class museopheno.sensors.Sentinel2(n_bands=10, bands_order='default')[source]

Use Sentinel2 as sensor via museopheno.sensors.sensorManager.

  • n_bands (int, default 4) – 4 or 10.

  • bands_order (str or list, default 'default') – If ‘default’, bands_order is the first 10m-bands (2,3,4 and 8), and if n_bands is 10 bands, the first 20-m bands (5,6,…11,12)


>>> dataset = Sentinel2(n_bands=10)
>>> dataset.bands_names
 'Vegetation Red Edge 1',
 'Vegetation Red Edge 2',
 'Vegetation Red Edge 3',
 'Vegetation Red Edge 4',
>>> dataset.get_index_expression('NDVI')
{'expression': '(B8-B4)/(B8+B4)', 'condition': '(B8+B4) != 0'}


SmoothSignal(input_dates[, output_dates, fmt])

__init__([n_bands, bands_order])

Initialize self.

add_index(index_name, expression[, …])

Add index for the current sensor, verify if band is available before adding the script.

compute_SITS(S2dir, out_SITS[, …])

Compute Satellite Image Time Series from Sentinel2 level 2A.


Configure how bands are ordered (by date or by band)

generate_index(X, expression[, …])

Generate index from array

generate_raster(input_raster, output_raster, …)

Generate index from raster

generate_temporal_sampling([start_date, …])

Generate sample time for gap-filling of Time Series


Return index expression

set_description_metadata(input_raster, dates)

Write metadata (band and date) in raster.