Systematic landcover bias in Collection 5 MODIS cloud mask and derived products – a global overview

__________

```r

opts_chunk$set(eval = F)

```

This document describes the analysis of the Collection 5 MOD35 data.

# Google Earth Engine Processing

The following code produces the annual (2009) summaries of cloud frequency from MOD09, MOD35, and MOD11 using the Google Earth Engine 'playground' API [http://ee-api.appspot.com/](http://ee-api.appspot.com/). 

```coffee

var startdate="2009-01-01"

var stopdate="2009-12-31"

// MOD11 MODIS LST

var mod11 = ee.ImageCollection("MOD11A2").map(function(img){ 

  return img.select(['LST_Day_1km'])});

// MOD09 internal cloud flag

var mod09 = ee.ImageCollection("MOD09GA").filterDate(new Date(startdate),new Date(stopdate)).map(function(img) {

  return img.select(['state_1km']).expression("((b(0)/1024)%2)");

});

// MOD35 cloud flag

var mod35 = ee.ImageCollection("MOD09GA").filterDate(new Date(startdate),new Date(stopdate)).map(function(img) {

  return img.select(['state_1km']).expression("((b(0))%4)==1|((b(0))%4)==2");

});

//define reducers

var COUNT = ee.call("Reducer.count");

var MEAN = ee.call("Reducer.mean");

//a few maps of constants

c100=ee.Image(100);  //to multiply by 100

c02=ee.Image(0.02);  //to scale LST data

c272=ee.Image(272.15); // to convert K->C

//calculate mean cloudiness (%), rename, and convert to integer

mod09a=mod09.reduce(MEAN).select([0], ['MOD09']).multiply(c100).int8();

mod35a=mod35.reduce(MEAN).select([0], ['MOD35']).multiply(c100).int8();

/////////////////////////////////////////////////

// Generate the cloud frequency surface:

getMiss = function(collection) {

  //filter by date

i2=collection.filterDate(new Date(startdate),new Date(stopdate));

// number of layers in collection

i2_n=i2.getInfo().features.length;

//get means

// image of -1s to convert to % missing

c1=ee.Image(-1);

// 1 Calculate the number of days with measurements

// 2 divide by the total number of layers

i2_c=ee.Image(i2_n).float()

// 3 Add -1 and multiply by -1 to invert to % cloudy

// 4 Rename to "Percent_Cloudy"

// 5 multiply by 100 and convert to 8-bit integer to decrease file size

i2_miss=i2.reduce(COUNT).divide(i2_c).add(c1).multiply(c1).multiply(c100).int8();

return (i2_miss);

};

// run the function

mod11a=getMiss(mod11).select([0], ['MOD11_LST_PMiss']);

// get long-term mean

mod11b=mod11.reduce(MEAN).multiply(c02).subtract(c272).int8().select([0], ['MOD11_LST_MEAN']);

// summary object with all layers

summary=mod11a.addBands(mod11b).addBands(mod35a).addBands(mod09a)

var region='[[-180, -60], [-180, 90], [180, 90], [180, -60]]'  //global

// get download link

print("All")

var path = summary.getDownloadURL({

  'scale': 1000,

  'crs': 'EPSG:4326',

  'region': region

});

print('https://earthengine.sandbox.google.com' + path);

```

# Data Processing

```r

setwd("~/acrobates/adamw/projects/MOD35C5")

library(raster)

```

```

## Loading required package: sp

```

```r

beginCluster(10)

```

```

## Loading required package: snow

```

```r

library(rasterVis)

```

```

## Loading required package: lattice Loading required package: latticeExtra

## Loading required package: RColorBrewer Loading required package: hexbin

## Loading required package: grid

```

```r

library(rgdal)

```

```

## rgdal: version: 0.8-10, (SVN revision 478) Geospatial Data Abstraction

## Library extensions to R successfully loaded Loaded GDAL runtime: GDAL

## 1.9.2, released 2012/10/08 but rgdal build and GDAL runtime not in sync:

## ... consider re-installing rgdal!! Path to GDAL shared files:

## /usr/share/gdal/1.9 Loaded PROJ.4 runtime: Rel. 4.8.0, 6 March 2012,

## [PJ_VERSION: 480] Path to PROJ.4 shared files: (autodetected)

```

```r

library(plotKML)

