In bwtian/TIR: Thermal Infrared Remote Sensing

raw <- read.table("~/Dropbox/2data/dataRaw/LANDSAT_8_37926_lst.csv", sep =",")
sub  <-raw[,c(3,4,5,16,20,21,22,36,37,14,15)]
colnames(sub)  <- c("ID", "path", "row", "date", "cloud", "sunE", "sunA", "lat", "lon","lvl","sensor")
ord  <- sub[-1,]
ord[,4]  <- as.Date(ord[,4])
ord[,5]  <- as.numeric(ord[,5])
ord$monthC  <- months(ord[,4])
ord$monthN  <- as.numeric(substr(ord[,4], 6,7))
ord <- ord[order(ord[,2], ord[,13], ord[,3],ord[,5]),]
ord  <- ord[order(ord[,6]),]
subset(ord, month == "September" | month = "Octorber" , select = c(ID))
### lessest cloud
subset(ord, cloud < 10)
minCloud  <- cbind(by(ord[,5], ord[,c(2,3)],min))
library("reshape")
minCloud  <- na.omit(melt(minCloud))
ord$combine = as.character(interaction(ord$path, ord$row, ord$cloud))
minCloud$combine = as.character(interaction(minCloud$X1,minCloud$X2,minCloud$value))

hkdMinCloud = ord[complete.cases(match(ord$combine, minCloud$combine)),]
#write.csv(hkdMinCloud, "hkdMinCloud.csv", quote = F)


### Single Test
```r
l  <- ReadLandsat8("LC81050292014153LGN00")
rad  <- ToTOARadiance (l, "red")
str(l    )
class(l)
l[[1]]$"file_name_band_1"
l8.lst  <- lapply(dir(dir.tif), ReadLandsat8)
lapply(l,function(x) grep("elevation_source",x)) 
l[which(sapply(l, `[[`, 1) == "elevation_source")]
l8.lst[[1]]$metadata$landsat_scene_id
sapply(bandnames, function(x) "red" %in% x)
seq_along(bandnames)[sapply(bandnames, function(x) "red" %in% x)]
l[[grepl("file_name_band_4", l)]]
grepl("file_name_band_4", l[][)
 bandName <-  unlist(sapply(l, "[[", bandidx))
sapply(l, "[[", "file_name_band_4")[[1]]
l$metadata$file_name_band_4
l[[1]]$metadata
l$metadata
tools::file_path_as_absolute(file.path(dir.toaRad, sceneName, bandName))
(file.path(dir.toaRad, sceneName))
paste0(file.path(dir.toaRad, sceneName), "/", "")
idx <- 4
     bandidx <- paste0("file_name_band_", idx)
     bandName <-  sapply(i, "[[", bandidx)[[1]]
     Rad.rst  <- ToTOARadiance(l, "nir")
     writeRaster(Rad.rst, filename = (file.path(dir.toaRad, sceneName, bandName), overwrite = T))
    }
### DN to Radiance Tes
```r
sourceDir("~/SparkleShare/rLandsat8/src/main//R//rLandsat8//R")
sourceDir("~/SparkleShare/TIR/R/")
worklist  <- paste0(hkdMinCloud$ID,".tgz")
dir.tif  <- "~/Share500sda/Landsat8/at0_Sensor"
dir.toaRad  <- "~/Share500sda/Landsat8/at1_TOA/toaRad"
library(raster)
setwd(dir.tif)  ## very important tips for use rLandsat8
## files  <- sapply(file.path(dir.tif,list.files(dir.tif)), tools::file_path_as_absolute)
## basename(files)
## l8.lst  <- lapply(basename(files), ReadLandsat8)
l8.lst   <- lapply(dir(dir.tif), ReadLandsat8)
bandnames <-c("aerosol", "blue", "green", "red",
              "nir", "swir1", "swir2",
              "panchromatic",
              "cirrus",
              "tirs1", "tirs2")
for (i in l8.lst) {
        sceneName  <- i$metadata$landsat_scene_id
        if (!file.exists(file.path(dir.toaRad, sceneName))) {
                dir.create(file.path(dir.toaRad, sceneName), recursive = T)
        }
        for(j in bandnames){
                idx <- seq_along(bandnames)[sapply(bandnames, function(x) j %in% x)] # a number
                bandidx <- paste0("file_name_band_", idx)
                bandName <-  sapply(i, "[[", bandidx)[[1]]
                Rad.rst  <- ToTOARadiance(i, j)
                writeRaster(Rad.rst, filename = file.path(dir.toaRad, sceneName, bandName), overwrite = T)
                raster::removeTmpFiles(h = 0.03) ## Improtant tips for save hardisk
        }
}
library(devtools)
install_github("bleutner/RStoolbox")