Details:
Published: Dec. 6, 2017
Working with AppEEARS NetCDF-4 Output Data in R
This tutorial demonstrates how to work with data output from the Application for Extracting and Exploring Analysis Ready Samples (AppEEARS) Area Sampler in R.
AppEEARS allows users to select NetCDF-4 as an output file format for Area Requests. This tutorial provides step-by-step instructions for creating NetCDF-4 files filtered based on specific quality and land cover type determinations.
Case Study:
Throughout the tutorial, the following study is used as an example of how and why quality filtering is necessary in order to arrive at scientifically sound conclusions when working with remote sensing data. The tutorial follows similar data and methods of this study to demonstrate how to work with AppEEARS NetCDF-4 output files in R.
Samanta, A., Ganguly, S., Hashimoto, H., Devadiga, S., Vermote, E., Knyazikhin, Y., Nemani, R.R., and Myneni, R.B., 2010, Amazon forests did not green-up during the 2005 drought: Geophysical Research Letters, v. 37, no. 5.
What: The study tries to reproduce reports from previous studies showing greening of Amazon forests during the 2005 drought.
Why: To demonstrate the importance of considering remote sensing data quality, and reconcile contradicting reports of increased tree mortality and biomass burning with reports of anomalous Amazon forest greening.
How: The authors replicated a previous study claiming that Amazon forests were greening during the 2005 drought, with very specific quality and land cover type screening in their methods. They then used the quality-filtered data to calculate standard anomalies of EVI in order to arrive at their conclusions.
Where: Amazon Rainforest, from latitude: 20° S to 10° N, and longitude: 80° W to 45° W
When: July-September 2000-2008
AppEEARS Request Parameters: Request time series data for MODIS Enhance Vegetation Indices (EVI) and Land Cover.
- Date: July 01, 2005 to September 30, 2005
- Data layers:
- Terra MODIS Vegetation Indices: MOD13A2.006, 1000m, 16 day: '_1_km_16_days_EVI'
- Combined MODIS Land Cover Type: MCD12Q1.051, 500m, Yearly: 'Land_Cover_Type_1'
- Location: 20° S to 10° N, and longitude: 80° W to 45° W
- File Format: NetCDF-4
- Projection: Geographic (EPSG: 4326)
AppEEARS Information
To access AppEEARS, visit: https://appeears.earthdatacloud.nasa.gov/
For information on how to make an AppEEARS request, visit: https://appeears.earthdatacloud.nasa.gov/help
Topics Covered in this Tutorial
- Setting up the Working Environment
- Downloading AppEEARS Output in R from Text File
- Opening a NetCDF-4 (.nc) File
- Reading NetCDF-4 File Metadata
- Reading Quality Lookup Tables (LUTs) from CSV Files
- Setting Custom QA/QC Filtering (Masking)
- Visualizing NetCDF-4 Data
- Masking by Land Cover Type
- Visualizing NetCDF-4 Data with GIS Layers
- Generating Statistics on Quality Filtered Data
- Automating QA/QC Filtering
- Visualizing Time Series
- Exporting NetCDF-4 Files
Prerequisites
- R: Version 3.*
R Libraries:
- raster
- ncdf4
- RColorBrewer
- rmarkdown
- rworldmap
- colorspace
- fields
- sp
NOTE: You will need to have cURL installed on your OS in order to perform topic two (downloading data). If you are unable to download cURL, you can skip step two and download the files needed to run the rest of the tutorial in the section below.
The AppEEARS files used in this tutorial can be downloaded from:
- JSON file
- Text file containing download links.
- MCD12Q1.051_aid0001.nc
- MOD13A2.006_aid0001.nc
- MOD13A2.006 Quality Lookup Table
The source code used to generate this tutorial can be downloaded from:
1. Set up Working Environment
First, load the R packages necessary to run the tutorial. If you are missing any of the packages, you can download them using the install.packages('missing package') command in the console.
# Load necessary packages into R
library(raster); library(rmarkdown); library(ncdf4); library(sp)
Next, set your input/working directory, and create an output directory for the results.
# Set input directory, and change working directory
in_dir <- 'C:/Users/ckrehbiel/Documents/appeears-tutorials/' # IMPORTANT: Need to update this to reflect your working directory
setwd(in_dir)
# Create and set output directory
out_dir <- paste(in_dir, 'R_output/', sep= '')
suppressWarnings(dir.create(out_dir))
2. Download AppEEARS Output from .txt
Now take an AppEEARS output download text file and download all of your NetCDF-4 output files.
For more information on how to retrieve the output download text file from your request, visit the AppEEARS help pages, under Area Samples -> Downloading Your Request.
If you plan to use the files for the use case in this tutorial, you will need to download the text file of AppEEARS output file links and make sure that the text file is located in your input directory.
# Search for, open, and read text file containing links to AppEEARS output files
download_file <- file(list.files(pattern = '.*txt$'),'r')
download_link <- readLines(download_file)
## Warning in readLines(download_file): incomplete final line found on
## 'amazon-appeears-tutorial-example-download-list.txt'
# Loop through text file and download all AppEEARS outputs
for (i in 1:length(download_link)){
file_name <- tail(strsplit(download_link[i], '/')[[1]],1)
# NOTE: you will need cURL installed on your OS in order to successfully download the files
download.file(download_link[i], destfile = file_name, method = 'curl')
print(paste('Downloaded file: ', file_name, ' (', i, " of ", length(download_link), ')', sep = ""))
}
## [1] "Downloaded file: MCD12Q1.051_500m_aid0001.nc (1 of 2)"
## [1] "Downloaded file: MOD13A2.006_1km_aid0001.nc (2 of 2)"
close(download_file)
# Once you have finished downloading the files, remove these variables
rm(download_link, download_file)
Notice that two files were downloaded. For an AppEEARS Area Sample request, if NetCDF-4 is selected as the output format, outputs will be grouped into .nc files by product and by feature.
3. Open a NetCDF-4 (.nc) File
Search for the NetCDF-4 files that you just downloaded in your working directory, create a list of the files, and print the list.
# Now search for downloaded files
file_list <- list.files(pattern = '.*nc$')
file_list
## [1] "MCD12Q1.051_500m_aid0001.nc" "MOD13A2.006_1km_aid0001.nc"
Notice that the request includes the version 5.1 MODIS Combined Yearly Land Cover Type (MCD12Q1.051) and version 6 MODIS Terra 16-day Vegetation Indices (MOD13A2.006) products. Start with the MOD13A2 version 6 NetCDF file.
Notice below that the file_list index is set to 2, to select the MOD13A2.006.nc file above [1,2]. Read the file in by calling the nc_open() function from the ncdf4 library.