```

```

## plotKML version 0.3-5 (2013-05-16) URL:

## http://plotkml.r-forge.r-project.org/

## 

## Attaching package: 'plotKML'

## 

## The following object is masked from 'package:raster':

## 

## count

```

```r

library(Cairo)

library(reshape)

```

```

## Loading required package: plyr

## 

## Attaching package: 'plyr'

## 

## The following object is masked from 'package:plotKML':

## 

## count

## 

## The following object is masked from 'package:raster':

## 

## count

## 

## Attaching package: 'reshape'

## 

## The following object is masked from 'package:plyr':

## 

## rename, round_any

## 

## The following object is masked from 'package:raster':

## 

## expand

```

```r

library(rgeos)

```

```

## rgeos version: 0.2-19, (SVN revision 394) GEOS runtime version:

## 3.3.3-CAPI-1.7.4 Polygon checking: TRUE

```

```r

library(splancs)

```

```

## Spatial Point Pattern Analysis Code in S-Plus

## 

## Version 2 - Spatial and Space-Time analysis

## 

## Attaching package: 'splancs'

## 

## The following object is masked from 'package:raster':

## 

## zoom

```

```r

## get % cloudy

mod09 = raster("data/MOD09_2009.tif")

names(mod09) = "C5MOD09CF"

NAvalue(mod09) = 0

mod35c5 = raster("data/MOD35_2009.tif")

names(mod35c5) = "C5MOD35CF"

NAvalue(mod35c5) = 0

## mod35C6 annual

mod35c6 = raster("data/MOD35C6_2009.tif")

names(mod35c6) = "C6MOD35CF"

NAvalue(mod35c6) = 255

## landcover

lulc = raster("data/MCD12Q1_IGBP_2009_051_wgs84_1km.tif")

# lulc=ratify(lulc)

data(worldgrids_pal)  #load palette

IGBP = data.frame(ID = 0:16, col = worldgrids_pal$IGBP[-c(18, 19)], lulc_levels2 = c("Water", 

    "Forest", "Forest", "Forest", "Forest", "Forest", "Shrublands", "Shrublands", 

    "Savannas", "Savannas", "Grasslands", "Permanent wetlands", "Croplands", 

    "Urban and built-up", "Cropland/Natural vegetation mosaic", "Snow and ice", 

    "Barren or sparsely vegetated"), stringsAsFactors = F)

IGBP$class = rownames(IGBP)

rownames(IGBP) = 1:nrow(IGBP)

levels(lulc) = list(IGBP)

names(lulc) = "MCD12Q1"

## MOD17

mod17 = raster("data/MOD17.tif", format = "GTiff")

NAvalue(mod17) = 65535

names(mod17) = "MOD17_unscaled"

mod17qc = raster("data/MOD17qc.tif", format = "GTiff")

NAvalue(mod17qc) = 255

names(mod17qc) = "MOD17CF"

## MOD11 via earth engine

mod11 = raster("data/MOD11_2009.tif", format = "GTiff")

names(mod11) = "MOD11_unscaled"

NAvalue(mod11) = 0

mod11qc = raster("data/MOD11qc_2009.tif", format = "GTiff")

names(mod11qc) = "MOD11CF"

```

Import the Collection 5 MOD35 processing path:

```r

pp = raster("data/MOD35pp.tif")

NAvalue(pp) = 255

names(pp) = "MOD35pp"

```

Define transects to illustrate the fine-grain relationship between MOD35 cloud frequency and both landcover and processing path.

```r

r1 = Lines(list(Line(matrix(c(-61.688, 4.098, -59.251, 3.43), ncol = 2, byrow = T))), 

    "Venezuela")

r2 = Lines(list(Line(matrix(c(133.746, -31.834, 134.226, -32.143), ncol = 2, 

    byrow = T))), "Australia")

r3 = Lines(list(Line(matrix(c(73.943, 27.419, 74.369, 26.877), ncol = 2, byrow = T))), 

    "India")

r4 = Lines(list(Line(matrix(c(33.195, 12.512, 33.802, 12.894), ncol = 2, byrow = T))), 

    "Sudan")

trans = SpatialLines(list(r1, r2, r3, r4), CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs "))

### write out shapefiles of transects

writeOGR(SpatialLinesDataFrame(trans, data = data.frame(ID = names(trans)), 

    match.ID = F), "output", layer = "transects", driver = "ESRI Shapefile", 

    overwrite = T)

