On estimating the gross primary productivity of Mediterranean grasslands under different fertilization regimes using vegetation indices and hyperspectral reflectance
We applied an empirical modelling approach for
gross primary productivity (GPP) estimation from hyperspectral
reflectance of Mediterranean grasslands undergoing
different fertilization treatments. The objective of the study
was to identify combinations of vegetation indices and bands
that best represent GPP changes between the annual peak of
growth and senescence dry out in Mediterranean grasslands.
In situ hyperspectral reflectance of vegetation and CO2 gas
exchange measurements were measured concurrently in unfertilized
(C) and fertilized plots with added nitrogen (N),
phosphorus (P) or the combination of N, P and potassium
(NPK). Reflectance values were aggregated according to
their similarity (r 90 %) in 26 continuous wavelength intervals
(Hyp). In addition, the same reflectance values were
resampled by reproducing the spectral bands of both the
Sentinel-2A Multispectral Instrument (S2) and Landsat 8
Operational Land Imager (L8) and simulating the signal that
would be captured in ideal conditions by either Sentinel-2A
or Landsat 8.
An optimal procedure for selection of the best subset of
predictor variables (LEAPS) was applied to identify the most
effective set of vegetation indices or spectral bands for GPP
estimation using Hyp, S2 or L8. LEAPS selected vegetation
indices according to their explanatory power, showing their
importance as indicators of the dynamic changes occurring in
community vegetation properties such as canopy water content
(NDWI) or chlorophyll and carotenoids = chlorophyll ratio
(MTCI, PSRI, GNDVI) and revealing their usefulness for
grasslands GPP estimates.
For Hyp and S2, bands performed as well as vegetation
indices to estimate GPP. To identify spectral bands with a
potential for improving GPP estimates based on vegetation
indices, we applied a two-step procedure which clearly indicated
the short-wave infrared region of the spectra as the
most relevant for this purpose. A comparison between S2-
and L8-based models showed similar explanatory powers for
the two simulated satellite sensors when both vegetation indices
and bands were included in the model.
Altogether, our results describe the potential of sensors
on board Sentinel-2 and Landsat 8 satellites for monitoring
grassland phenology and improving GPP estimates in support
of a sustainable agriculture managementinfo:eu-repo/semantics/publishedVersio