# Read file in, lets start with MOD13A2 version 6
file_in <- nc_open(file_list[2])
4. Read NetCDF-4 File Metadata
Below, look at the variables and metadata in the NetCDF-4 file.
# Print a list of variables in file
attributes(file_in$var)$names
## [1] "crs" "_1_km_16_days_EVI"
## [3] "_1_km_16_days_VI_Quality"
# Print a list of dimensions in file
attributes(file_in$dim)$names
## [1] "time" "lat" "lon"
_1_km_16_days_EVI and _1_km_16_days_VI_Quality are the data layers focused on for this tutorial, which are Enhanced Vegetation Index (EVI) and the corresponding quality data that were used in the case study.
Below, read in the metadata for the EVI dataset.
v6_info <- ncatt_get(file_in, "_1_km_16_days_EVI")
v6_info
## $`_FillValue`
## [1] -3000
##
## $coordinates
## [1] "time lat lon"
##
## $grid_mapping
## [1] "crs"
##
## $valid_min
## [1] -2000
##
## $valid_max
## [1] 10000
##
## $long_name
## [1] "1 km 16 days EVI"
##
## $units
## [1] "EVI"
##
## $scale_factor_err
## [1] 0
##
## $add_offset_err
## [1] 0
##
## $calibrated_nt
## [1] 5
##
## $scale_factor
## [1] 1e-04
##
## $add_offset
## [1] 0
Above, notice the useful metadata, including things like units, scale factor, and fill value.
Below, set the dataset arrays to variables by using the ncdf4 library's ncvar_get() command.
# Grab the EVI and VI Quality datasets and set to a variable
v6_EVI <- ncvar_get(file_in, "_1_km_16_days_EVI")
v6_QA <- ncvar_get(file_in, "_1_km_16_days_VI_Quality")
# Variable dimensions
dim(v6_EVI)
## [1] 4200 3600 7
Above, notice the NetCDF-4 file dimensions. 'time' indicates that the file includes 6 timesteps or layers (observations).
Grab the latitude and longitude arrays that are included in the NetCDF-4 files.
# Set lat and lon arrays for EVI data
lat_EVI <- ncvar_get(file_in, "lat")
lon_EVI <- ncvar_get(file_in, "lon")
# Grab the fill value and set to NA
fillvalue <- ncatt_get(file_in, "_1_km_16_days_EVI", "_FillValue")
v6_EVI[v6_EVI == fillvalue$value] <- NA
# Define the coordinate referense system proj.4 string
crs <- CRS("+proj=longlat +ellps=WGS84 +datum=WGS84 +no_defs+ towgs84=0,0,0")
# Grab first observation of EVI and Quality datasets
v6_EVI <- raster(t(v6_EVI[,,1]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
v6_EVI_original <- v6_EVI
v6_QA <- raster(t(v6_QA[,,1]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
5. Read Quality Lookup Tables from CSV
In this section, take the quality lookup table (LUT) CSV file from an AppEEARS request, read it into R using read.csv(), and use it to help set the quality masking parameters.
The quality LUT for your request (files ending in lookup.csv) can be found under Supporting Files on the Download Area Sample page.
For more information on how to retrieve the quality LUTs from your request, visit the AppEEARS help pages, under Area Samples -> Downloading Your Request.
Next, search the working directory for the quality LUT CSV file.
# Search for look up table
lut <- list.files(pattern = '.*lookup.csv$')
lut
## [1] "MOD13A2-006-1-km-16-days-VI-Quality-lookup.csv"
Below, use the read.csv() function to read in the CSV file as a dataframe.
# Read in the lut
v6_QA_lut <- read.csv(lut[1])
v6_QA_lut
## Value MODLAND
## 1 2050 Pixel produced, but most probably cloudy
## 2 2057 VI produced, but check other QA
## 3 2058 Pixel produced, but most probably cloudy
## 4 2061 VI produced, but check other QA
## 5 2062 Pixel produced, but most probably cloudy
## 6 2112 VI produced, good quality
## 7 2114 Pixel produced, but most probably cloudy
## 8 2116 VI produced, good quality
## 9 2118 Pixel produced, but most probably cloudy
## 10 2177 VI produced, but check other QA
## 11 2181 VI produced, but check other QA
## 12 2182 Pixel produced, but most probably cloudy
## 13 2185 VI produced, but check other QA
## 14 2186 Pixel produced, but most probably cloudy
## 15 2241 VI produced, but check other QA
## 16 2242 Pixel produced, but most probably cloudy
## 17 2253 VI produced, but check other QA
## 18 2254 Pixel produced, but most probably cloudy
## 19 2257 VI produced, but check other QA
## 20 2258 Pixel produced, but most probably cloudy
## 21 2317 VI produced, but check other QA
## 22 2318 Pixel produced, but most probably cloudy
## 23 2321 VI produced, but check other QA
## 24 2322 Pixel produced, but most probably cloudy
## 25 2372 VI produced, good quality
## 26 2374 Pixel produced, but most probably cloudy
## 27 2376 VI produced, good quality
## 28 2378 Pixel produced, but most probably cloudy
## 29 2433 VI produced, but check other QA
## 30 2441 VI produced, but check other QA
## 31 2442 Pixel produced, but most probably cloudy
## 32 2445 VI produced, but check other QA
## 33 2446 Pixel produced, but most probably cloudy
## 34 2497 VI produced, but check other QA
## 35 2498 Pixel produced, but most probably cloudy
## 36 2513 VI produced, but check other QA
## 37 2514 Pixel produced, but most probably cloudy
## 38 2517 VI produced, but check other QA
## 39 2518 Pixel produced, but most probably cloudy
## 40 3074 Pixel produced, but most probably cloudy
## 41 3094 Pixel produced, but most probably cloudy
## 42 3098 Pixel produced, but most probably cloudy
## 43 3150 Pixel produced, but most probably cloudy
## 44 3154 Pixel produced, but most probably cloudy
## 45 3218 Pixel produced, but most probably cloudy
## 46 3222 Pixel produced, but most probably cloudy
## 47 3266 Pixel produced, but most probably cloudy
## 48 3290 Pixel produced, but most probably cloudy
## 49 3294 Pixel produced, but most probably cloudy
## 50 3354 Pixel produced, but most probably cloudy
## 51 3358 Pixel produced, but most probably cloudy
## 52 3410 Pixel produced, but most probably cloudy
## 53 3414 Pixel produced, but most probably cloudy
## 54 3478 Pixel produced, but most probably