Remote sensing derived variables for modelling above ground biomass
The dataset displays remote sensing modelling variables extracted from active and passive remote sensing technology acquired in the summer and winter season of 2017. The extracted variables included Sentinel-1A derived variables which contained 16 GLCMs, VH and VV backscatter channels, and VH/VV band ratio. Sentinel-2 MSI predictor variables for wetland vegetation AGB estimation in both summer and winter included ten reflectance bands and eight vegetation indices (VIs). The VIs used included traditional VIs such as the Normalized Difference Vegetation Index (NDVI), Simple Ratio (SR), Green Red Vegetation Index (GRVI), Green Normalized Difference Vegetation Index (GNDVI). Other used VIs were derived from the red-edge regions (NDVIre5: Normalized Difference Vegetation Index Red-edge 1; NDVIre6: Normalized Difference Vegetation Index Red-edge 2; NDVIre7: Normalized Difference Vegetation Index Red-edge 3; SRre5: Simple Ratio Red-edge 1). All these variables were used for development of predictive models of above ground biomass of wetland vegetation.
South African Space Agency
Council for Scientific Industrial Research (CSIR)
Water Research Commission
Department/UnitGeography, Geoinformatics and Meteorology
Sustainable Development Goals
- 15 Life on Land