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MOnitoring VEgetation status and functioning at high spatio-temporal resolution from Sentinel-2

Project description

Two satellites are better than one: higher resolution Earth imaging for crop and forest monitoring

Imaging satellites for Earth observation provide important spatial and temporal information of relevance to numerous fields including meteorology, oceanography, agriculture, conservation, regional planning, intelligence and defence. When it comes to crop and forest management, higher resolution is urgently needed to monitor biophysical variables including leaf area index and the fraction of absorbed photosynthetically active radiation. Current technologies fall short when trying to balance spatial versus temporal resolution. The EU-funded MOVES project is developing an operational algorithm to retrieve the necessary information at required resolution harnessing Sentinel-2 satellites, a constellation of two identical satellites in the same orbit, with a spatial resolution of 10–20 metres and 5-day temporal sampling.


Leaf Area Index (LAI), Fraction of green Vegetation Cover (FCOVER) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are key biophysical variables representing the status and functioning of vegetation. High spatiotemporal resolution LAI/FAPAR/FCOVER products are urgently needed in many terrestrial applications including crop and forest management. However, the trade-off in traditional remote sensing sensors between temporal and spatial resolutions hinders the generation of such products. The launch of Sentinel-2 satellites, with spatial resolution of 10-20 m and 5-day temporal sampling (in tandem), opens a new paradigm in satellite vegetation monitoring. The proposed project “MOVES” will develop an operational algorithm for retrieving LAI/FAPAR/FCOVER from Sentinel-2 data. An easily-invertible radiative transfer model (RTM) will be firstly developed, which will apply a universal model framework for all vegetation types (continuous vs discrete) and terrains (horizontal vs sloping). In this project, the hybrid training and domain adaption paradigms will be introduced into the retrieval of LAI/FAPAR/FCOVER, to enhance the transferability of the retrieval algorithm and achieve spatiotemporally consistent retrieval. The Copernicus ground-based observations (GBOC) and FLUXNET sites will be used to validate the proposed algorithm and assess its potential in the monitoring of vegetation status and functioning. The project is conceived to combine the prominent expertise of the hosting institute in biophysical variable retrieval and remote sensing ecological application, with my well-demonstrated RTM development skills. Overall, MOVES will facilitate the delivery of Sentinel-2 LAI/FAPAR/FCOVER products of physical consistence and high accuracy, and underpin new avenues for the development of high spatiotemporal frequency vegetation monitoring systems.


Net EU contribution
€ 172 932,48
08193 Bellaterra

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Este Cataluña Barcelona
Activity type
Research Organisations
Total cost
€ 172 932,48