flip NDVI

Raster_NDVI.r

library(raster)

flipMaxMin <-function(vin){
  n = length(vin)
  nrw=nrow(vin)
  #eleminate the null values
  v <- na.omit(as.vector(vin))
  #length of the vector after elemination of null
  nn =length(v)
  #the regular rank
  r1 <- order(v)
  #inverse rank
  r2 <- r1[nn:1]
  #1 to n
  ori = 1:nn
  #flip first regular rank with the first in the inverse rank
  for (i in 1:(nn/2)){
    ori[r1[i]]= r2[i]
    ori[r2[i]]= r1[i]
  }
  #output 
  v = v[ori]
  vin[! is.na(vin)] <- v
  flipMaxMin <- vin
}


setwd("\\\\Server24\\iwm\\Projects\\Users\\Michael\\MODIS_Rwork\\MODIS_Rwork")

lst <- raster("LST_1km.tif")
ndvi <- raster("ndvi.tif")
nul <- NAvalue(ndvi)
ndvi[ndvi[] < 0] = nul
# 1 sum ndvi
# sum the ndvi in 250m grid to the 1km grid
sumNDVi <- aggregate(ndvi, fact=4,fun=sum)
# split the sum grid back to 250m grid
sumNDVI <- disaggregate(sumNDVi, fact = 4)


writeRaster(sumNDVI, "sumNDVI.tif",overwrite=TRUE)
# 2 percent
# calculation only perform between grids with same resolutions
fracNDVI = ndvi/sumNDVI*100
writeRaster(fracNDVI, "percNDVI.tif",overwrite=TRUE)
# 3 flip order for each 1km cell
fn <- as.matrix(fracNDVI)
for (i in seq(0,nrow(fn)-4,4)){
  for (j in seq(0,ncol(fn)-4,4)){
    #v <- fn[i+1:4,j+1:4]
    fn[i+1:4,j+1:4] <- flipMaxMin(fn[i+1:4,j+1:4])
  }
  print (i/ nrow(fn))
}
# output
fracNDVI[] <- fn
writeRaster(fracNDVI, "fracNDVI.tif",overwrite=TRUE)


##### 
g1km <- readTIFF("1km.tif")
ndvi <- readTIFF("ndvi.tif")
# split the 1km grid
g250 <- disaggregate(g1km, fact = 4)
# do the math
var <- ndvi * g250
writeRaster(var, "finalNDVI.tif",overwrite=TRUE)