Cloud-Free Global Maps of Essential Vegetation Traits Processed from the TOA Sentinel-3 Catalogue in Google Earth Engine

Global mapping of essential vegetation traits (EVTs) through data acquired by Earth-observing satellites provides a spatially explicit way to analyze the current states and dynamics our planet. Although significant efforts have been made, there is still lack global consistently derived multi-temporal trait maps that are cloud-free. Here we present processing chain for spatiotemporally continuous production four EVTs at scale: (1) fraction absorbed photosynthetically active radiation (FAPAR), (2) leaf area index (LAI), (3) fractional cover (FVC), (4) chlorophyll content (LCC). The proposed workflow presents scalable approach cloud-free EVTs. Hybrid retrieval models, named S3-TOA-GPR-1.0-WS, were implemented into Google Earth Engine (GEE) using Sentinel-3 Ocean Land Color Instrument (OLCI) Level-1B along with associated uncertainty estimates. We used Whittaker smoother (WS) temporal reconstruction EVTs, which led streams, here applied year 2019. Cloud-free produced 5 km spatial resolution 10-day time intervals. consistency plausibility EVT estimates resulting annual profiles evaluated per-pixel intra-annually correlating against corresponding products both MODIS Copernicus Service (CGLS). most consistent results obtained LAI, showed intra-annual correlations an average Pearson correlation coefficient (R) 0.57 CGLS LAI product. Globally, results, specifically obtaining higher than R> 0.5 reference between 30 60° latitude in Northern Hemisphere. Additionally, goodness-of-fit statistics also calculated locally over distinct vegetated land covers. As general trend, covers pronounced phenological high different products. However, sparsely fields as well areas near equator linked smaller seasonality lower correlations. conclude gap-free was overall consistent. Thanks GEE, entire OLCI L1B catalogue can be processed efficiently on scale made WS method. GEE facilitates operationally applicable easily accessible broader community.