Alberto Baudena , Wilhem Riom , Vincent Taillandier , Nicolas Mayot , Alexandre Mignot , Fabrizio D’Ortenzio
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引用次数: 0
Abstract
Ocean primary production is a key process that regulates marine ecosystems and the global climate, but its estimation is still affected by multiple uncertainties. Typically, the chlorophyll-a concentration (CHL) is used to characterise this process, as it is considered as a proxy of phytoplankton biomass. To date, the most common observing systems for studying CHL are ocean colour satellites and Biogeochemical-Argo (BGC-Argo) floats. These are complementary systems: satellite observations provide global coverage but are limited to the ocean surface, while BGC-Argo floats provide punctual observations along the whole water column. Quantitative matching of these two observing systems has been obtained only at regional or single-float scales, while at a global scale the relatively low and irregular BGC-Argo coverage results in large uncertainties. Here, we propose a different method, by comparing satellite and BGC-Argo climatological annual time series within seven different bioregions, each characterised by a homogeneous phytoplankton phenology, allowing us to smooth the uncertainties. By comparing the mean values, amplitudes, and shapes of the two time series, we identify regions (a) where they agree (58%–61% of the ocean surface area); (b) regions undersampled by the BGC-Argo float network (particularly in the Arabian Sea and near the Amazon delta); (c) where the discrepancy may stem from satellite or (d) BGC-Argo performance (mainly found at subtropical and high latitudes, respectively). Caution is required when using BGC-Argo and satellite data in regions b–d, and, for each region, we provide suggestions on which system could be affected by the largest uncertainties.
期刊介绍:
Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing.
The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques.
RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.