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Empirical NNI from a vegetation index (no GPR models required)

Source: R/nni_from_vi.R
nni_from_vi_empirical.Rd

Alternative to the GPR-based compute_NNI_from_S2 pipeline that lets the user derive a Nitrogen Nutrition Index map directly from a single vegetation index raster (NDRE, NDVI, or a red-edge chlorophyll index) using a published crop-specific empirical regression. The approach is less physically rigorous than the PROSAIL-trained GPR pipeline but does not require any model files and runs on a single band ratio.

Usage

nni_from_vi_empirical(
  vi_raster,
  index = c("NDRE", "NDVI", "CIred_edge"),
  crop = "wheat",
  slope = NULL,
  intercept = NULL,
  nni_range = c(0.5, 1.5),
  nni_thresholds = c(0.9, 1.1)
)

Arguments

vi_raster

A RasterLayer / SpatRaster with a normalised vegetation index (dimensionless, typically 0-1).

index

Character. The vegetation index used. One of "NDRE", "NDVI", "CIred_edge".

crop

Character. One of "wheat", "maize", "rice", "barley". Case-insensitive; Italian aliases ("frumento", "mais", "orzo") are accepted.

slope, intercept

Optional numeric overrides for the linear regression NNI = slope * VI + intercept. Both must be supplied together.

nni_range

Numeric length-2 vector giving the clipping range for the output (default c(0.5, 1.5)).

nni_thresholds

Numeric length-2 vector with the lower and upper NNI thresholds for zone classification (default c(0.90, 1.10)).

Value

A named list with two SpatRaster / RasterLayer objects: NNI (continuous) and zones (integer 1 / 2 / 3 for deficient / optimal / excessive).

Details

The default equations ship as a lookup table tied to index and crop. They reproduce the commonly cited regressions of Cao et al. (2013) for rice (NDRE), Cilia et al. (2014) and Magney et al. (2017) for wheat (NDRE), Li et al. (2014) for maize (Cired-edge), Fitzgerald et al. (2010) for wheat (NDVI). They are linear in the vegetation index: $$NNI = a \cdot VI + b$$ Users are strongly encouraged to replace a and b with locally calibrated values (via slope and intercept arguments) whenever a ground-truth dataset is available. NNI is clipped to a user-configurable range (default 0.5-1.5).

References

Cao Q, Miao Y, Wang H, Huang S, Cheng S, Khosla R, Jiang R. Non-destructive estimation of rice plant nitrogen status with Crop Circle multispectral active canopy sensor. Field Crops Res 2013;154:133-144.

Fitzgerald G, Rodriguez D, O'Leary G. Measuring and predicting canopy nitrogen nutrition in wheat using a spectral index - The canopy chlorophyll content index (CCCI). Field Crops Res 2010;116:318-324.

See also

compute_NNI_from_S2 for the rigorous PROSAIL + GPR pipeline; compute_vi for index computation from raw bands.

Examples

if (FALSE) { # \dontrun{
library(raster)
ndre <- raster("ndre.tif")
out  <- nni_from_vi_empirical(ndre, index = "NDRE", crop = "wheat")
plot(out$NNI)
} # }