```

Buffer transects to identify a small region around each transect for comparison and plotting

```r

transb = gBuffer(trans, byid = T, width = 0.4)

## make polygons of bounding boxes

bb0 <- lapply(slot(transb, "polygons"), bbox)

bb1 <- lapply(bb0, bboxx)

# turn these into matrices using a helper function in splancs

bb2 <- lapply(bb1, function(x) rbind(x, x[1, ]))

# close the matrix rings by appending the first coordinate

rn <- row.names(transb)

# get the IDs

bb3 <- vector(mode = "list", length = length(bb2))

# make somewhere to keep the output

for (i in seq(along = bb3)) bb3[[i]] <- Polygons(list(Polygon(bb2[[i]])), ID = rn[i])

# loop over the closed matrix rings, adding the IDs

bbs <- SpatialPolygons(bb3, proj4string = CRS(proj4string(transb)))

```

Extract the CF and mean values from each raster of interest.

```r

trd1 = lapply(1:length(transb), function(x) {

    td = crop(mod11, transb[x])

    tdd = lapply(list(mod35c5, mod35c6, mod09, mod17, mod17qc, mod11, mod11qc, 

        lulc, pp), function(l) resample(crop(l, transb[x]), td, method = "ngb"))

    ## normalize MOD11 and MOD17

    for (j in which(do.call(c, lapply(tdd, function(i) names(i))) %in% c("MOD11_unscaled", 

        "MOD17_unscaled"))) {

        trange = cellStats(tdd[[j]], range)

        tscaled = 100 * (tdd[[j]] - trange[1])/(trange[2] - trange[1])

        tscaled@history = list(range = trange)

        names(tscaled) = sub("_unscaled", "", names(tdd[[j]]))

        tdd = c(tdd, tscaled)

    }

    return(brick(tdd))

})

## bind all subregions into single dataframe for plotting

trd = do.call(rbind.data.frame, lapply(1:length(trd1), function(i) {

    d = as.data.frame(as.matrix(trd1[[i]]))

    d[, c("x", "y")] = coordinates(trd1[[i]])

    d$trans = names(trans)[i]

    d = melt(d, id.vars = c("trans", "x", "y"))

    return(d)

}))

transd = do.call(rbind.data.frame, lapply(1:length(trans), function(l) {

    td = as.data.frame(extract(trd1[[l]], trans[l], along = T, cellnumbers = F)[[1]])

    td$loc = extract(trd1[[l]], trans[l], along = T, cellnumbers = T)[[1]][, 

        1]

    td[, c("x", "y")] = xyFromCell(trd1[[l]], td$loc)

    td$dist = spDistsN1(as.matrix(td[, c("x", "y")]), as.matrix(td[1, c("x", 

        "y")]), longlat = T)

    td$transect = names(trans[l])

    td2 = melt(td, id.vars = c("loc", "x", "y", "dist", "transect"))

    td2 = td2[order(td2$variable, td2$dist), ]

    # get per variable ranges to normalize

    tr = cast(melt.list(tapply(td2$value, td2$variable, function(x) data.frame(min = min(x, 

        na.rm = T), max = max(x, na.rm = T)))), L1 ~ variable)

    td2$min = tr$min[match(td2$variable, tr$L1)]

    td2$max = tr$max[match(td2$variable, tr$L1)]

    print(paste("Finished ", names(trans[l])))

    return(td2)

}))

transd$type = ifelse(grepl("MOD35|MOD09|CF", transd$variable), "CF", "Data")

```

Compute difference between MOD09 and MOD35 cloud masks

```r

## comparison of % cloudy days

dif_c5_09 = raster("data/dif_c5_09.tif", format = "GTiff")

```

Define a color scheme

```r

n = 100

at = seq(0, 100, len = n)

bgyr = colorRampPalette(c("purple", "blue", "green", "yellow", "orange", "red", 

    "red"))

bgrayr = colorRampPalette(c("purple", "blue", "grey", "red", "red"))

cols = bgyr(n)

```

Import a global coastline map for overlay

```r

library(maptools)

coast = map2SpatialLines(map("world", interior = FALSE, plot = FALSE), proj4string = CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs"))

