Next Article in Journal
Mapping up-to-Date Paddy Rice Extent at 10 M Resolution in China through the Integration of Optical and Synthetic Aperture Radar Images
Next Article in Special Issue
LDAS-Monde Sequential Assimilation of Satellite Derived Observations Applied to the Contiguous US: An ERA-5 Driven Reanalysis of the Land Surface Variables
Previous Article in Journal
Estimates of the Change in the Oceanic Precipitation Off the Coast of Europe due to Increasing Greenhouse Gas Emissions
Previous Article in Special Issue
Quantifying Drought Propagation from Soil Moisture to Vegetation Dynamics Using a Newly Developed Ecohydrological Land Reanalysis
Open AccessArticle

Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area

Centre National de Recherches Meteorologiques, UMR3589 (CNRS, Météo-France), 31057 Toulouse, France
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(8), 1199; https://doi.org/10.3390/rs10081199
Received: 25 June 2018 / Revised: 26 July 2018 / Accepted: 27 July 2018 / Published: 31 July 2018
(This article belongs to the Special Issue Assimilation of Remote Sensing Data into Earth System Models)
Within a global Land Data Assimilation System (LDAS-Monde), satellite-derived Surface Soil Moisture (SSM) and Leaf Area Index (LAI) products are jointly assimilated with a focus on the Euro-Mediterranean region at 0.5 resolution between 2007 and 2015 to improve the monitoring quality of land surface variables. These products are assimilated in the CO2 responsive version of ISBA (Interactions between Soil, Biosphere and Atmosphere) land surface model, which is able to represent the vegetation processes including the functional relationship between stomatal aperture and photosynthesis, plant growth and mortality (ISBA-A-gs). This study shows the positive impact on SSM and LAI simulations through assimilating their satellite-derived counterparts into the model. Using independent flux estimates related to vegetation dynamics (evapotranspiration, Sun-Induced Fluorescence (SIF) and Gross Primary Productivity (GPP)), it is also shown that simulated water and CO2 fluxes are improved with the assimilation. These vegetation products tend to have higher root-mean-square deviations in summer when their values are also at their highest, representing 20–35% of their absolute values. Moreover, the connection between SIF and GPP is investigated, showing a linear relationship depending on the vegetation type with correlation coefficient values larger than 0.8, which is further improved by the assimilation. View Full-Text
Keywords: land surface model; land data assimilation system; accuracy; fluorescence land surface model; land data assimilation system; accuracy; fluorescence
Show Figures

Graphical abstract

MDPI and ACS Style

Leroux, D.J.; Calvet, J.-C.; Munier, S.; Albergel, C. Using Satellite-Derived Vegetation Products to Evaluate LDAS-Monde over the Euro-Mediterranean Area. Remote Sens. 2018, 10, 1199.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Back to TopTop