Sentinel-2 Red-Edge Position

Sentinel-2 Red-Edge Position for vegetation applications Vegetation indices quantify plant health, biomass, and photosynthetic activity by exploiting the contrast between how plants absorb visible light for photosynthesis and reflect near-infrared radiation from their cellular structure. S2REP is particularly suited for vegetation. The general formula is 705.0 + 35.0 * ((((RE3 + R) / 2.0) - RE1) / (RE2 - RE1)), which requires RE3 and R and RE1 and RE2 spectral bands.

S2REP is supported by 3 satellite sensors in our database, including Dragonette-1, Dragonette-2/3, Sentinel-2. Each sensor uses different band designations — for example, Dragonette-1 uses the formula 705.0 + 35.0 * ((((Band 22 + Band 12) / 2.0) - Band 16) / (Band 19 - Band 16)), while Dragonette-2/3 uses 705.0 + 35.0 * ((((Band26 + Band16) / 2.0) - Band20) / (Band23 - Band20)). Select a sensor above to see its specific band mapping.

S2REP requires RE3 (773-793 nm), R (630-690 nm), RE1 (700-710 nm), RE2 (730-745 nm). Vegetation strongly absorbs red light for photosynthesis while reflecting near-infrared light from its mesophyll cell structure, making this contrast a reliable indicator of plant vigour.

Both Python and R code samples are provided above. In Python, use rasterio to load individual band GeoTIFF files and numpy for the arithmetic. In R, the terra package handles raster operations efficiently. The key is to load bands as floating-point arrays to avoid integer division, and to handle division-by-zero cases where the denominator equals zero. For production use, consider applying a valid data mask to exclude no-data pixels before calculation.

While NDVI is the most common vegetation index, S2REP provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.