Abstract
Remote sensing data provide a huge number of sea surface observations, but cannot give direct information on deeper ocean layers, which can only be provided by sparse in situ data. The combination of measurements collected by satellite and in situ sensors represents one of the most effective strategies to improve our knowledge of the interior structure of the ocean ecosystems. In this work, we describe a Multi-Layer-Perceptron (MLP) network designed to reconstruct the 3D fields of ocean temperature and chlorophyll-a concentration, two variables of primary importance for many upper-ocean bio-physical processes. Artificial neural networks can efficiently model eventual non-linear relationships among input variables, and the choice of the predictors is thus crucial to build an accurate model. Here, concurrent temperature and chlorophyll-a in situ profiles and several different combinations of satellite-derived surface predictors are used to identify the optimal model configuration, focusing on the Mediterranean Sea. The lowest errors are obtained when taking in input surface chlorophyll-a, temperature, and altimeter-derived absolute dynamic topography and surface geostrophic velocity components. Network training and test validations give comparable results, significantly improving with respect to Mediterranean climatological data (MEDATLAS). 3D fields are then also reconstructed from full basin 2D satellite monthly climatologies (1998–2015) and resulting 3D seasonal patterns are analyzed. The method accurately infers the vertical shape of temperature and chlorophyll-a profiles and their spatial and temporal variability. It thus represents an effective tool to overcome the in-situ data sparseness and the limits of satellite observations, also potentially suitable for the initialization and validation of bio-geophysical models.
Highlights
In recent years, the assessment and monitoring of the marine environmental status has received ever-growing attention, due to the potentially critical impact of ongoing natural and human-induced changes on related ecosystem functioning and services
The test set represents 30% of the total database (364 stations accounting for 53,872 single depth individual data values)
The combination of satellite and in situ observations to extrapolate information on the deeper layers through artificial intelligence tools/deep learning approaches is expected to play a key role in the future
Summary
The assessment and monitoring of the marine environmental status has received ever-growing attention, due to the potentially critical impact of ongoing natural and human-induced changes on related ecosystem functioning and services. Most of the existing data, especially those providing biological/bio-optical/bio-chemical parameters, are collected by in situ sampling either through coastal monitoring programs and time-limited oceanographic cruises, or by fixed platform, as moored buoys, and by autonomous instruments, such as Biogeochemical Argo (BGC-Argo) [1,2,3,4,5,6] These data are eventually able to provide accurate descriptions of local conditions along the water column, but they are clearly not sufficient to describe the processes occurring over the wide range of temporal and spatial scales involved/affected by undergoing changes [7]. Satellite observations can only reveal the signals integrated between the surface and the first optical depth, sensing even shallower layers [15]
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