Abstract
Increases in the temporal frequency of satellite-derived imagery mean a greater diversity of ocean surface features can be studied, modelled, and understood. The ongoing temporal data “explosion” is a valuable resource, having prompted the development of adapted and new methodologies to extract information from hypertemporal datasets. Current suitable methodologies for use in hypertemporal ocean surface studies include using pixel-centred measurement analyses (PMA), classification analyses (CLS), and principal components analyses (PCA). These require limited prior knowledge of the system being measured. Time-series analyses (TSA) are also promising, though they require more expert knowledge which may be unavailable. Full use of this resource by ocean and fisheries researchers is restrained by limitations in knowledge on the regional to sub-regional spatiotemporal characteristics of the ocean surface. To lay the foundations for more expert, knowledge-driven research, temporal signatures and temporal baselines need to be identified and quantified in large datasets. There is an opportunity for data-driven hypertemporal methodologies. This review examines nearly 25 years of advances in exploratory hypertemporal research, and how methodologies developed for terrestrial research should be adapted when tasked towards ocean applications. It highlights research gaps which impede methodology transfer, and suggests achievable research areas to be addressed as short-term priorities.
Highlights
Single-date and multi-date remote sensing imagery are widely used in support of oceanographic and fisheries research and monitoring
Bracketed numbers link to the reference list of this review, to aid the reader identify the specific publication of interest to them
It hints at the oceanographic knowledge potential which would be unlocked if hypertemporal studies first focused on addressing the knowledge gap on the temporal diversity of the ocean surface
Summary
Single-date and multi-date remote sensing imagery are widely used in support of oceanographic and fisheries research and monitoring. Piwowar and LeDrew [1] were amongst the first published authors to use the term hypertemporal ( referred to as “hyper-temporal”, or “high temporal resolution”) with respect to satellite remote sensing data (hereafter referred to as Earth Observation, or EO, data) They highlighted the need for new hypertemporal image analysis approaches to process temporal signals in these datasets in a spatially coherent manner. Regarding hypertemporal estimate techniques and algorithms, and draw upon the pool of existing methods, adapted for use on large, temporally orientated datasets (see Figure 2) Despite their efforts and the increasingly frequent reference to hypertemporal data in the scientific literature, the term is still somewhat ill-defined. Bracketed numbers link to the reference list of this review, to aid the reader identify the specific publication of interest to them
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