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

__________

```{r,echo=TRUE,eval=TRUE}

 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/). 

```{r, engine='coffee', eval=F}

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)

beginCluster(10)

library(rasterVis)

library(rgdal)

library(plotKML)

library(Cairo)

library(reshape)

library(rgeos)

library(splancs)

## 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,

                -59.251,3.430

                ),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=.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=.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,.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=1e6,

          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-.5)))+layer(sp.polygons(bbs,lwd=2))+layer(sp.lines(coast,lwd=.5))

### levelplot of regions

print(p1,position=c(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}

td=cast(transect+loc+dist~variable,value="value",data=transd)

td2=melt.data.frame(td,id.vars=c("transect","dist","loc","MOD35pp","MCD12Q1"))

## function to prettyprint mean/sd's

msd= function(x) paste(round(mean(x,na.rm=T),1),"% ±",round(sd(x,na.rm=T),1),sep="")

cast(td2,transect+variable~MOD35pp,value="value",fun=msd)

cast(td2,transect+variable~MOD35pp+MCD12Q1,value="value",fun=msd)

cast(td2,transect+variable~.,value="value",fun=msd)

cast(td2,transect+variable~.,value="value",fun=msd)

cast(td2,variable~MOD35pp,value="value",fun=msd)

cast(td2,variable~.,value="value",fun=msd)

td[td$transect=="Venezuela",]

```

Export regional areas as KML for inclusion on website

```{r}

library(plotKML)

kml_open("output/modiscloud.kml")

readAll(mod35c5)

kml_layer.Raster(mod35c5,

     plot.legend = TRUE,raster_name="Collection 5 MOD35 Cloud Frequency",

    z.lim = c(0,100),colour_scale = get("colour_scale_numeric", envir = plotKML.opts),

#    home_url = get("home_url", envir = plotKML.opts),

#    metadata = NULL, html.table = NULL,

    altitudeMode = "clampToGround", balloon = FALSE

)

system(paste("gdal_translate -of KMLSUPEROVERLAY ",mod35c5@file@name," output/mod35c5.kmz -co FORMAT=JPEG"))

logo = "http://static.tumblr.com/t0afs9f/KWTm94tpm/yale_logo.png"

kml_screen(image.file = logo, position = "UL", sname = "YALE logo",size=c(.1,.1))

kml_close("modiscloud.kml")

kml_compress("modiscloud.kml",files=c(paste(month.name,".png",sep=""),"obj_legend.png"),zip="/usr/bin/zip")

```