```

Draw the global cloud frequencies

```r

g1 = levelplot(stack(mod35c5, mod09), xlab = " ", scales = list(x = list(draw = F), 

    y = list(alternating = 1)), col.regions = cols, at = at) + layer(sp.polygons(bbs[1:4], 

    lwd = 2)) + layer(sp.lines(coast, lwd = 0.5))

g2 = levelplot(dif_c5_09, col.regions = bgrayr(100), at = seq(-70, 70, len = 100), 

    margin = F, ylab = " ", colorkey = list("right")) + layer(sp.polygons(bbs[1:4], 

    lwd = 2)) + layer(sp.lines(coast, lwd = 0.5))

g2$strip = strip.custom(var.name = "Difference (C5MOD35-C5MOD09)", style = 1, 

    strip.names = T, strip.levels = F)

```

Now illustrate the fine-grain regions

```r

p1=useOuterStrips(levelplot(value~x*y|variable+trans,data=trd[!trd$variable%in%c("MOD17_unscaled","MOD11_unscaled","MCD12Q1","MOD35pp"),],asp=1,scales=list(draw=F,rot=0,relation="free"),

                                       at=at,col.regions=cols,maxpixels=7e6,

                                       ylab="Latitude",xlab="Longitude"),strip = strip.custom(par.strip.text=list(cex=.7)))+layer(sp.lines(trans,lwd=2))

p2=useOuterStrips(

  levelplot(value~x*y|variable+trans,data=trd[trd$variable%in%c("MCD12Q1"),],

            asp=1,scales=list(draw=F,rot=0,relation="free"),colorkey=F,

            at=c(-1,IGBP$ID),col.regions=IGBP$col,maxpixels=7e7,

            legend=list(

              right=list(fun=draw.key(list(columns=1,#title="MCD12Q1 \n IGBP Land \n Cover",

                           rectangles=list(col=IGBP$col,size=1),

                           text=list(as.character(IGBP$ID),at=IGBP$ID-.5))))),

            ylab="",xlab=" "),strip = strip.custom(par.strip.text=list(cex=.7)),strip.left=F)+layer(sp.lines(trans,lwd=2))

p3=useOuterStrips(

  levelplot(value~x*y|variable+trans,data=trd[trd$variable%in%c("MOD35pp"),],

            asp=1,scales=list(draw=F,rot=0,relation="free"),colorkey=F,

            at=c(-1:4),col.regions=c("blue","cyan","tan","darkgreen"),maxpixels=7e7,

            legend=list(

              right=list(fun=draw.key(list(columns=1,#title="MOD35 \n Processing \n Path",

                           rectangles=list(col=c("blue","cyan","tan","darkgreen"),size=1),

                           text=list(c("Water","Coast","Desert","Land")))))),

            ylab="",xlab=" "),strip = strip.custom(par.strip.text=list(cex=.7)),strip.left=F)+layer(sp.lines(trans,lwd=2))

