The status and functioning of vegetation play key roles in many biophysical processes, such as photosynthesis and transpiration, and therefore significantly influence the global climate system and carbon cycle. Accurate estimation of canopy biophysical parameters is the prerequisite for a wide range of agricultural, ecological, hydrological and meteorological applications. LAI, FAPAR and FCOVER are three widely used biophysical parameters. LAI is defined as one-half the total area of green leaves per unit area of horizontal ground, and it controls the exchanges of energy, water, and greenhouse gases between the land surface and the atmosphere. FAPAR is dened as the fraction of radiation absorbed by the canopy in the 400-700 nm spectral domain and is a main input in models of light-use efficiency to estimate Gross Primary Productivity (GPP). The FCOVER, which is defined as the fraction of the background covered by green vegetation as seen from the nadir, is also a pertinent variable that can be used in models of the surface-energy balance to separate the contribution of the soil from that of the canopy.
High spatiotemporal resolution LAI/FAPAR/FCOVER products are urgently needed to characterize terrestrial ecosystem processes, supporting crop management activities at the parcel level and for better understanding land greening and browning phenomena at local scales. The overall objective of this project is to develop a robust, efficient and highly accurate LAI/FAPAR/FCOVER retrieval algorithm for Sentinel-2 data and therefore achieve the operational monitoring of vegetation structure and functioning at a high spatiotemporal frequency.
Due to the adverse impact of Covid-19, it is very hard to implement the project fully following the WP. For example, the time spending on field measurement, secondment and conference was significantly reduced because of the strict confinement. However, our project still achieved fruitful results. Main project results include:
(1) Proposed an integrated method for Sentinel-2 topographic and angular normalization, which could estimate the intrinsic vegetation reflectances and improve retrieval accuracy of biophysical parameters.
(2) Validated decametric (Sentinel-2) and kilometric LAI (MODIS, CGLS, SAF, GLASS and C3S) product in Maize Crops, which informed the retrieval algorithm improvement.
(3) Proposed a multiband constraint harmonization method for combined use of Sentinel-2 and Landsat 8, which contributes to generate spatiotemporally continuous time series of decametric data from the MSI-OLI virtual constellation.
(4) Developed an algorithm to generate daily 16-m biophysical parameter maps by the combination of LAINet observation system and Gaofen-1/6 satellite images, which is crucial for agricultural monitoring.
(5) We also compared the performance of structure and physiology vegetation indices for photosynthetic phenology monitoring, and extend the application of CCI (a reliable proxy for tracking GPP dynamics) to sentinel-2 by replacing narrowband green with broadband green.
The results of the above work have been and will further be made available to targeted peers:
(1) Parts of results have been published in top-ranking journal on EO (6 SCI papers, with 1 on Geophysical Research Letters, 1 on IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 1 on IEEE Geoscience and Remote Sensing Letters, and 3 on Remote Sensing), and others are in review (1 on Photogrammetric Engineering and Remote Sensing and 1 on Journal of Remote Sensing) or preparation.
(2) A project website was successfully established:
https://www.creaf.cat/monitoring-vegetation-status-and-functioning-high-spatio-temporal-resolution-sentinel-2(se abrirá en una nueva ventana)(3) Our results will inform the decision-making for the stakeholders in agriculture, forestry, and insurance.