cloudy
## 55 3482 Pixel produced, but most probably cloudy
## 56 3522 Pixel produced, but most probably cloudy
## 57 3550 Pixel produced, but most probably cloudy
## 58 3554 Pixel produced, but most probably cloudy
## 59 4097 VI produced, but check other QA
## 60 4098 Pixel produced, but most probably cloudy
## 61 4105 VI produced, but check other QA
## 62 4106 Pixel produced, but most probably cloudy
## 63 4109 VI produced, but check other QA
## 64 4110 Pixel produced, but most probably cloudy
## 65 4160 VI produced, good quality
## 66 4162 Pixel produced, but most probably cloudy
## 67 4164 VI produced, good quality
## 68 4166 Pixel produced, but most probably cloudy
## 69 4225 VI produced, but check other QA
## 70 4229 VI produced, but check other QA
## 71 4230 Pixel produced, but most probably cloudy
## 72 4233 VI produced, but check other QA
## 73 4234 Pixel produced, but most probably cloudy
## 74 4289 VI produced, but check other QA
## 75 4301 VI produced, but check other QA
## 76 4302 Pixel produced, but most probably cloudy
## 77 4305 VI produced, but check other QA
## 78 4306 Pixel produced, but most probably cloudy
## 79 4353 VI produced, but check other QA
## 80 4365 VI produced, but check other QA
## 81 4366 Pixel produced, but most probably cloudy
## 82 4369 VI produced, but check other QA
## 83 4370 Pixel produced, but most probably cloudy
## 84 5122 Pixel produced, but most probably cloudy
## 85 5142 Pixel produced, but most probably cloudy
## 86 5146 Pixel produced, but most probably cloudy
## 87 5186 Pixel produced, but most probably cloudy
## 88 5198 Pixel produced, but most probably cloudy
## 89 5202 Pixel produced, but most probably cloudy
## 90 5250 Pixel produced, but most probably cloudy
## 91 5266 Pixel produced, but most probably cloudy
## 92 5270 Pixel produced, but most probably cloudy
## 93 5314 Pixel produced, but most probably cloudy
## 94 5338 Pixel produced, but most probably cloudy
## 95 5342 Pixel produced, but most probably cloudy
## 96 5378 Pixel produced, but most probably cloudy
## 97 5402 Pixel produced, but most probably cloudy
## 98 5406 Pixel produced, but most probably cloudy
## 99 6145 VI produced, but check other QA
## 100 6146 Pixel produced, but most probably cloudy
## 101 6153 VI produced, but check other QA
## 102 6154 Pixel produced, but most probably cloudy
## 103 6157 VI produced, but check other QA
## 104 6158 Pixel produced, but most probably cloudy
## 105 6208 VI produced, good quality
## 106 6210 Pixel produced, but most probably cloudy
## 107 6212 VI produced, good quality
## 108 6214 Pixel produced, but most probably cloudy
## 109 6273 VI produced, but check other QA
## 110 6274 Pixel produced, but most probably cloudy
## 111 6277 VI produced, but check other QA
## 112 6278 Pixel produced, but most probably cloudy
## 113 6281 VI produced, but check other QA
## 114 6282 Pixel produced, but most probably cloudy
## 115 6337 VI produced, but check other QA
## 116 6338 Pixel produced, but most probably cloudy
## 117 6349 VI produced, but check other QA
## 118 6350 Pixel produced, but most probably cloudy
## 119 6353 VI produced, but check other QA
## 120 6354 Pixel produced, but most probably cloudy
## 121 6401 VI produced, but check other QA
## 122 6413 VI produced, but check other QA
## 123 6414 Pixel produced, but most probably cloudy
## 124 6417 VI produced, but check other QA
## 125 6418 Pixel produced, but most probably cloudy
## 126 6464 VI produced, good quality
## 127 6468 VI produced, good quality
## 128 6472 VI produced, good quality
## 129 6529 VI produced, but check other QA
## 130 6537 VI produced, but check other QA
## 131 6541 VI produced, but check other QA
## 132 6593 VI produced, but check other QA
## 133 6609 VI produced, but check other QA
## 134 6613 VI produced, but check other QA
## 135 6614 Pixel produced, but most probably cloudy
## 136 7170 Pixel produced, but most probably cloudy
## 137 7190 Pixel produced, but most probably cloudy
## 138 7194 Pixel produced, but most probably cloudy
## 139 7234 Pixel produced, but most probably cloudy
## 140 7246 Pixel produced, but most probably cloudy
## 141 7250 Pixel produced, but most probably cloudy
## 142 7298 Pixel produced, but most probably cloudy
## 143 7314 Pixel produced, but most probably cloudy
## 144 7318 Pixel produced, but most probably cloudy
## 145 7362 Pixel produced, but most probably cloudy
## 146 7386 Pixel produced, but most probably cloudy
## 147 7390 Pixel produced, but most probably cloudy
## 148 7426 Pixel produced, but most probably cloudy
## 149 7450 Pixel produced, but most probably cloudy
## 150 7454 Pixel produced, but most probably cloudy
## 151 7618 Pixel produced, but most probably cloudy
## 152 7646 Pixel produced, but most probably cloudy
## 153 7650 Pixel produced, but most probably cloudy
## 154 34833 VI produced, but check other QA
## 155 34834 Pixel produced, but most probably cloudy
## 156 34837 VI produced, but check other QA
## 157 34838 Pixel produced, but most probably cloudy
## 158 34888 VI produced, good quality
## 159 34890 Pixel produced, but most probably cloudy
## 160 34892 VI produced, good quality
## 161 34894 Pixel produced, but most probably cloudy
## 162 34957 VI produced, but check other QA
## 163 34958 Pixel produced, but most probably cloudy
## 164 34961 VI produced, but check other QA
## 165 34962 Pixel produced, but most probably cloudy
## 166 35029 VI produced, but check other QA
## 167 35030 Pixel produced, but most probably cloudy
## 168 35033 VI produced, but check other QA
## 169 35034 Pixel produced, but most probably cloudy
## 170 35089 VI produced, but check other QA
## 171 35090 Pixel produced, but most probably cloudy
## 172 35093 VI produced, but check other QA
## 173 35094 Pixel produced, but