Sentinel 2 False Color

Sentinel-2 L1C cloud-free pan-tropical annual composites for the period 2015-2017 (Oct-Oct) computed by extracting per-band annual median values after cloud and shadow masking based on spectral conditions specifically developed for tropical regions as described in Simonetti D. et al., 2021 and available as standalone python code in IMPACT Toolbox. All available Sentinel-2 images have been processed in Google Earth Engine (GEE) and downloaded by selecting only TOA-Reflectance B11, B08, B04 (SWIR1, NIR, RED) bands at a spatial resolution of 20m (10m bands are resampled to 20m using nearest neighbour approach) and converted to 8bit (Byte) using a multiplicative factor of 0.051 for visualization purposes and size optimization. Prototyping and processing has been done in Google Earth Engine .

Sentinel-2 L1C cloud-free pan-tropical annual composites for 2018 (Jan-Dec) are compute by extracting per-band annual median values after cloud and shadow masking based on spectral conditions specifically developed for tropical regions as described in Simonetti D. et al., 2021 and available as standalone python code in IMPACT Toolbox. All available Sentinel-2 images have been processed in Google Earth Engine (GEE) and downloaded by selecting only TOA-Reflectance B11, B08, B04 (SWIR1, NIR, RED) bands at a spatial resolution of 20m (10m bands are resampled to 20m using nearest neighbour approach) and converted to 8bit (Byte) using a multiplicative factor of 0.051 for visualization purposes and size optimization. Prototyping and processing has been done in Google Earth Engine.

Sentinel-2 L1C cloud-free pan-tropical annual composites for 2019 (Jan-Dec) are compute by extracting per-band annual median values after cloud and shadow masking based on spectral conditions specifically developed for tropical regions as described in Simonetti D. et al., 2021 and available as standalone python code in IMPACT Toolbox. All available Sentinel-2 images have been processed in Google Earth Engine (GEE) and downloaded by selecting only TOA-Reflectance B11, B08, B04 (SWIR1, NIR, RED) bands at a spatial resolution of 20m (10m bands are resampled to 20m using nearest neighbour approach) and converted to 8bit (Byte) using a multiplicative factor of 0.051 for visualization purposes and size optimization. Prototyping and processing has been done in Google Earth Engine.

Sentinel-2 L1C cloud-free pan-tropical annual composites for 2020 (Jan-Dec) are compute by extracting per-band annual median values after cloud and shadow masking based on spectral conditions specifically developed for tropical regions as described in Simonetti D. et al., 2021 and available as standalone python code in IMPACT Toolbox. All available Sentinel-2 images have been processed in Google Earth Engine (GEE) and downloaded by selecting only TOA-Reflectance B11, B08, B04 (SWIR1, NIR, RED) bands at a spatial resolution of 20m (10m bands are resampled to 20m using nearest neighbour approach) and converted to 8bit (Byte) using a multiplicative factor of 0.051 for visualization purposes and size optimization. Prototyping and processing has been done in Google Earth Engine.

Sentinel-2 L1C cloud-free pan-tropical annual composites for the period 2015-2017, 2018, 2019 and 2020 are computeb by extracting per-band annual median values after cloud and shadow masking based on spectral conditions specifically developed for tropical regions as described in Simonetti D. et al., 2021 and available as standalone python code in IMPACT Toolbox. All available Sentinel-2 images have been processed in Google Earth Engine (GEE) and downloaded by selecting only TOA-Reflectance B11, B08, B04 (SWIR1, NIR, RED) bands at a spatial resolution of 20m (10m bands are resampled to 20m using nearest neighbour approach) and converted to 8bit (Byte) using a multiplicative factor of 0.051 for visualization purposes and size optimization. Prototyping and processing has been done in Google Earth Engine.

- Indication for potential change between 2015-17/2018/2019/2020 composites computed 'on the fly' based on simple spectral distance of SWIR1 bands. The product may serve as quick "alert" for potential forest and land cover change, to be then confirmed by visual verification. Violet and green colors correspond to an increase (e.g. soil component) and a decrease (absorption e.g. due to vegetation growth or water) in the SWIR1 band, respectively. Due to different processing buffers, artifacts may occour along edges.