.3390/rs14164024 - 18 Aug 2022

Cited by 3 | Viewed by 1186

Abstract

The characterization of suspended sediment dynamics in the coastal ocean provides key information for both scientific studies and operational challenges regarding, among others, turbidity, water transparency and the development of micro-organisms using photosynthesis, which is critical to primary production. Due to the complex interplay between natural and anthropogenic forcings, the understanding and monitoring of the dynamics of suspended sediments remain highly challenging. Numerical models still lack the capabilities to account for the variability depicted by in situ and satellite-derived datasets. Through the ever increasing availability of both in situ and satellite-derived observation data, data-driven schemes have naturally become relevant approaches to complement model-driven ones. Our previous work has stressed this potential within an observing system simulation experiment. Here, we further explore their application to the interpolation of sea surface sediment concentration fields from real gappy satellite-derived observation datasets. We demonstrate that end-to-end deep learning schemes—namely 4DVarNet, which relies on variational data assimilation formulation—apply to the considered real dataset where the training phase cannot rely on gap-free references but only on the available gappy data. 4DVarNet significantly outperforms other data-driven schemes such as optimal interpolation and DINEOF with a relative gain greater than 20% in terms of RMSLE and improves the high spatial resolution of patterns in the reconstruction process. Interestingly, 4DVarNet also shows a better agreement between the interpolation performance assessed for an OSSE and for real data. This result emphasizes the relevance of OSSE settings for future development calibration phases before the applications to real datasets. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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Open AccessArticle

Prediction of Sea Surface Temperature in the East China Sea Based on LSTM Neural Network

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Remote Sens. 2022, 14(14), 3300; https://doi.org/10.3390/rs14143300 - 08 Jul 2022

Cited by 8 | Viewed by 2239

Abstract

Sea surface temperature (SST) is an important physical factor in the interaction between the ocean and the atmosphere. Accurate monitoring and prediction of the temporal and spatial distribution of SST are of great significance in dealing with climate change, disaster prevention, disaster reduction, and marine ecological protection. This study establishes a prediction model of sea surface temperature for the next five days in the East China Sea using long-term and short-term memory neural networks (LSTM). It investigates the influence of different parameters on prediction accuracy. The sensitivity experiment results show that, based on the same training data, the length of the input data of the LSTM model can improve the model’s prediction performance to a certain extent. However, no obvious positive correlation is observed between the increase in the input data length and the improvement of the model’s prediction accuracy. On the contrary, the LSTM model’s performance decreases with the prediction length increase. Furthermore, the single-point prediction results of the LSTM model for the estuary of the Yangtze River, Kuroshio, and the Pacific Ocean are accurate. In particular, the prediction results of the point in the Pacific Ocean are the most accurate at the selected four points, with an RMSE of 0.0698 °C and an

R^{2}

of 99.95%. At the same time, the model in the Pacific region is migrated to the East China Sea. The model was found to have good mobility and can well represent the long-term and seasonal trends of SST in the East China Sea. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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Open AccessArticle

An Algorithm to Bias-Correct and Transform Arctic SMAP-Derived Skin Salinities into Bulk Surface Salinities

David Trossman

and

Eric Bayler

Remote Sens. 2022, 14(6), 1418; https://doi.org/10.3390/rs14061418 - 15 Mar 2022

Viewed by 1821

Abstract

An algorithmic approach, based on satellite-derived sea-surface (“skin”) salinities (SSS), is proposed to correct for errors in SSS retrievals and convert these skin salinities into comparable in-situ (“bulk”) salinities for the top-5 m of the subpolar and Arctic Oceans. In preparation for routine assimilation into operational ocean forecast models, Soil Moisture Active Passive (SMAP) satellite Level-2 SSS observations are transformed using Argo float data from the top-5 m of the ocean to address the mismatch between the skin depth of satellite L-band SSS measurements (∼1 cm) and the thickness of top model layers (typically at least 1 m). Separate from the challenge of Argo float availability in most of the subpolar and Arctic Oceans, satellite-derived SSS products for these regions currently are not suitable for assimilation for a myriad of other reasons, including erroneous ancillary air-sea forcing/flux products. In the subpolar and Arctic Oceans, the root-mean-square error (RMSE) between the SMAP SSS product and several in-situ salinity observational data sets for the top-5 m is greater than 1.5 pss (Practical Salinity Scale), which can be larger than their temporal variability. Thus, we train a machine-learning algorithm (called a Generalized Additive Model) on in-situ salinities from the top-5 m and an independent air-sea forcing/flux product to convert the SMAP SSS into bulk-salinities, correct biases, and quantify their standard errors. The RMSE between these corrected bulk-salinities and in-situ measurements is less than 1 pss in open ocean regions. Barring persistently problematic data near coasts and ice-pack edges, the corrected bulk-salinity data are in better agreement with in-situ data than their SMAP SSS equivalent. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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Open AccessArticle

Super-Resolving Ocean Dynamics from Space with Computer Vision Algorithms

Bruno Buongiorno Nardelli

Davide Cavaliere

Elodie Charles

and

Daniele Ciani

Remote Sens. 2022, 14(5), 1159; https://doi.org/10.3390/rs14051159 - 26 Feb 2022

Cited by 8 | Viewed by 2075

Abstract

Surface ocean dynamics play a key role in the Earth system, contributing to regulate its climate and affecting the marine ecosystem functioning. Dynamical processes occur and interact in the upper ocean at multiple scales, down to, or even less than, few kilometres. These scales are not adequately resolved by present observing systems, and, in the last decades, global monitoring of surface currents has been based on the application of geostrophic balance to absolute dynamic topography maps obtained through the statistical interpolation of along-track satellite altimeter data. Due to the cross-track distance and repetitiveness of satellite acquisitions, the effective resolution of interpolated data is limited to several tens of kilometres. At the kilometre scale, sea surface temperature pattern evolution is dominated by advection, providing indirect information on upper ocean currents. Computer vision techniques are perfect candidates to infer this dynamical information from the combination of altimeter data, surface temperature images and observing-system geometry. Here, we exploit one class of image processing techniques, super-resolution, to develop an original neural-network architecture specifically designed to improve absolute dynamic topography reconstruction. Our model is first trained on synthetic observations built from a numerical general-circulation model and then tested on real satellite products. Provided concurrent clear-sky thermal observations are available, it proves able to compensate for altimeter sampling/interpolation limitations by learning from primitive equation data. The algorithm can be adapted to learn directly from future surface topography, and eventual surface currents, high-resolution satellite observations. Full article

(This article belongs to the Special Issue AI for Marine, Ocean and Climate Change Monitoring)

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