Vogelmann Red Edge Index 2

A vegetation index that uses specific red edge wavelengths to assess vegetation health and chlorophyll content. It is sensitive to changes in leaf internal structure and chlorophyll concentration. 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. VOG2 is particularly suited for vegetation stress detection, chlorophyll content assessment, forest health monitoring. The general formula is (re2_734 - re2_747) / (re1_715 + re1_726), which requires re1_715 and re1_726 and re2_734 and re2_747 spectral bands.

VOG2 is supported by 2 satellite sensors in our database, including Dragonette-1, Dragonette-2/3. Each sensor uses different band designations — for example, Dragonette-1 uses the formula (Band 19 - Band 20) / (Band 17 + Band 18), while Dragonette-2/3 uses (Band23 - Band24) / (Band21 + Band22). Select a sensor above to see its specific band mapping.

VOG2 requires re1_715 (715), re1_726 (726), re2_734 (734), re2_747 (747). 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, VOG2 provides complementary information that NDVI cannot capture on its own. The choice of index depends on your application, sensor availability, and atmospheric conditions.