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Remote estimation of nitrogen is more accurate at the start of the growing season when compared with end of the growing season in miombo woodlands

Godfrey Mutowo,O. Mutanga,M. Masocha

Published 2020 in Remote Sensing Applications: Society and Environment

ABSTRACT

Abstract The growing need to include foliar biochemical measurements in global change models has triggered the landscape level application of broadband satellites in nitrogen (N) estimation. In addition, the determination of seasonal variations in the accuracy of estimating N from remote sensing platforms is critical for understanding the reliability of N data used as an input into these models. However, seasonal differences in the accuracies of landscape level remote sensing models in dry deciduous landscapes such as the savanna woodlands remain poorly understood. Our study was carried out in the dry miombo woodland, one of the most expansive deciduous woodland in Southern Africa. A bootstrapped random forest model in the R environment was used to estimate foliar N from sentinel-2 broadband satellite platform. The relationship between foliar N and spectral reflectance was determined at two key phenological stages; the start and end of the growing season. Our results showed that the start of the growing season model (RMSE = 0.42) estimated N concentration better than the end of the growing season model (RMSE = 0.49). The difference between the two statistics are significantly (p

PUBLICATION RECORD

  • Publication year

    2020

  • Venue

    Remote Sensing Applications: Society and Environment

  • Publication date

    Unknown publication date

  • Fields of study

    Environmental Science

  • Identifiers
    DOI 10.1016/j.rsase.2019.100285
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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