Mapping the Intertidal Microphytobenthos Gross Primary Production Part I: Coupling Multispectral Remote Sensing and Physical Modeling

Type Article
Date 2020-07
Language English
Author(s) Méléder Vona1, Savelli Raphael2, Barnett Alexandre1, 2, Polsenaere PierreORCID3, Gernez Pierre1, Cugier Philippe4, Lerouxel Astrid1, Le Bris Anthony1, 5, Dupuy Christine2, Le Fouest Vincent2, Lavaud Johann6, 7
Affiliation(s) 1 : Mer Molécules Santé (EA 21 60), Université de Nantes, Nantes, France
2 : LIENSs ‘Littoral ENvironnement et Sociétés’ UMR 7266, Institut du Littoral et de l’Environnement, CNRS/Université de La Rochelle, La Rochelle, France
3 : Laboratoire Environnement Ressources des Pertuis Charentais (LER-PC), Ifremer, L’Houmeau, France
4 : Département Dynamiques de l’Environnement Côtier, Laboratoire d’Ecologie Benthique, Ifremer, Plouzané, France
5 : Centre d’Etude et de Valorisation des Algues (CEVA), Pleubian, France
6 : Takuvik Joint International Laboratory UMI3376, CNRS (France) & ULaval (Canada), Département de Biologie, Université Laval, Québec, QC, Canada
7 : Takuvik Joint International Laboratory UMI3376, CNRS (France) & ULaval (Canada), Département de Biologie, Université Laval, Québec, QC, Canada
Source Frontiers In Marine Science (2296-7745) (Frontiers Media SA), 2020-07 , Vol. 7 , P. 520 (16p.)
DOI 10.3389/fmars.2020.00520
WOS© Times Cited 2
Keyword(s) microphytobenthos, intertidal mudflat, gross primary production, remote sensing, NDVI, modeling
Abstract

The gross primary production (GPP) of intertidal mudflat microphytobenthos supports important ecosystem services such as shoreline stabilization and food production, and it contributes to blue carbon. However, monitoring microphytobenthos GPP over a long-term and large spatial scale is rendered difficult by its high temporal and spatial variability. To overcome this issue, we developed an algorithm to map microphytobenthos GPP in which the following are coupled: (i) NDVI maps derived from high spatial resolution satellite images (SPOT6 or Pléiades), estimating the horizontal distribution of the microphytobenthos biomass; (ii) emersion time, photosynthetically active radiation (PAR), and mud surface temperature simulated from the physical model MARS-3D; (iii) photophysiological parameters retrieved from Production–irradiance (P–E) curves, obtained under controlled conditions of PAR and temperature, using benthic chambers, and expressing the production rate into mg C h–1 m–2 ndvi–1. The productivity was directly calibrated to NDVI to be consistent with remote-sensing measurements of microphytobenthos biomass and was spatially upscaled using satellite-derived NDVI maps acquired at different seasons. The remotely sensed microphytobenthos GPP reasonably compared with in situ GPP measurements. It was highest in March with a daily production reaching 50.2 mg C m–2 d–1, and lowest in July with a daily production of 22.3 mg C m–2 d–1. Our remote sensing algorithm is a new step in the perspective of mapping microphytobenthos GPP over large mudflats to estimate its actual contribution to ecosystem functions, including blue carbon, from local and global scales.

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How to cite 

Méléder Vona, Savelli Raphael, Barnett Alexandre, Polsenaere Pierre, Gernez Pierre, Cugier Philippe, Lerouxel Astrid, Le Bris Anthony, Dupuy Christine, Le Fouest Vincent, Lavaud Johann (2020). Mapping the Intertidal Microphytobenthos Gross Primary Production Part I: Coupling Multispectral Remote Sensing and Physical Modeling. Frontiers In Marine Science, 7, 520 (16p.). Publisher's official version : https://doi.org/10.3389/fmars.2020.00520 , Open Access version : https://archimer.ifremer.fr/doc/00643/75531/