![]() EnviSat carried the Medium Resolution Imaging Spectrometer( MERIS), Michelson Interferometer for Passive Atmospheric Sounding ( MIPAS), Radar Altimeter-2 ( RA-2), Laser Retro-Reflector ( LRR), Microwave Radiometer ( MWR), Advanced SAR ( ASAR), Global Ozone Monitoring by Occultation of Stars ( GOMOS), Scanning Imaging Absorption Spectrometer for Atmospheric Cartography ( SCIAMACHY), Advanced Along Track Scanning Radiometer, provided by the UK and Australia ( AATSR), and Doppler Orbitography and Radiopositioning Integrated by Satellite ( DORIS). See EnviSat (Environmental Satellite) summaryĮnviSat (Image credit: ESA) Summary Mission CapabilitiesĮnviSat was a research mission that carried ten instruments and provided a wealth of data related to Earth’s health and climate change. GOMOS, SCIAMACHY, RA-2, MIPAS, MERIS, ASAR (wave mode), MWR, ASAR (image mode), ASAR, ENVISAT Comms, DORIS-NG, AATSRĪtmospheric temperature and humidity sounders, Imaging microwave radars, Communications, Precision orbit, Atmospheric chemistry, Radar altimeters, Imaging multi-spectral radiometers (vis/IR), Imaging multi-spectral radiometers (passive microwave) Sea-ice thickness, Sea-ice sheet topography, Cloud optical depth, Aerosol optical depth (column/profile), SO2 Mole Fraction, Land surface topography, Cloud type, Color dissolved organic matter (CDOM), Cloud imagery, Cloud liquid water (column/profile), Aerosol Extinction / Backscatter (column/profile), Atmospheric specific humidity (column/profile), Sea level, O3 Mole Fraction, Fraction of Absorbed PAR (FAPAR), Dominant wave direction, Iceberg height, Land surface temperature, Cloud top height, Earth surface albedo, OClO (column/profile), Vegetation type, CO2 Mole Fraction, Atmospheric temperature (column/profile), Ocean imagery and water leaving spectral radiance, Sea-ice type, CH4 Mole Fraction, Sea surface temperature, CO Mole Fraction, Aerosol absorption optical depth (column/profile), CFC-11 (column/profile), HNO3 (column/profile), Dominant wave period, Ocean surface currents (vector), Ocean chlorophyll concentration, Glacier motion, N2O (column/profile), Ocean dynamic topography, Iceberg fractional cover, Photosynthetically Active Radiation (PAR), Bathymetry, Significant wave height, NO (column/profile), BrO (column/profile), Geoid, Wind speed over sea surface (horizontal), Wind vector over sea surface (horizontal), NO2 Mole Fraction, Ocean suspended sediment concentration, Short-wave Earth surface bi-directional reflectance, CFC-12 (column/profile), ClONO2 (column/profile), Land cover, Soil moisture at the surface, Sea-ice cover, Snow cover, Land surface imagery Multi-purpose imagery (ocean), Aerosols, Vegetation, Radiation budget, Ocean topography/currents, Albedo and reflectance, Ocean wave height and spectrum, Ice sheet topography, Atmospheric Humidity Fields, Cloud type, amount and cloud top temperature, Atmospheric Temperature Fields, Surface temperature (ocean), Cloud particle properties and profile, Surface temperature (land), Landscape topography, Ozone, Ocean colour/biology, Trace gases (excluding ozone), Snow cover, edge and depth, Ocean surface winds, Gravity, Magnetic and Geodynamic measurements, Soil moisture, Multi-purpose imagery (land), Sea ice cover, edge and thickness Var ndvi_95 = ndvi.reduce(ee.Reducer.Atmosphere, Ocean, Gravity and Magnetic Fields, Land, Snow & Ice limit values returned to ONE value in the 95th percentile not using max to avoid compute ndvi using red band 4 and NIR band 8a calculated as Print('size of image collection', count) print number of images in collection, given spatial/temporal filters ![]() ![]() to the boundary and filter date range by start and end vars defined above s2_filter: filters all avail sentinel 2 (s2) images: limit spatial extent The portion of code I think you are looking for is the last line, but I've included some code with comments above that for context: // define start and end date vars. However, I think you are trying to do something similar, so I hope this can help you. We use the 95th percentile value rather than the max to avoid any outliers due to errors. We take all the images from a given time period, find the value in the 95th percentile in that stack, and create one image that contains the 95th percentile value over the time period of interest. We often create composites of various vegetation indexes from Sentinel-2 using Google Earth Engine.
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