Garisoain R., Delire C., Decharme B., Ferrant Sylvain, Granouillac F., Payre-Suc V., Gandois L. (2023). A study of dominant vegetation phenology in a sphagnum mountain peatland using in situ and Sentinel-2 observations. Journal of Geophysical Research : Biogeosciences, 128 (10), p. e2023JG007403 [16 p.]. ISSN 2169-8953.
Titre du document
A study of dominant vegetation phenology in a sphagnum mountain peatland using in situ and Sentinel-2 observations
Garisoain R., Delire C., Decharme B., Ferrant Sylvain, Granouillac F., Payre-Suc V., Gandois L.
Source
Journal of Geophysical Research : Biogeosciences, 2023,
128 (10), p. e2023JG007403 [16 p.] ISSN 2169-8953
Peatlands store more than a third of the global soil organic carbon stock. Bryophytes, more specifically Sphagnum mosses, play a major role in the carbon and water cycles of these ecosystems. There is a need to include Sphagnum mosses into Earth system models to better simulate the dynamics of peatlands in a changing environment. Leaf area index (LAI) is a key plant trait that characterizes the plant photosynthesizing capacity. Moreover, LAI is a variable calculated by land surface models used in climate models, allowing control of the exchange of matter and energy between vegetation and the environment. There is extremely little data on Sphagnum LAI and none on its seasonal change. We monitored Sphagnum mosses LAI phenology in a mountainous peatland site (altitude of 1,343 m) from June to December 2021 using two methods: 2D scans of monthly Sphagnum moss samples and analysis of Sentinel-2 images. LAI derived from field campaigns and the remote sensing approach show a strong seasonality, with high peak values reaching 10 and 7 m(2)m (-2), respectively. The Sentinel-2 images were also used to derive common vegetation indices. The moisture soil index effectively discriminates Sphagnum-dominated areas in the peatland. Satellite-derived LAI of Sphagnum mosses is directly correlated to gross primary production monitored by gas exchange measurements (R-2 = 0.83) but also to physical drivers of the environment such as air temperature (R-2 = 0.74) or water table depth (R-2 = 0.61) over the 2017-2021 period. It is therefore highly suitable to investigate ecosystemic functions.
Plan de classement
Sciences du milieu [021]
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Sciences du monde végétal [076]
;
Etudes, transformation, conservation du milieu naturel [082]
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Télédétection [126]
