Diferences series values EOBrowser vs Python API / GEE

Hello,

I have a question which I could not find a solution for

I use the EO Browser service to access NDVI data, with zero clouds, for specific polygons.

I am testing some Python API GEE scripts, to retrieve a data set from Sentinel 2, but the returned values of this series, for the same polygon, are pretty different.

Considering that the script is the same and I am using images without clouds, what is the reason for this big difference?

The pictures below show the results charts for each service.

Thanks in advance.

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Hi,

That is interesting to note, here are the possible reasons for these differences

If you have not noted already, since January 2022 ESA updated the Sentinel 2 processing baseline, as reported in this post, Sentinel Hub has since implemented parameters to harmonize the values, before and after the baseline change, see docs . I have no information whether this has been implemented yet in GEE. This is worth checking , seeing that significant differences appear in the months after January 2022
Processing options such as cloud filtering maybe quite different, it seems there were abit more cloud free dates selected in EO browser as seen in the chart. Perhaps you could compare the cloud filtering options you did use for GEE , even better could you share the GEE script ? that might make the comparison easier

Best regards

Thanks ,

The reason must be that the difference in the data series increases in January 2022.

Is there a way to implement the harmonization in the code?

The code: (Base Time Series By Region (with Conversion to Pandas and Visualization with Seaborn)

import ee, eemont, geemap

import pandas as pd

import numpy as np

import seaborn as sns

from matplotlib import pyplot as plt



Map = geemap.Map()



ee.Initialize()



area = ee.Geometry.Polygon(

    [[[-56.280807216723396,-30.29604656439111],

      [-56.27818938072486,-30.28926537271408],

      [-56.28400440986915,-30.287783082586177],

      [-56.2867509919004,-30.294731123971946]]]);



S2 = (ee.ImageCollection('COPERNICUS/S2_SR')

    .filterBounds(area)

    .filterDate('2021-05-01','2022-05-11')

    .filter(ee.Filter.lt('CLOUDY_PIXEL_PERCENTAGE',1))

    .maskClouds() # Extended (pre-processing) 

    .scaleAndOffset() # Extended (pre-processing)

    .spectralIndices(['NDVI','EVI'])) # Extended (processing)



ts = S2.getTimeSeriesByRegion(reducer = [ee.Reducer.mean()],

                             geometry = area,

                             scale=10, crs='EPSG:4326')