KGERaster {greenbrown} | R Documentation |
This function can be used to apply the function KGE
on raster data. See KGE
for details.
KGERaster(x, ref, trend = FALSE, start = c(1982, 1), freq = 12, ...)
x |
multi-layer raster object of class |
ref |
multi-layer raster object of class |
trend |
Include the effect of trend in the calculation? (default: FALSE). The calculation of breakpoints is currently not implemented for the function KGERaster. |
start |
beginning of the time series (i.e. the time of the first observation). The default is c(1982, 1), i.e. January 1982 which is the usual start date to compute trends on long-term series of satellite observations of NDVI. See |
freq |
The frequency of observations. The default is 12 for monthly observations. Use 24 for bi-monthly observations, 365 for daily observations or 1 for annual observations. See |
... |
further arguments for the function |
See KGE
for details.
The function returns a raster brick with the following layers:
KGE
Kling-Gupta effciency = 1 - eTotal
eTotal
total effect, i.e. euclidean distance
fMean
fraction of mean to the total effect
fVar
fraction of variance to the total effect
fCor
fraction of correlation to the total effect
fTrend
fraction of trend to the total effect (only if trend=TRUE)
eMean
effect of mean
eVar
effect of variance
eCor
effect of correlation
eTrend
effect of trend (only if trend=TRUE)
Matthias Forkel <matthias.forkel@geo.tuwien.ac.at> [aut, cre]
Gupta, H. V., H. Kling, K. K. Yilmaz, G. F. Martinez (2009): Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology 377, 80-91.
# # load a map of NDVI (normalized difference vegetation index) time series # data(ndvimap) # plot(ndvimap) # # increase mean # ndvimap2 <- ndvimap + 0.01 # kge1.r <- KGERaster(x=ndvimap2, ref=ndvimap) # plot(kge1.r) # # increase mean and variance # ndvimap3 <- ndvimap + 0.01 + rnorm(1000, 0, 0.05) # kge2.r <- KGERaster(ndvimap3, ndvimap) # plot(kge2.r) # # check also effects on trend (takes more time because of trend calculations) # kge3.r <- KGERaster(ndvimap3, ndvimap, trend=TRUE) # plot(kge3.r)