most probably cloudy
## 174 35097 VI produced, but check other QA
## 175 35098 Pixel produced, but most probably cloudy
## 176 35144 VI produced, good quality
## 177 35146 Pixel produced, but most probably cloudy
## 178 35148 VI produced, good quality
## 179 35150 Pixel produced, but most probably cloudy
## 180 35152 VI produced, good quality
## 181 35154 Pixel produced, but most probably cloudy
## 182 35201 VI produced, but check other QA
## 183 35213 VI produced, but check other QA
## 184 35214 Pixel produced, but most probably cloudy
## 185 35217 VI produced, but check other QA
## 186 35218 Pixel produced, but most probably cloudy
## 187 35221 VI produced, but check other QA
## 188 35222 Pixel produced, but most probably cloudy
## 189 35266 Pixel produced, but most probably cloudy
## 190 35285 VI produced, but check other QA
## 191 35286 Pixel produced, but most probably cloudy
## 192 35289 VI produced, but check other QA
## 193 35290 Pixel produced, but most probably cloudy
## 194 35293 VI produced, but check other QA
## 195 35294 Pixel produced, but most probably cloudy
## 196 35870 Pixel produced, but most probably cloudy
## 197 35874 Pixel produced, but most probably cloudy
## 198 35926 Pixel produced, but most probably cloudy
## 199 35930 Pixel produced, but most probably cloudy
## 200 35994 Pixel produced, but most probably cloudy
## 201 35998 Pixel produced, but most probably cloudy
## 202 36034 Pixel produced, but most probably cloudy
## 203 36066 Pixel produced, but most probably cloudy
## 204 36070 Pixel produced, but most probably cloudy
## 205 36126 Pixel produced, but most probably cloudy
## 206 36130 Pixel produced, but most probably cloudy
## 207 36134 Pixel produced, but most probably cloudy
## 208 36178 Pixel produced, but most probably cloudy
## 209 36182 Pixel produced, but most probably cloudy
## 210 36186 Pixel produced, but most probably cloudy
## 211 36190 Pixel produced, but most probably cloudy
## 212 36246 Pixel produced, but most probably cloudy
## 213 36250 Pixel produced, but most probably cloudy
## 214 36254 Pixel produced, but most probably cloudy
## 215 36258 Pixel produced, but most probably cloudy
## 216 36318 Pixel produced, but most probably cloudy
## 217 36322 Pixel produced, but most probably cloudy
## 218 36326 Pixel produced, but most probably cloudy
## 219 36330 Pixel produced, but most probably cloudy
## 220 36881 VI produced, but check other QA
## 221 36885 VI produced, but check other QA
## 222 36928 VI produced, good quality
## 223 36936 VI produced, good quality
## 224 36938 Pixel produced, but most probably cloudy
## 225 36940 VI produced, good quality
## 226 36942 Pixel produced, but most probably cloudy
## 227 36993 VI produced, but check other QA
## 228 37005 VI produced, but check other QA
## 229 37006 Pixel produced, but most probably cloudy
## 230 37009 VI produced, but check other QA
## 231 37010 Pixel produced, but most probably cloudy
## 232 37077 VI produced, but check other QA
## 233 37078 Pixel produced, but most probably cloudy
## 234 37081 VI produced, but check other QA
## 235 37082 Pixel produced, but most probably cloudy
## 236 37121 VI produced, but check other QA
## 237 37137 VI produced, but check other QA
## 238 37141 VI produced, but check other QA
## 239 37142 Pixel produced, but most probably cloudy
## 240 37145 VI produced, but check other QA
## 241 37146 Pixel produced, but most probably cloudy
## 242 37192 VI produced, good quality
## 243 37196 VI produced, good quality
## 244 37261 VI produced, but check other QA
## 245 37265 VI produced, but check other QA
## 246 37333 VI produced, but check other QA
## 247 37337 VI produced, but check other QA
## 248 37918 Pixel produced, but most probably cloudy
## 249 37974 Pixel produced, but most probably cloudy
## 250 37978 Pixel produced, but most probably cloudy
## 251 38042 Pixel produced, but most probably cloudy
## 252 38046 Pixel produced, but most probably cloudy
## 253 38082 Pixel produced, but most probably cloudy
## 254 38114 Pixel produced, but most probably cloudy
## 255 38118 Pixel produced, but most probably cloudy
## 256 38146 Pixel produced, but most probably cloudy
## 257 38174 Pixel produced, but most probably cloudy
## 258 38178 Pixel produced, but most probably cloudy
## 259 38182 Pixel produced, but most probably cloudy
## 260 38913 VI produced, but check other QA
## 261 38929 VI produced, but check other QA
## 262 38930 Pixel produced, but most probably cloudy
## 263 38933 VI produced, but check other QA
## 264 38976 VI produced, good quality
## 265 38984 VI produced, good quality
## 266 38988 VI produced, good quality
## 267 39041 VI produced, but check other QA
## 268 39053 VI produced, but check other QA
## 269 39054 Pixel produced, but most probably cloudy
## 270 39057 VI produced, but check other QA
## 271 39105 VI produced, but check other QA
## 272 39125 VI produced, but check other QA
## 273 39126 Pixel produced, but most probably cloudy
## 274 39129 VI produced, but check other QA
## 275 39130 Pixel produced, but most probably cloudy
## 276 39169 VI produced, but check other QA
## 277 39170 Pixel produced, but most probably cloudy
## 278 39185 VI produced, but check other QA
## 279 39186 Pixel produced, but most probably cloudy
## 280 39189 VI produced, but check other QA
## 281 39190 Pixel produced, but most probably cloudy
## 282 39193 VI produced, but check other QA
## 283 39194 Pixel produced, but most probably cloudy
## 284 39232 VI produced, good quality
## 285 39240 VI produced, good quality
## 286 39244 VI produced, good quality
## 287 39297 VI produced, but check other QA
## 288 39309 VI produced, but check other QA
## 289 39313 VI produced, but check other QA
## 290 39317 VI produced, but check other QA
## 291 39361 