This study aimed to understand which vegetation indices (VIs) are an ideal proxy for describing phenology and interannual variability of Gross Primary Productivity (GPP) in short-rotation coppice (SRC) plantations. Canopy structure- and chlorophyll-sensitive VIs derived from Sentinel-2 images were used to estimate the start and end of the growing season (SOS and EOS, respectively) during the period 2016–2018, for an SRC poplar (Populus spp.) plantation in Lochristi (Belgium). Three different filtering methods (Savitzky–Golay (SavGol), polynomial (Polyfit) and Harmonic Analysis of Time Series (HANTS)) and five SOS- and EOS threshold methods (first derivative function, 10% and 20% percentages and 10% and 20% percentiles) were applied to identify the optimal methods for the determination of phenophases. Our results showed that the MEdium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) had the best fit with GPP phenology, as derived from eddy covariance measurements, in identifying SOS- and EOS-dates. For SOS, the performance was only slightly better than for several other indices, whereas for EOS, MTCI performed markedly better. The relationship between SOS/EOS derived from GPP and VIs varied interannually. MTCI described best the seasonal pattern of the SRC plantation’s GPP (R² = 0.52 when combining all three years). However, during the extreme dry year 2018, the Chlorophyll Red Edge Index performed slightly better in reproducing growing season GPP variability than MTCI (R² = 0.59; R² = 0.49, respectively). Regarding smoothing functions, Polyfit and HANTS methods showed the best (and very similar) performances. We further found that defining SOS as the date at which the 10% or 20% percentile occurred, yielded the best agreement between the VIs and the GPP; while for EOS the dates of the 10% percentile threshold came out as the best.

The processes involved in the exchange of water, energy and carbon in terrestrial ecosystems are strongly intertwined. To accurately represent the terrestrial biosphere in land surface models (LSMs), the intrinsic coupling between these processes is required. Soil moisture and leaf area index (LAI) are two key variables at the nexus of water, energy and vegetation. Here, we evaluated two prognostic LSMs (ISBA and ORCHIDEE) and a diagnostic model (based on the LSA SAF, Satellite Application Facility for Land Surface Analysis, algorithms) in their ability to simulate the latent heat flux (LE) and gross primary production (GPP) coherently and their interactions through LAI and soil moisture. The models were validated using in situ eddy covariance observations, soil moisture measurements and remote-sensing-based LAI. It was found that the diagnostic model performed consistently well, regardless of land cover, whereas important shortcomings of the prognostic models were revealed for herbaceous and dry sites. Despite their different architecture and parametrization, ISBA and ORCHIDEE shared some key weaknesses. In both models, LE and GPP were found to be oversensitive to drought stress. Though the simulated soil water dynamics could be improved, this was not the main cause of errors in the surface fluxes. Instead, these errors were strongly correlated to errors in LAI. The simulated phenological cycle in ISBA and ORCHIDEE was delayed compared to observations and failed to capture the observed seasonal variability. The feedback mechanism between GPP and LAI (i.e. the biomass allocation scheme) was identified as a key element to improve the intricate coupling between energy, water and vegetation in LSMs.

Monitoring soil moisture at the Earth’surface is of great importance for drought early warnings. Spaceborne remote sensing is a keystone in monitoring at continental scale, as satellites can make observations of locations which are scarcely monitored by ground-based techniques. In recent years, several soil moisture products for continental scale monitoring became available from the main space agencies around the world. Making use of sensors aboard polar satellites sampling in the microwave spectrum, soil moisture can be measured and mapped globally every few days at a spatial resolution as fine as 25 km. However, complementarity of satellite observations is a crucial issue to improve the quality of the estimations provided. In this context, measurements within the visible and infrared from geostationary satellites provide information on the surface from a totally different perspective. In this study, we design a new retrieval algorithm for daily soil moisture monitoring based only on the land surface temperature observations derived from the METEOSAT second generation geostationary satellites. Soil moisture has been retrieved from the retrieval algorithm for an eight years period over Europe and Africa at the SEVIRI sensor spatial resolution (3 km at the sub-satellite point). The results, only available for clear sky and partly cloudy conditions, are for the first time extensively evaluated against in-situ observations provided by the International Soil Moisture Network and FLUXNET at sites across Europe and Africa. The soil moisture retrievals have approximately the same accuracy as the soil moisture products derived from microwave sensors, with the most accurate estimations for semi-arid regions of Europe and Africa, and a progressive degradation of the accuracy towards northern latitudes of Europe. Although some possible improvements can be expected by a better use of other products derived from SEVIRI, the new approach developped and assessed here is a valuable alternative to microwave sensors to monitor daily soil moisture at the resolution of few kilometers over entire continents and could reveal a good complementarity to an improved monitoring system, as the algorithm can produce surface soil moisture with less than 1 day delay over clear sky and non-steady cloudy conditions (over 10% of the time).

Evapotranspiration (ET) is a key parameter in the description of the energy and water fluxes over land. Continuous and spatially detailed ET simulations are thus required for a number of scientific and management-related purposes. These conditions are determined by the modelling approach and the composition of the forcing dataset. This study aimed at simulating daily ET in a diversity of climate and land cover conditions at a spatial resolution of ∼1 km and higher. The modelling approach was based on the algorithm driving the ET product developed and set in operations in the framework of the Satellite Application Facility on Land Surface Analysis programme (LSA-SAF). The implemented algorithm allowed the ingestion of biophysical parameters derived from SPOT-V and PROBA-V observations developed by the Copernicus Global Land Programme, as well as other model parameters at a similar spatial resolution. The model was tested at an ∼1 km spatial resolution in over 40 sites located in different climate and land cover contexts. The implementation at ∼300 m was tested in the upper Biebrza basin, in Poland. The simulations correlated well with the validation dataset (r2 > 0.75 in 80% of sites) and exhibited root mean squared values lower than 1 mm/day in 80% of the cases. The results also pointed to the need for refining the accuracy of soil moisture data sources, especially in dry areas. The results showed the ability of the modelling approach and the SPOT-V/PROBA-V missions to support the generation of long ET time series. They also opened the gate to incorporate Sentinel-3 in ET continuous modelling.

Evapotranspiration (ET) estimates based on remote sensing are constrained by the spatial and temporal resolution of spaceborne observations. Therefore, the choice to be made is whether modelling at high temporal resolution with geostationary satellites at the cost of the spatial detail, or spatially detailed instantaneous modelling at overpass-time with polar-orbit satellites. This study aimed at exploiting the strength of both modelling approaches and derive daily ET estimates with cell size 1.0 km. The study was conducted in four sites with different land cover arrangements and catalogued as wetlands. The methodological approach was based on the algorithm behind the operational ET product of the Satellite Application Facility on Land Surface Analysis (LSA-SAF) initiative. The examined period comprised the end of the Spot-Végétation (Spot-V) mission and the first year of Proba-V operational modus. Two aspects were investigated: 1. The adequacy of combining observations from geostationary and Spot-V and Proba-V satellite missions for obtaining the target product; and, 2. The intercompatibility of ET estimates based on Spot-V and Proba-V so that continuous series over the operational period of both missions could be built. The comparison of simulated ET with in situ measured fluxes (eddy covariance) showed that the introduction of more spatial detail through polar-satellite observations resulted in the reduction of bias and root-mean-square error in ET estimates, as compared with estimations where Earth observation data were taken from geostationary satellites only. Moreover, the signals derived from Spot-V and Proba-V exhibited similarity during the period of operational overlap; especially in non-cultivated surfaces. This suggests that continuity objective of these two missions can be exploited in the estimation of ET time series.