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.
Highlights
This study demonstrated immense potentials for Sentinel-2 data to be used for estimating leaf nitrogen, potassium, calcium, magnesium, phosphorus, sulphur, zinc, boron and copper concentrations
Different vegetation indices and bands including the red edge position, NIR-based vegetation indices (NDVI) and band 2 can be important in estimating different nutrients across the seasons even though foliar estimation using Sentinel-2 did not show a strong relationship in estimating leaf magnesium, copper and sulphur in a wet season
The major conclusion of this work is that the random forests (RF) model was able to provide estimated nutrients with reasonable accuracies
Summary
Wetlands in South Africa cover about 2.9 million hectares and about 2.4% of the country’s land area [1] They are recognised as highly valuable natural resources that sustain the livelihoods of local communities by providing a wide-ranging ecosystem goods and services that include, wild fruits, vegetables, rice and water purification [2,3]. They mediate the adverse effects of extreme weather conditions [4] by attenuating floods and slowing down the speed of water movement [5]. The rate of wetland degradation worldwide and in SA remains high [6,7]
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