VI produced, but check other QA
## 292 39362 Pixel produced, but most probably cloudy
## 293 39381 VI produced, but check other QA
## 294 39385 VI produced, but check other QA
## 295 39386 Pixel produced, but most probably cloudy
## 296 39389 VI produced, but check other QA
## 297 39390 Pixel produced, but most probably cloudy
## 298 39938 Pixel produced, but most probably cloudy
## 299 39966 Pixel produced, but most probably cloudy
## 300 39970 Pixel produced, but most probably cloudy
## 301 40022 Pixel produced, but most probably cloudy
## 302 40026 Pixel produced, but most probably cloudy
## 303 40066 Pixel produced, but most probably cloudy
## 304 40090 Pixel produced, but most probably cloudy
## 305 40094 Pixel produced, but most probably cloudy
## 306 40130 Pixel produced, but most probably cloudy
## 307 40162 Pixel produced, but most probably cloudy
## 308 40166 Pixel produced, but most probably cloudy
## 309 40194 Pixel produced, but most probably cloudy
## 310 40222 Pixel produced, but most probably cloudy
## 311 40226 Pixel produced, but most probably cloudy
## 312 40230 Pixel produced, but most probably cloudy
## 313 40386 Pixel produced, but most probably cloudy
## 314 40422 Pixel produced, but most probably cloudy
## 315 40426 Pixel produced, but most probably cloudy
## 316 51199 Pixel not produced due to other reasons than clouds
## 317 63487 Pixel not produced due to other reasons than clouds
## VI.Usefulness Aerosol.Quantity
## 1 Higest quality Climatology
## 2 Decreasing quality (0010) Climatology
## 3 Decreasing quality (0010) Climatology
## 4 Decreasing quality (0011) Climatology
## 5 Decreasing quality (0011) Climatology
## 6 Higest quality Low
## 7 Higest quality Low
## 8 Lower quality Low
## 9 Lower quality Low
## 10 Higest quality Average
## 11 Lower quality Average
## 12 Lower quality Average
## 13 Decreasing quality (0010) Average
## 14 Decreasing quality (0010) Average
## 15 Higest quality High
## 16 Higest quality High
## 17 Decreasing quality (0011) High
## 18 Decreasing quality (0011) High
## 19 Decreasing quality (0100) High
## 20 Decreasing quality (0100) High
## 21 Decreasing quality (0011) Climatology
## 22 Decreasing quality (0011) Climatology
## 23 Decreasing quality (0100) Climatology
## 24 Decreasing quality (0100) Climatology
## 25 Lower quality Low
## 26 Lower quality Low
## 27 Decreasing quality (0010) Low
## 28 Decreasing quality (0010) Low
## 29 Higest quality Average
## 30 Decreasing quality (0010) Average
## 31 Decreasing quality (0010) Average
## 32 Decreasing quality (0011) Average
## 33 Decreasing quality (0011) Average
## 34 Higest quality High
## 35 Higest quality High
## 36 Decreasing quality (0100) High
## 37 Decreasing quality (0100) High
## 38 Decreasing quality (0101) High
## 39 Decreasing quality (0101) High
## 40 Higest quality Climatology
## 41 Decreasing quality (0101) Climatology
## 42 Decreasing quality (0110) Climatology
## 43 Decreasing quality (0011) Low
## 44 Decreasing quality (0100) Low
## 45 Decreasing quality (0100) Average
## 46 Decreasing quality (0101) Average
## 47 Higest quality High
## 48 Decreasing quality (0110) High
## 49 Decreasing quality (0111) High
## 50 Decreasing quality (0110) Climatology
## 51 Decreasing quality (0111) Climatology
## 52 Decreasing quality (0100) Low
## 53 Decreasing quality (0101) Low
## 54 Decreasing quality (0101) Average
## 55 Decreasing quality (0110) Average
## 56 Higest quality High
## 57 Decreasing quality (0111) High
## 58 Decreasing quality (1000) High
## 59 Higest quality Climatology
## 60 Higest quality Climatology
## 61 Decreasing quality (0010) Climatology
## 62 Decreasing quality (0010) Climatology
## 63 Decreasing quality (0011) Climatology
## 64 Decreasing quality (0011) Climatology
## 65 Higest quality Low
## 66 Higest quality Low
## 67 Lower quality Low
## 68 Lower quality Low
## 69 Higest quality Average
## 70 Lower quality Average
## 71 Lower quality Average
## 72 Decreasing quality (0010) Average
## 73 Decreasing quality (0010) Average
## 74 Higest quality High
## 75 Decreasing quality (0011) High
## 76 Decreasing quality (0011) High
## 77 Decreasing quality (0100) High
## 78 Decreasing quality (0100) High
## 79 Higest quality Climatology
## 80 Decreasing quality (0011) Climatology
## 81 Decreasing quality (0011) Climatology
## 82 Decreasing quality (0100) Climatology
## 83 Decreasing quality (0100) Climatology
## 84 Higest quality Climatology
## 85 Decreasing quality (0101) Climatology
## 86 Decreasing quality (0110) Climatology
## 87 Higest quality Low
## 88 Decreasing quality (0011) Low
## 89 Decreasing quality (0100) Low
## 90 Higest quality Average
## 91 Decreasing quality (0100) Average
## 92 Decreasing quality (0101) Average
## 93 Higest quality High
## 94 Decreasing quality (0110) High
## 95 Decreasing quality (0111) High
## 96 Higest quality Climatology
## 97 Decreasing quality (0110) Climatology
## 98 Decreasing quality (0111) Climatology
## 99 Higest quality Climatology
## 100 Higest quality Climatology
## 101 Decreasing quality (0010) Climatology
## 102 Decreasing quality (0010) Climatology
## 103 Decreasing quality (0011) Climatology
## 104 Decreasing quality (0011) Climatology
## 105 Higest quality Low
## 106 Higest quality Low
## 107 Lower quality Low
## 108 Lower quality Low
## 109 Higest quality Average
## 110 Higest quality Average
## 111 Lower quality Average
## 112 Lower quality Average
## 113 Decreasing quality (0010) Average
## 114 Decreasing quality (0010) Average
## 115 Higest quality High
## 116 Higest quality High
## 117 Decreasing quality (0011) High
## 118 Decreasing quality (0011) High
## 119 Decreasing quality (0100) High
## 120 Decreasing quality (0100) High
## 121 Higest quality Climatology
## 122 Decreasing quality (0011) Climatology
## 123 Decreasing quality (0011) Climatology
## 124 Decreasing quality (0100) Climatology
## 125 Decreasing quality (0100) Climatology
## 126 Higest quality Low
## 127 Lower quality Low
## 128 Decreasing quality (0010) Low
## 129 Higest quality Average
## 130 Decreasing quality (0010) Average
## 131 Decreasing quality (0011) Average
## 132 Higest quality High
## 133 Decreasing quality (0100) High
## 134 Decreasing quality (0101) High
## 135 Decreasing quality (0101) High
## 136 Higest quality Climatology
## 137 Decreasing quality (0101) Climatology
## 138 Decreasing quality (0110) Climatology
## 139 Higest quality Low
## 140 Decreasing quality (0011) Low
## 141 Decreasing quality (0100) Low
## 142 Higest quality Average
## 143 Decreasing quality (0100) Average
## 144 Decreasing quality (0101) Average
## 145 Higest quality High
## 146 Decreasing quality (0110) High
## 147 Decreasing quality (0111) High
## 148 Higest quality Climatology
## 149 Decreasing quality (0110) Climatology
## 150 Decreasing quality (0111) Climatology
## 151 Higest quality High
## 152 Decreasing quality (0111) High
## 153 Decreasing quality (1000) High
## 154 Decreasing quality (0100) Climatology
## 155 Decreasing quality (0100) Climatology
## 156 Decreasing quality (0101) Climatology
## 157 Decreasing quality (0101) Climatology
## 158 Decreasing quality (0010) Low
## 159 Decreasing quality (0010) Low
## 160 Decreasing quality (0011) Low
## 161 Decreasing quality (0011) Low
## 162 Decreasing quality (0011) Average
## 163 Decreasing quality (0011) Average
## 164 Decreasing quality (0100) Average
## 165 Decreasing quality (0100) Average
## 166 Decreasing quality (0101) High
## 167 Decreasing quality (0101) High
## 168 Decreasing quality (0110) High
## 169 Decreasing quality (0110) High
## 170 Decreasing quality (0100) Climatology
## 171 Decreasing quality (0100) Climatology
## 172 Decreasing quality (0101) Climatology
## 173 Decreasing quality (0101) Climatology
## 174 Decreasing quality (0110) Climatology
## 175 Decreasing quality (0110) Climatology
## 176 Decreasing quality (0010) Low
## 177 Decreasing quality (0010) Low
## 178 Decreasing quality (0011) Low
## 179 Decreasing quality (0011) Low
## 180 Decreasing quality (0100) Low
## 181 Decreasing quality (0100) Low
## 182 Higest quality Average
## 183 Decreasing quality (0011) Average
## 184 Decreasing quality (0011) Average
## 185 Decreasing quality (0100) Average
## 186 Decreasing quality (0100) Average
## 187 Decreasing quality (0101) Average
## 188 Decreasing quality (0101) Average
## 189 Higest quality High
## 190 Decreasing quality (0101) High
## 191 Decreasing quality (0101) High
## 192 Decreasing quality (0110) High
## 193 Decreasing quality (0110) High
## 194 Decreasing quality (0111) High
## 195 Decreasing quality (0111) High
## 196 Decreasing quality (0111) Climatology
## 197 Decreasing quality (1000) Climatology
## 198 Decreasing quality (0101) Low
## 199 Decreasing quality (0110) Low
## 200 Decreasing quality (0110) Average
## 201 Decreasing quality (0111) Average
## 202 Higest quality High
## 203 Decreasing quality (1000) High
## 204 Decreasing quality (1001) High
## 205 Decreasing quality (0111) Climatology
## 206 Decreasing quality (1000) Climatology
## 207 Decreasing quality (1001) Climatology
## 208 Decreasing quality (0100) Low
## 209 Decreasing quality (0101) Low
## 210 Decreasing quality (0110) Low
## 211 Decreasing quality (0111) Low
## 212 Decreasing quality (0101) Average
## 213 Decreasing quality (0110) Average
## 214 Decreasing quality (0111) Average
## 215 Decreasing quality (1000) Average
## 216 Decreasing quality (0111) High
## 217 Decreasing quality (1000) High
## 218 Decreasing quality (1001) High
## 219 Decreasing quality (1010) High
## 220 Decreasing quality (0100) Climatology
## 221 Decreasing quality (0101) Climatology
## 222 Higest quality Low
## 223 Decreasing quality (0010) Low
## 224 Decreasing quality (0010) Low
## 225 Decreasing quality (0011) Low
## 226 Decreasing quality (0011) Low
## 227 Higest quality Average
## 228 Decreasing quality (0011) Average
## 229 Decreasing quality (0011) Average
## 230 Decreasing quality (0100) Average
## 231 Decreasing quality (0100) Average
## 232 Decreasing quality (0101) High
## 233 Decreasing quality (0101) High
## 234 Decreasing quality (0110) High
## 235 Decreasing quality (0110) High
## 236 Higest quality Climatology
## 237 Decreasing quality (0100) Climatology
## 238 Decreasing quality (0101) Climatology
## 239 Decreasing quality (0101) Climatology
## 240 Decreasing quality (0110) Climatology
## 241 Decreasing quality (0110) Climatology
## 242 Decreasing quality (0010) Low
## 243 Decreasing quality (0011) Low
## 244 Decreasing quality (0011) Average
## 245 Decreasing quality (0100) Average
## 246 Decreasing quality (0101) High
## 247 Decreasing quality (0110) High
## 248 Decreasing quality (0111) Climatology
## 249 Decreasing quality (0101) Low
## 250 Decreasing quality (0110) Low
## 251 Decreasing quality (0110) Average
## 252 Decreasing quality (0111) Average
## 253 Higest quality High
## 254 Decreasing quality (1000) High
## 255 Decreasing quality (1001) High
## 256 Higest quality Climatology
## 257 Decreasing quality (0111) Climatology
## 258 Decreasing quality (1000) Climatology
## 259 Decreasing quality (1001) Climatology
## 260 Higest quality Climatology
## 261 Decreasing quality (0100) Climatology
## 262 Decreasing quality (0100) Climatology
## 263 Decreasing quality (0101) Climatology
## 264 Higest quality Low
## 265 Decreasing quality (0010) Low
## 266 Decreasing quality (0011) Low
## 267 Higest quality Average
## 268 Decreasing quality (0011) Average
## 269 Decreasing quality (0011) Average
## 270 Decreasing quality (0100) Average
## 271 Higest quality