From Land to Sea, a Review of Hypertemporal Remote Sensing Advances to Support Ocean Surface Science

Scarrott, Rory; Cawkwell, Fiona; Jessopp, Mark; O’Rourke, Eleanor; Cusack, Caroline; Bie, C.A.J.M. de

doi:10.3390/w11112286

Cited by 5 publications

(6 citation statements)

References 98 publications

(229 reference statements)

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“…Time-series analysis can be used to explain and characterise the occurrence of observed temporal phenomena [88]. However, its use in ocean studies is restricted by the need for experience, and a-priori knowledge of regional seasonalities [26], used to guide model training [89]. Here, the suite of OHMA output datasets could be of use.…”

Section: Discussionmentioning

confidence: 99%

“…They are systematically collected, temporally and spatially extensive, with many parameter datasets now in excess of 30+ years, and are often freely available. A subset of these long-term, high-temporal-resolution imagery, are known as hypertemporal data [25,26]. They are characterised by:…”

Section: Hypertemporal Opportunitiesmentioning

confidence: 99%

“…Only when the minimum Jeffries-Matusita separability is at 1.414 or above, are coincident peaks in divergence acceptable as indicators of the upper bounds of the OHMA cluster boundary ensemble (Figure 4). The process of optimising the number of clusters and producing the cluster ensemble is thus fully automated, without need for expert knowledge of the region, and meeting the ocean science criteria outlined in [26]. A graphical illustration of the separability analysis which determined the optimal number of data clusters.…”

Section: Ohma Implementationmentioning

confidence: 99%

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Ocean-Surface Heterogeneity Mapping (OHMA) to Identify Regions of Change

et al. 2021

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Mapping heterogeneity of the ocean’s surface waters is important for understanding biogeographical distributions, ocean surface habitat mapping, and ocean surface stability. This article describes the Ocean-surface Heterogeneity MApping (OHMA) algorithm—an objective, replicable approach that uses hypertemporal, satellite-derived datasets to map the spatio-temporal heterogeneity of ocean surface waters. The OHMA produces a suite of complementary datasets—a surface spatio-temporal heterogeneity dataset, and an optimised spatio-temporal classification of the ocean surface. It was demonstrated here using a hypertemporal Sea Surface Temperature image dataset of the North Atlantic. Validation with Underway-derived temperature data showed higher heterogeneity areas were associated with stronger surface temperature gradients, or an increased presence of locally extreme temperature values. Using four exploratory case studies, spatio-temporal heterogeneity values were related to a range of region-specific surface and sub-surface characteristics including fronts, currents and bathymetry. The values conveyed the interactions between these parameters as a single metric. Such over-arching heterogeneity information is virtually impossible to map from in-situ instruments, or less temporally dense satellite datasets. This study demonstrated the OHMA approach is a useful and robust tool to explore, examine, and describe the ocean’s surface. It advances our capability to map biologically relevant measures of ocean surface heterogeneity. It can support ongoing efforts in Ocean Surface Partitioning, and attempts to understand marine species distributions. The study highlighted the need to establish dedicated spatio-temporal ocean validation sites, specifically measured using surface transits, to support advances in hypertemporal ocean data use, and exploitation. A number of future research avenues are also highlighted.

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Section: Discussionmentioning

confidence: 99%

Section: Hypertemporal Opportunitiesmentioning

confidence: 99%

Section: Ohma Implementationmentioning

confidence: 99%

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Ocean-Surface Heterogeneity Mapping (OHMA) to Identify Regions of Change

et al. 2021

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“…An uncommon phenomenon was experienced with the PCA: application of PCs usually results in higher model performance, as has been proven in different studies [104][105][106][107]. However, in this case, the PCA was not a successful alternative.…”

Section: Variable Selection Number Of Variables and The Issue Of Overfitmentioning

confidence: 97%

Uncertainty and Overfitting in Fluvial Landform Classification Using Laser Scanned Data and Machine Learning: A Comparison of Pixel and Object-Based Approaches