```

Now draw the profile plots for each transect.

```r

## transects

p4=xyplot(value~dist|transect,groups=variable,type=c("smooth","p"),

       data=transd,panel=function(...,subscripts=subscripts) {

         td=transd[subscripts,]

         ## mod09

         imod09=td$variable=="C5MOD09CF"

         panel.xyplot(td$dist[imod09],td$value[imod09],type=c("p","smooth"),span=0.2,subscripts=1:sum(imod09),col="red",pch=16,cex=.25)

         ## mod35C5

         imod35=td$variable=="C5MOD35CF"

         panel.xyplot(td$dist[imod35],td$value[imod35],type=c("p","smooth"),span=0.09,subscripts=1:sum(imod35),col="blue",pch=16,cex=.25)

         ## mod35C6

         imod35c6=td$variable=="C6MOD35CF"

         panel.xyplot(td$dist[imod35c6],td$value[imod35c6],type=c("p","smooth"),span=0.09,subscripts=1:sum(imod35c6),col="black",pch=16,cex=.25)

         ## mod17

         imod17=td$variable=="MOD17"

         panel.xyplot(td$dist[imod17],100*((td$value[imod17]-td$min[imod17][1])/(td$max[imod17][1]-td$min[imod17][1])),

                      type=c("smooth"),span=0.09,subscripts=1:sum(imod17),col="darkgreen",lty=5,pch=1,cex=.25)

         imod17qc=td$variable=="MOD17CF"

         panel.xyplot(td$dist[imod17qc],td$value[imod17qc],type=c("p","smooth"),span=0.09,subscripts=1:sum(imod17qc),col="darkgreen",pch=16,cex=.25)

         ## mod11

         imod11=td$variable=="MOD11"

         panel.xyplot(td$dist[imod11],100*((td$value[imod11]-td$min[imod11][1])/(td$max[imod11][1]-td$min[imod11][1])),

                      type=c("smooth"),span=0.09,subscripts=1:sum(imod17),col="orange",lty="dashed",pch=1,cex=.25)

         imod11qc=td$variable=="MOD11CF"

         qcspan=ifelse(td$transect[1]=="Australia",0.2,0.05)

         panel.xyplot(td$dist[imod11qc],td$value[imod11qc],type=c("p","smooth"),npoints=100,span=qcspan,subscripts=1:sum(imod11qc),col="orange",pch=16,cex=.25)

         ## land

         path=td[td$variable=="MOD35pp",]

         panel.segments(path$dist,-10,c(path$dist[-1],max(path$dist,na.rm=T)),-10,col=c("blue","cyan","tan","darkgreen")[path$value+1],subscripts=1:nrow(path),lwd=10,type="l")

         land=td[td$variable=="MCD12Q1",]

         panel.segments(land$dist,-20,c(land$dist[-1],max(land$dist,na.rm=T)),-20,col=IGBP$col[land$value+1],subscripts=1:nrow(land),lwd=10,type="l")

        },subscripts=T,par.settings = list(grid.pars = list(lineend = "butt")),

       scales=list(

         x=list(alternating=1,relation="free"),#, lim=c(0,70)),

         y=list(at=c(-18,-10,seq(0,100,len=5)),

           labels=c("MCD12Q1 IGBP","MOD35 path",seq(0,100,len=5)),

           lim=c(-25,100)),

         alternating=F),

       xlab="Distance Along Transect (km)", ylab="% Missing Data / % of Maximum Value",

       legend=list(

         bottom=list(fun=draw.key(list( rep=FALSE,columns=1,title=" ",

                      lines=list(type=c("b","b","b","b","b","l","b","l"),pch=16,cex=.5,

                        lty=c(0,1,1,1,1,5,1,5),

                        col=c("transparent","red","blue","black","darkgreen","darkgreen","orange","orange")),

                       text=list(

                         c("MODIS Products","C5 MOD09 % Cloudy","C5 MOD35 % Cloudy","C6 MOD35 % Cloudy","MOD17 % Missing","MOD17 (scaled)","MOD11 % Missing","MOD11 (scaled)")),

                       rectangles=list(border=NA,col=c(NA,"tan","darkgreen")),

                       text=list(c("C5 MOD35 Processing Path","Desert","Land")),

                       rectangles=list(border=NA,col=c(NA,IGBP$col[sort(unique(transd$value[transd$variable=="MCD12Q1"]+1))])),

                       text=list(c("MCD12Q1 IGBP Land Cover",IGBP$class[sort(unique(transd$value[transd$variable=="MCD12Q1"]+1))])))))),

  strip = strip.custom(par.strip.text=list(cex=.75)))

print(p4)

```

Compile the PDF:

```r

CairoPDF("output/mod35compare.pdf", width = 11, height = 7)

### Global Comparison

print(g1, position = c(0, 0.35, 1, 1), more = T)

print(g2, position = c(0, 0, 1, 0.415), more = F)

### MOD35 Desert Processing path

levelplot(pp, asp = 1, scales = list(draw = T, rot = 0), maxpixels = 1e+06, 

    at = c(-1:3), col.regions = c("blue", "cyan", "tan", "darkgreen"), margin = F, 

    colorkey = list(space = "bottom", title = "MOD35 Processing Path", labels = list(labels = c("Water", 

        "Coast", "Desert", "Land"), at = 0:4 - 0.5))) + layer(sp.polygons(bbs, 

    lwd = 2)) + layer(sp.lines(coast, lwd = 0.5))

### levelplot of regions

print(p1, position = c(0, 0, 0.62, 1), more = T)

print(p2, position = c(0.6, 0.21, 0.78, 0.79), more = T)

print(p3, position = c(0.76, 0.21, 1, 0.79))

### profile plots

print(p4)

dev.off()

```

Derive summary statistics for manuscript

```r