High
## 272 Decreasing quality (0101) High
## 273 Decreasing quality (0101) High
## 274 Decreasing quality (0110) High
## 275 Decreasing quality (0110) High
## 276 Higest quality Climatology
## 277 Higest quality Climatology
## 278 Decreasing quality (0100) Climatology
## 279 Decreasing quality (0100) Climatology
## 280 Decreasing quality (0101) Climatology
## 281 Decreasing quality (0101) Climatology
## 282 Decreasing quality (0110) Climatology
## 283 Decreasing quality (0110) Climatology
## 284 Higest quality Low
## 285 Decreasing quality (0010) Low
## 286 Decreasing quality (0011) Low
## 287 Higest quality Average
## 288 Decreasing quality (0011) Average
## 289 Decreasing quality (0100) Average
## 290 Decreasing quality (0101) Average
## 291 Higest quality High
## 292 Higest quality High
## 293 Decreasing quality (0101) High
## 294 Decreasing quality (0110) High
## 295 Decreasing quality (0110) High
## 296 Decreasing quality (0111) High
## 297 Decreasing quality (0111) High
## 298 Higest quality Climatology
## 299 Decreasing quality (0111) Climatology
## 300 Decreasing quality (1000) Climatology
## 301 Decreasing quality (0101) Low
## 302 Decreasing quality (0110) Low
## 303 Higest quality Average
## 304 Decreasing quality (0110) Average
## 305 Decreasing quality (0111) Average
## 306 Higest quality High
## 307 Decreasing quality (1000) High
## 308 Decreasing quality (1001) High
## 309 Higest quality Climatology
## 310 Decreasing quality (0111) Climatology
## 311 Decreasing quality (1000) Climatology
## 312 Decreasing quality (1001) Climatology
## 313 Higest quality High
## 314 Decreasing quality (1001) High
## 315 Decreasing quality (1010) High
## 316 Not useful for any other reason/not processed High
## 317 Not useful for any other reason/not processed High
## Adjacent.cloud.detected Atmosphere.BRDF.Correction Mixed.Clouds
## 1 No No No
## 2 No No No
## 3 No No No
## 4 No No No
## 5 No No No
## 6 No No No
## 7 No No No
## 8 No No No
## 9 No No No
## 10 No No No
## 11 No No No
## 12 No No No
## 13 No No No
## 14 No No No
## 15 No No No
## 16 No No No
## 17 No No No
## 18 No No No
## 19 No No No
## 20 No No No
## 21 Yes No No
## 22 Yes No No
## 23 Yes No No
## 24 Yes No No
## 25 Yes No No
## 26 Yes No No
## 27 Yes No No
## 28 Yes No No
## 29 Yes No No
## 30 Yes No No
## 31 Yes No No
## 32 Yes No No
## 33 Yes No No
## 34 Yes No No
## 35 Yes No No
## 36 Yes No No
## 37 Yes No No
## 38 Yes No No
## 39 Yes No No
## 40 No No Yes
## 41 No No Yes
## 42 No No Yes
## 43 No No Yes
## 44 No No Yes
## 45 No No Yes
## 46 No No Yes
## 47 No No Yes
## 48 No No Yes
## 49 No No Yes
## 50 Yes No Yes
## 51 Yes No Yes
## 52 Yes No Yes
## 53 Yes No Yes
## 54 Yes No Yes
## 55 Yes No Yes
## 56 Yes No Yes
## 57 Yes No Yes
## 58 Yes No Yes
## 59 No No No
## 60 No No No
## 61 No No No
## 62 No No No
## 63 No No No
## 64 No No No
## 65 No No No
## 66 No No No
## 67 No No No
## 68 No No No
## 69 No No No
## 70 No No No
## 71 No No No
## 72 No No No
## 73 No No No
## 74 No No No
## 75 No No No
## 76 No No No
## 77 No No No
## 78 No No No
## 79 Yes No No
## 80 Yes No No
## 81 Yes No No
## 82 Yes No No
## 83 Yes No No
## 84 No No Yes
## 85 No No Yes
## 86 No No Yes
## 87 No No Yes
## 88 No No Yes
## 89 No No Yes
## 90 No No Yes
## 91 No No Yes
## 92 No No Yes
## 93 No No Yes
## 94 No No Yes
## 95 No No Yes
## 96 Yes No Yes
## 97 Yes No Yes
## 98 Yes No Yes
## 99 No No No
## 100 No No No
## 101 No No No
## 102 No No No
## 103 No No No
## 104 No No No
## 105 No No No
## 106 No No No
## 107 No No No
## 108 No No No
## 109 No No No
## 110 No No No
## 111 No No No
## 112 No No No
## 113 No No No
## 114 No No No
## 115 No No No
## 116 No No No
## 117 No No No
## 118 No No No
## 119 No No No
## 120 No No No
## 121 Yes No No
## 122 Yes No No
## 123 Yes No No
## 124 Yes No No
## 125 Yes No No
## 126 Yes No No
## 127 Yes No No
## 128 Yes No No
## 129 Yes No No
## 130 Yes No No
## 131 Yes No No
## 132 Yes No No
## 133 Yes No No
## 134 Yes No No
## 135 Yes No No
## 136 No No Yes
## 137 No No Yes
## 138 No No Yes
## 139 No No Yes
## 140 No No Yes
## 141 No No Yes
## 142 No No Yes
## 143 No No Yes
## 144 No No Yes
## 145 No No Yes
## 146 No No Yes
## 147 No No Yes
## 148 Yes No Yes
## 149 Yes No Yes
## 150 Yes No Yes
## 151 Yes No Yes
## 152 Yes No Yes
## 153 Yes No Yes
## 154 No No No
## 155 No No No
## 156 No No No
## 157 No No No
## 158 No No No
## 159 No No No
## 160 No No No
## 161 No No No
## 162 No No No
## 163 No No No
## 164 No No No
## 165 No No No
## 166 No No No
## 167 No No No
## 168 No No No
## 169 No No No
## 170 Yes No No
## 171 Yes No No
## 172 Yes No No
## 173 Yes No No
## 174 Yes No No
## 175 Yes No No
## 176 Yes No No
## 177 Yes No No
## 178 Yes No No
## 179 Yes No No
## 180 Yes No No
## 181 Yes No No
## 182 Yes No No
## 183 Yes No No
## 184 Yes No No
## 185 Yes No No
## 186 Yes No No
## 187 Yes No No
## 188 Yes No No
## 189 Yes No No
## 190 Yes No No
## 191 Yes No No
## 192 Yes No No
## 193 Yes No No
## 194 Yes No No
## 195 Yes No No
## 196 No No Yes
## 197 No No Yes
## 198 No No Yes
## 199 No No Yes
## 200 No No Yes
## 201 No No Yes
## 202 No No Yes
## 203 No No Yes
## 204 No No Yes
## 205 Yes No Yes
## 206 Yes No Yes
## 207 Yes No Yes
## 208 Yes No Yes
## 209 Yes No Yes
## 210 Yes No Yes
## 211 Yes No Yes
## 212 Yes No Yes
## 213 Yes No Yes
## 214 Yes No Yes
## 215 Yes No Yes
## 216 Yes No Yes
## 217 Yes No Yes
## 218 Yes No Yes
## 219 Yes No Yes
## 220 No No No
## 221 No No No
## 222 No No No
## 223 No No No
## 224 No No No
## 225 No No No
## 226 No No No
## 227 No No No
## 228 No No No
## 229 No No No
## 230 No No No
## 231 No No No
## 232 No No No
## 233 No No No
## 234 No No No
## 235 No No No
## 236 Yes No No
## 237 Yes No No
## 238 Yes No No
## 239 Yes No No
## 240 Yes No No
## 241 Yes