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

Mapping the Intertidal Microphytobenthos Gross Primary Production, Part II: Merging Remote Sensing and Physical-Biological Coupled Modeling

“…The GPP simulated in the MARS-3D season run model is much higher than GPP measured in situ. Over the mudflat, the GPP simulated in the MARS-3D season run is also higher than the GPP derived from the GPP-algo developed by Méléder et al (2020) for the whole mudflat. However, both the model (MARS-3D season ) and the remote sensing algorithm provide hourly and daily GPP rates in the range of GPP values reported in the literature (Cahoon, 1999;Underwood and Kromkamp, 1999;Daggers et al, 2018).…”

“…The Chl a concentration was normalized to the sampled surface (1.5 cm 2 ) to be expressed in mg Chl a m −2 . The sampling protocol is fully detailed in Méléder et al (2020).…”

“…The GPP-algo developed by Méléder et al (2020) is parameterized with synoptic measurements of the photosynthetic activity and the related photophysiological parameters (P b MAX and also the optimal irradiance for photosynthesis and the initial slope of the production-irradiance relationship) of MPB cells collected during the field campaigns. It is therefore well-suited to depict the high temporal variability of the MPB photosynthetic response to the physical environment.…”

“…Daggers et al (2018) first coupled in situ measurements, satellite remote sensing data, and observed tidal heights to map synoptic MPB PP in spring at the scale of the Oosterschelde and Westerschelde estuaries (The Netherlands). Méléder et al (2020) coupled in situ measurements, satellite remote sensing data, and data simulated (light, MST, and tidal height) by a 3D physical-biological coupled model to map synoptic MPB PP at three seasons over the large intertidal Brouage mudflat on the French Atlantic Coast.…”

“…Such a comparison does not exist for MPB PP. The recent development for the Brouage mudflat of a regional MPB Gross Primary Production (GPP) algorithm (Méléder et al, 2020) and of a 1D MPB GPP physical-biological coupled model (Savelli et al, 2018) allows for the comparison of MPB GPP estimates derived from space remote sensing and a prognostic modeling approach. The objective of this study is to infer the capacity of remote sensing and prognostic modeling to converge toward realistic MPB GPP estimates over the large Brouage mudflat.…”