Mapping foliar N in miombo woodlands using sentinel-2 derived chlorophyll and structural indices

Abstract. The remote estimation of foliar nitrogen (N) is key in mapping and monitoring ecosystem health globally. Hyperspectral remote estimation of foliar N has mainly been via its relationship with chlorophyll content. However, the application of broadband remote sensing in foliar N estimation has been receiving increasing attention. The broadband estimation of N provides opportunities for landscape level estimation of N due to the wider spatial coverage. To this end, several studies have tested the potential applications of broadband sensors in estimating N. These were mainly focused on the near-infrared region. Our study was carried out in miombo woodlands, one of the most extensive woodland types in southern Africa. A bootstrapped random forest regression technique, in the R environment, was used for testing for significance in differences in accuracies of chlorophyll and structural indices when estimating foliar N. Our results showed a significant difference between the root mean square error of prediction obtained when chlorophyll indices (mean  =  0.52  %  ) and structural indices (mean  =  0.57  %  ) are used for predicting foliar N. We conclude that the chlorophyll indices predict foliar N significantly better than structural indices. This finding is significant in the estimation of foliar N at the landscape level.

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