Correlating Sentinel-2 MSI-derived vegetation indices with in-situ reflectance and tissue macronutrients in savannah grass

ABSTRACT

The application of vegetation indices is subject to sensor-dependent errors and uncertainty. This study examines the accuracy of Sentinel-2 Multi-Spectral Instrument (MSI) imagery when estimating biophysical properties of savannah grasses. Six commonly used vegetation indices utilising spectral ranges covered by the MSI were derived from a satellite image coinciding with a field campaign. The imaging and fieldwork dates were at the end of the growing season, at peak grass productivity. Two common grass species were selected for sampling: one broad-leaved, the other narrow-leaved. At widely spread sampling sites under different grazing intensities, five plants with no external manifestation of infection and in close proximity were selected for each species. From each species 35 foliar and 10 stem reflectance measurements were collected using a spectroradiometer which sensed in the 350–2500 nm range in 1.1–1.4 nm bandwidths. The reflectance was later averaged to generate one reflectance profile per species per sampling site. The leaves and stems from which reflectance was measured were collected for laboratory analysis to determine macronutrient (N, P, K, Ca, Mg) concentrations. At three sites where sampling coincided with sunny weather during the satellite overpass window of 09:30–10:30 AM local time, above canopy grass reflectance was measured at ground resolution distance (GRD) of 1 m. Some reflectance was collected within 10 min of image acquisition, which facilitated comparison. The image data were corrected to bottom-of-atmosphere reflectance using the Sent2Cor algorithm whose output included 20 m GRD visible, red edge, near- and short-wave infrared (SWIR) bands, which were used for the respective vegetation indices. The plant level and above canopy reflectance were resampled to the spectral ranges of the MSI bands, and values of the respective indices computed. Plant level values of three red edge indices, which collectively indicated green biomass and chlorophyll, had the strongest significant correlation with N concentrations in both grass species (r = 0.473–0.561; p < 0.01). P and K concentrations had low correlations with the tested indices. Largely due to canopy background reflectance, the above canopy and image-derived vegetation index values differed from corresponding plant level values by up to 9% and 40%, respectively. Despite the attenuation, the Red Edge Chlorophyll Index, Red Edge Inflection Point and a devised SWIR ratio index (ρ_{1650 nm}/ρ_{2200 nm}) showed potential for monitoring relative chlorophyll and green biomass (indicated by N concentrations), Ca and Mg content of savannah grass using Sentinel-2 MSI images.

Acknowledgements

The Leicester Institute for Advanced Studies (LIAS) is gratefully acknowledged for granting a strategic research fellowship at the University of Leicester to the first author, which facilitated this work. North-West University (NWU) is thanked for funding the local fieldwork expenses in South Africa through Institutional Research Excellence Awards (IREA) funding, and the costs of grass sample analysis through a Food Security and Safety (FSS) grant. Eco-Analytica Laboratory on the Potchefstroom Campus of NWU analysed the grass samples. Sincere gratitude to Mala Mala Game Reserve for granting permission for the research in the Game Reserve and assisting with lodging, subsistence and transport during fieldwork in the Reserve. The Mpumalanga Tourism and Parks Agency is thanked for granting permission for the research in Bush Buck Ridge Nature Reserve, and then providing transport for the fieldwork in the Nature Reserve.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Leicester Institute for Advanced Studies and North-West University.