Time series with MODISTools - r

Question

I need to get full EVI time series, along with dates and a quality information. After executing MODISSubsets() the crude data is available, but not processed in a comparably nice way as MODISSummaries() would do.
MODISSummaries() however reduces the time series to summary statistics, taking into account quality information.
Is there a way to extract time series for each tile from the crude data (see data frame crude below)? It would be great if that this could return a list of data frames, where each data frame represents one tile and holds data for EVI (or whatever variable), its date, and a quality flag.
Specifically, after doing the following ...
savedir <- './'
modis.subset <- data.frame(
lat = 11.3175,
long = 47.1167,
end.date = "2016-09-29"
)
MODISSubsets(
LoadDat = modis.subset,
Products = "MOD13Q1",
Bands = c("250m_16_days_EVI", "250m_16_days_pixel_reliability"),
Size = c(1,1),
StartDate = FALSE,
SaveDir = savedir,
TimeSeriesLength = 3
)
crude <- read.csv("./Lat47.11670Lon11.31750Start2013-01-01End2016-09-29___MOD13Q1.asc", header = FALSE, as.is = TRUE)
... how would you get to something like
nice <- list( lonX1_latY1=data.frame( date=..., var=..., qual=... ), lonX2_latX2=... )
...?

Answer 1

In short, I was missing that ExtractTile() would be usable with the return value of MODISTimeSeries(). My workaround is based on using ExtractTile() in combination with the output of reading the ASCII file. Here is what I got working for my purpose, returning a list that contains a matrix (npixels_lon, npixels_lat, n_timesteps) containing all the downloaded MODIS data, in this case EVI; a matrix of identical dimensions containing the pixel reliability code; and a vector of length n_timesteps holding the centre pixel information if its quality flag is 0 or the mean of its surrounding pixels otherwise:
read_crude_modis <- function( filn, savedir, expand_x, expand_y ){
# arguments:
# filn: file name of ASCII file holding MODIS "crude" data
# savedir: directory, where to look for that file
# expand_x : number of pixels to the right and left of centre
# expand_y : number of pixels to the top and bottom of centre
# MODIS quality flags:
# -1 Fill/No Data Not Processed
# 0 Good Data Use with confidence
# 1 Marginal data Useful, but look at other QA information
# 2 Snow/Ice Target covered with snow/ice
# 3 Cloudy Target not visible, covered with cloud
library( MODISTools )
ScaleFactor <- 0.0001 # applied to output variable
ndayyear <- 365
## Read dowloaded ASCII file
crude <- read.csv( paste( savedir, filn, sep="" ), header = FALSE, as.is = TRUE )
crude <- rename( crude, c( "V1"="nrows", "V2"="ncols", "V3"="modislon_ll", "V4"="modislat_ll", "V5"="dxy_m", "V6"="id", "V7"="MODISprod", "V8"="yeardoy", "V9"="coord", "V10"="MODISprocessdatetime" ) )
## this is just read to get length of time series and dates
tseries <- MODISTimeSeries( savedir, Band = "250m_16_days_EVI" )
ntsteps <- dim(tseries[[1]])[1]
tmp <- rownames( tseries[[1]] )
time <- data.frame( yr=as.numeric( substr( tmp, start=2, stop=5 )), doy=as.numeric( substr( tmp, start=6, stop=8 )) )
time$dates <- as.POSIXlt( as.Date( paste( as.character(time$yr), "-01-01", sep="" ) ) + time$doy - 1 )
time$yr_dec<- time$yr + ( time$doy - 1 ) / ndayyear
## get number of products for which data is in ascii file (not used)
nprod <- dim(crude)[1] / ntsteps
if ((dim(crude)[1]/nprod)!=ntsteps) { print("problem") }
## re-arrange data
if ( dim(crude)[2]==11 && expand_x==0 && expand_y==0 ){
## only one pixel downloaded
nice_all <- as.matrix( crude$V11[1:ntsteps], dim(1,1,ntsteps) ) * ScaleFactor ## EVI data
nice_qual_flg <- as.matrix( crude$V11[(ntsteps+1):(2*ntsteps)], dim(1,1,ntsteps) ) ## pixel reliability data
} else if ( dim(crude)[2]>11 ){
## multiple pixels downloaded
# nice <- ExtractTile( Data = tseries, Rows = c(crude$nrows,expand_y), Cols = c(crude$ncols,expand_x), Grid = TRUE ) ## > is not working: applying ExtractTile to return of MODISTimeSeries
nice_all <- ExtractTile( Data = crude[1:ntsteps,11:dim(crude)[2]] * ScaleFactor, Rows = c(crude$nrows[1],expand_y), Cols = c(crude$ncols[1],expand_x), Grid = TRUE )
nice_qual_flg <- ExtractTile( Data = crude[(ntsteps+1):(2*ntsteps),11:dim(crude)[2]], Rows = c(crude$nrows[1],expand_y), Cols = c(crude$ncols[1],expand_x), Grid = TRUE )
} else {
print( "Not sufficient data downloaded. Adjust expand_x and expand_y.")
}
## Clean data for centre pixel: in case quality flag is not '0', use mean of all 8 surrounding pixels
if ( expand_x==1 && expand_y==1 ){
nice_centre <- nice_all[2,2,]
nice_centre[ which( nice_qual_flg[2,2,]!=0 ) ] <- apply( nice_all[,,which( nice_qual_flg[2,2,]!=0 )], c(3), FUN=mean)
}
modis <- list( nice_all=nice_all, nice_centre=nice_centre, nice_qual_flg=nice_qual_flg, time=time )
return( modis )
}