Abstract:
In arid environments of the world, particularly in sub-Saharan Africa and Asia, floodplain
wetlands are a valuable agricultural resource. However, the water reticulation role by wetlands and
crop production can negatively impact wetland plants. Knowledge on the foliar biochemical elements
of wetland plants enhances understanding of the impacts of agricultural practices in wetlands. This
study thus used Sentinel-2 multispectral data to predict seasonal variations in the concentrations of
nine foliar biochemical elements in plant leaves of key floodplain wetland vegetation types and crops
in the uMfolozi floodplain system (UFS). Nutrient concentrations in different floodplain plant species
were estimated using Sentinel-2 multispectral data derived vegetation indices in concert with the
random forest regression. The results showed a mean R2 of 0.87 and 0.86 for the dry winter and wet
summer seasons, respectively. However, copper, sulphur, and magnesium were poorly correlated
(R2 ≤ 0.5) with vegetation indices during the summer season. The average % relative root mean
square errors (RMSE’s) for seasonal nutrient estimation accuracies for crops and wetland vegetation
were 15.2 % and 26.8%, respectively. There was a significant difference in nutrient concentrations
between the two plant types, (R2 = 0.94 (crops), R2 = 0.84 (vegetation). The red-edge position 1 (REP1)
and the normalised difference vegetation index (NDVI) were the best nutrient predictors. These
results demonstrate the usefulness of Sentinel-2 imagery and random forests regression in predicting
seasonal, nutrient concentrations as well as the accumulation of chemicals in wetland vegetation
and crops.