Szabó¹,

Mikita²,

Négyesi³

et al. 2020

Remote Sensing

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Floodplains are valuable scenes of water management and nature conservation. A better understanding of their geomorphological characteristic helps to understand the main processes involved. We performed a classification of floodplain forms in a naturally developed area in Hungary using a Digital Terrain Model (DTM) of aerial laser scanning. We derived 60 geomorphometric variables from the DTM and prepared a geomorphological map of 265 forms (crevasse channels, point bars, swales, levees). Random Forest classification was conducted with Recursive Feature Elimination (RFE) on the objects (mean pixel values by forms) and on the pixels of the variables. We also evaluated the classification probabilities (CP), the spatial uncertainties (SU), and the overfitting in the function of the number of the variables. We found that the object-based method had a better performance (95%) than the pixel-based method (78%). RFE helped to identify the most important 13–20 variables, maintaining the high model performance and reducing the overfitting. However, CP and SU were not efficient measures of classification accuracy as they were not in accordance with the class level accuracy metric. Our results help to understand classification results and the specific limits of laser scanned DTMs. This methodology can be useful in geomorphologic mapping.

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“…The present work aims to reduce the gap between near-polar orbiting and geostationary mission temporal resolutions, creating a Level 3 hyper-temporal analysis-ready OC dataset [27,28], containing observations provided by the currently functioning near-polar orbiting OC imagers.…”

Section: Introductionmentioning

confidence: 99%

A Virtual Geostationary Ocean Color Sensor to Analyze the Coastal Optical Variability

et al. 2020

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In the coastal environment the optical properties can vary on temporal scales that are shorter than the near-polar orbiting satellite temporal resolution (~1 image per day), which does not allow capturing most of the coastal optical variability. The objective of this work is to fill the gap between the near-polar orbiting and geostationary sensor temporal resolutions, as the latter sensors provide multiple images of the same basin during the same day. To do that, a Level 3 hyper-temporal analysis-ready Ocean Color (OC) dataset, named Virtual Geostationary Ocean Color Sensor (VGOCS), has been created. This dataset contains the observations acquired over the North Adriatic Sea by the currently functioning near-polar orbiting sensors, allowing approaching the geostationary sensor temporal resolution. The problem in using data from different sensors is that they are characterized by different uncertainty sources that can introduce artifacts between different satellite images. Hence, the sensors have different spatial and spectral resolutions, their calibration procedures can have different accuracies, and their Level 2 data can be retrieved using different processing chains. Such differences were reduced here by adjusting the satellite data with a multi-linear regression algorithm that exploits the Fiducial Reference Measurements data stream of the AERONET-OC water-leaving radiance acquired at the Acqua Alta Oceanographic Tower, located in the Gulf of Venice. This work aims to prove the suitability of VGOCS in analyzing the coastal optical variability, presenting the improvement brought by the adjustment on the quality of the satellite data, the VGOCS spatial and temporal coverage, and the inter-sensor differences. Hence, the adjustment will strongly increase the agreement between the satellite and in situ data and between data from different near-polar orbiting OC imagers; moreover, the adjustment will make available data traditionally masked in the standard processing chains, increasing the VGOCS spatial and temporal coverage, fundamental to analyze the coastal optical variability. Finally, the fulfillment by VGOCS of the three conditions for a hyper-temporal dataset will be demonstrated in this work.

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From Land to Sea, a Review of Hypertemporal Remote Sensing Advances to Support Ocean Surface Science

Cited by 5 publications

References 98 publications

Ocean-Surface Heterogeneity Mapping (OHMA) to Identify Regions of Change

Ocean-Surface Heterogeneity Mapping (OHMA) to Identify Regions of Change

Uncertainty and Overfitting in Fluvial Landform Classification Using Laser Scanned Data and Machine Learning: A Comparison of Pixel and Object-Based Approaches

A Virtual Geostationary Ocean Color Sensor to Analyze the Coastal Optical Variability

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