Sentinel-3/FLEX Biophysical Product Confidence Using Sentinel-2 Land-Cover Spatial Distributions

The estimation of biophysical variables from remote sensing data raises important challenges in terms the acquisition technology and its limitations. In this way, some vegetation parameters, such as chlorophyll fluorescence, require sensors with a high spectral resolution that constrains spatial while significantly increasing subpixel land-cover heterogeneity. Precisely, variability often makes rather different canopy structures are aggregated together, which eventually generates deviations corresponding parameter quantification. context Copernicus program (and other related Earth Explorer missions), article proposes new statistical methodology to manage heterogeneity problem Sentinel-3 (S3) FLuorescence EXplorer (FLEX) by taking advantage higher Sentinel-2 (S2). Specifically, proposed approach first characterizes patterns S3/FLEX using inter-sensor S2. Then, multivariate analysis is conducted model influence these errors estimated used fluorescence proxies. Finally, modeled distributions employed predict confidence products on demand. Our experiments, multiple operational S2 simulated S3 products, reveal advantages effectively measure expected parameters respect standard regression algorithms. source codes work will be available at https://github.com/rufernan/PixelS3.