Climate Data Records of Vegetation Variables from Geostationary SEVIRI/MSG Data: Products, Algorithms and Applications

Abstract

The scientific community requires long-term data records with well-characterized uncertainty and suitable for modeling terrestrial ecosystems and energy cycles at regional and global scales. This paper presents the methodology currently developed in EUMETSAT within its Satellite Application Facility for Land Surface Analysis (LSA SAF) to generate biophysical variables from the Spinning Enhanced Visible and InfraRed Imager (SEVIRI) on board MSG 1-4 (Meteosat 8-11) geostationary satellites. Using this methodology, the LSA SAF generates and disseminates at a time a suite of vegetation products, such as the leaf area index (LAI), the fraction of the photosynthetically active radiation absorbed by vegetation (FAPAR) and the fractional vegetation cover (FVC), for the whole Meteosat disk at two temporal frequencies, daily and 10-days. The FVC algorithm relies on a novel stochastic spectral mixture model which addresses the variability of soils and vegetation types using statistical distributions whereas the LAI and FAPAR algorithms use statistical relationships general enough for global applications. An overview of the LSA SAF SEVIRI/MSG vegetation products, including expert knowledge and quality assessment of its internal consistency is provided. The climate data record (CDR) is freely available in the LSA SAF, offering more than fifteen years (2004-present) of homogeneous time series required for climate and environmental applications. The high frequency and good temporal continuity of SEVIRI products addresses the needs of near-real-time users and are also suitable for long-term monitoring of land surface variables. The study also evaluates the potential of the SEVIRI/MSG vegetation products for environmental applications, spanning from accurate monitoring of vegetation cycles to resolving long-term changes of vegetation.

A Simple Algorithm for Deriving an NDVI-Based Index Compatible between GEO and LEO Sensors: Capabilities and Limitations in Japan

“…To the best of our knowledge, only a few studies have discussed differences in FVC data obtained using GEO and LEO sensors. A key work on this topic was a series of studies on the MSG SEVIRI FVC data products [7], which determine FVC using an algorithm based on the stochastic spectral mixture model. In that series of studies, the MSG SEVIRI FVC data were compared with those of LEO's FVC products, with SPOT VEGETATION data as a reference [7].…”

“…A key work on this topic was a series of studies on the MSG SEVIRI FVC data products [7], which determine FVC using an algorithm based on the stochastic spectral mixture model. In that series of studies, the MSG SEVIRI FVC data were compared with those of LEO's FVC products, with SPOT VEGETATION data as a reference [7]. The SPOT FVC was based on a neural network that was trained to generate the best estimates of LAI, fraction of absorbed photosynthetically active radiation (FAPAR), and FVC from the fused and scaled MODIS and Carbon Cycle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) products [64].…”

“…The SPOT FVC was based on a neural network that was trained to generate the best estimates of LAI, fraction of absorbed photosynthetically active radiation (FAPAR), and FVC from the fused and scaled MODIS and Carbon Cycle and Change in Land Observational Products from an Ensemble of Satellites (CYCLOPES) products [64]. Although the MSG SEVIRI and SPOT VEGETATION approaches yielded consistent results of FVC for a site in Gabon (Figure 11 in [7]), the algorithms implemented to retrieve FVC were not identical. Our intention in the present work was to compare FVCs (NDVI-based indices in this study) obtained using GEO and LEO sensors with an identical algorithm.…”

“…The sensor provides earth observation data across 12 spectral channels, including the visible and near-infrared (NIR) regions, with 3 km spatial sampling at sub-satellite points and a 15 min temporal resolution [4]. The SEVIRI data have been widely used for phenological monitoring [5], drought monitoring [6], and calculating vegetation products involving the leaf area index (LAI) and fraction of vegetation cover (FVC) [7].…”

Potencial del producto SEVIRI/MSG GPP en la detección de zonas afectadas por estrés hídrico

“…Estos productos están basados en datos de los diferentes satélites de EUMETSAT, como el sensor SEVIRI (Spinning Enhanced Visible and InfraRed Imager) a bordo de la plataforma MSG (Meteosat Second Generation) o el sensor AVHRR (Advanced Very High Resolution Radiometer) a bordo del sistema EPS (EUMETSAT Polar System). Desde 2008 se dispone de los productos: índice de superficie foliar (LAI, leaf area index), la fracción de radiación fotosintéticamente activa que es absorbida por la vegetación (f APAR , fraction of absorbed photosynthetically active radiation) y la fracción de cubierta vegetal (FVC, fractional vegetation cover) basado en datos SEVIRI/MSG (García-Haro et al, 2019). Este conjunto de datos se completa con los recientes productos de vegetación derivados a partir del sistema polar AVHRR/EPS (García-Haro et al, 2018).…”