No No
## 242 Yes No No
## 243 Yes No No
## 244 Yes No No
## 245 Yes No No
## 246 Yes No No
## 247 Yes No No
## 248 No No Yes
## 249 No No Yes
## 250 No No Yes
## 251 No No Yes
## 252 No No Yes
## 253 No No Yes
## 254 No No Yes
## 255 No No Yes
## 256 Yes No Yes
## 257 Yes No Yes
## 258 Yes No Yes
## 259 Yes No Yes
## 260 No No No
## 261 No No No
## 262 No No No
## 263 No No No
## 264 No No No
## 265 No No No
## 266 No No No
## 267 No No No
## 268 No No No
## 269 No No No
## 270 No No No
## 271 No No No
## 272 No No No
## 273 No No No
## 274 No No No
## 275 No No No
## 276 Yes No No
## 277 Yes No No
## 278 Yes No No
## 279 Yes No No
## 280 Yes No No
## 281 Yes No No
## 282 Yes No No
## 283 Yes No No
## 284 Yes No No
## 285 Yes No No
## 286 Yes No No
## 287 Yes No No
## 288 Yes No No
## 289 Yes No No
## 290 Yes No No
## 291 Yes No No
## 292 Yes No No
## 293 Yes No No
## 294 Yes No No
## 295 Yes No No
## 296 Yes No No
## 297 Yes No No
## 298 No No Yes
## 299 No No Yes
## 300 No No Yes
## 301 No No Yes
## 302 No No Yes
## 303 No No Yes
## 304 No No Yes
## 305 No No Yes
## 306 No No Yes
## 307 No No Yes
## 308 No No Yes
## 309 Yes No Yes
## 310 Yes No Yes
## 311 Yes No Yes
## 312 Yes No Yes
## 313 Yes No Yes
## 314 Yes No Yes
## 315 Yes No Yes
## 316 Yes Yes Yes
## 317 Yes Yes Yes
## Land.Water.Mask Possible.snow.ice Possible.shadow
## 1 Land (Nothing else but land) No No
## 2 Land (Nothing else but land) No No
## 3 Land (Nothing else but land) No No
## 4 Land (Nothing else but land) No No
## 5 Land (Nothing else but land) No No
## 6 Land (Nothing else but land) No No
## 7 Land (Nothing else but land) No No
## 8 Land (Nothing else but land) No No
## 9 Land (Nothing else but land) No No
## 10 Land (Nothing else but land) No No
## 11 Land (Nothing else but land) No No
## 12 Land (Nothing else but land) No No
## 13 Land (Nothing else but land) No No
## 14 Land (Nothing else but land) No No
## 15 Land (Nothing else but land) No No
## 16 Land (Nothing else but land) No No
## 17 Land (Nothing else but land) No No
## 18 Land (Nothing else but land) No No
## 19 Land (Nothing else but land) No No
## 20 Land (Nothing else but land) No No
## 21 Land (Nothing else but land) No No
## 22 Land (Nothing else but land) No No
## 23 Land (Nothing else but land) No No
## 24 Land (Nothing else but land) No No
## 25 Land (Nothing else but land) No No
## 26 Land (Nothing else but land) No No
## 27 Land (Nothing else but land) No No
## 28 Land (Nothing else but land) No No
## 29 Land (Nothing else but land) No No
## 30 Land (Nothing else but land) No No
## 31 Land (Nothing else but land) No No
## 32 Land (Nothing else but land) No No
## 33 Land (Nothing else but land) No No
## 34 Land (Nothing else but land) No No
## 35 Land (Nothing else but land) No No
## 36 Land (Nothing else but land) No No
## 37 Land (Nothing else but land) No No
## 38 Land (Nothing else but land) No No
## 39 Land (Nothing else but land) No No
## 40 Land (Nothing else but land) No No
## 41 Land (Nothing else but land) No No
## 42 Land (Nothing else but land) No No
## 43 Land (Nothing else but land) No No
## 44 Land (Nothing else but land) No No
## 45 Land (Nothing else but land) No No
## 46 Land (Nothing else but land) No No
## 47 Land (Nothing else but land) No No
## 48 Land (Nothing else but land) No No
## 49 Land (Nothing else but land) No No
## 50 Land (Nothing else but land) No No
## 51 Land (Nothing else but land) No No
## 52 Land (Nothing else but land) No No
## 53 Land (Nothing else but land) No No
## 54 Land (Nothing else but land) No No
## 55 Land (Nothing else but land) No No
## 56 Land (Nothing else but land) No No
## 57 Land (Nothing else but land) No No
## 58 Land (Nothing else but land) No No
## 59 Ocean coastlines and lake shorelines No No
## 60 Ocean coastlines and lake shorelines No No
## 61 Ocean coastlines and lake shorelines No No
## 62 Ocean coastlines and lake shorelines No No
## 63 Ocean coastlines and lake shorelines No No
## 64 Ocean coastlines and lake shorelines No No
## 65 Ocean coastlines and lake shorelines No No
## 66 Ocean coastlines and lake shorelines No No
## 67 Ocean coastlines and lake shorelines No No
## 68 Ocean coastlines and lake shorelines No No
## 69 Ocean coastlines and lake shorelines No No
## 70 Ocean coastlines and lake shorelines No No
## 71 Ocean coastlines and lake shorelines No No
## 72 Ocean coastlines and lake shorelines No No
## 73 Ocean coastlines and lake shorelines No No
## 74 Ocean coastlines and lake shorelines No No
## 75 Ocean coastlines and lake shorelines No No
## 76 Ocean coastlines and lake shorelines No No
## 77 Ocean coastlines and lake shorelines No No
## 78 Ocean coastlines and lake shorelines No No
## 79 Ocean coastlines and lake shorelines No No
## 80 Ocean coastlines and lake shorelines No No
## 81 Ocean coastlines and lake shorelines No No
## 82 Ocean coastlines and lake shorelines No No
## 83 Ocean coastlines and lake shorelines No No
## 84 Ocean coastlines and lake shorelines No No
## 85 Ocean coastlines and lake shorelines No No
## 86 Ocean coastlines and lake shorelines No No
## 87 Ocean coastlines and lake shorelines No No
## 88 Ocean coastlines and lake shorelines No No
## 89 Ocean coastlines and lake shorelines No No
## 90 Ocean coastlines and lake shorelines No No
## 91 Ocean coastlines and lake shorelines No No
## 92 Ocean coastlines and lake shorelines No No
## 93 Ocean coastlines and lake shorelines No No
## 94 Ocean coastlines and lake shorelines No No
## 95 Ocean coastlines and lake shorelines No No
## 96 Ocean coastlines and lake shorelines No No
## 97 Ocean coastlines and lake shorelines No No
## 98 Ocean coastlines and lake shorelines No No
## 99 Shallow inland water No No
## 100 Shallow inland water No No
## 101 Shallow inland water No No
## 102 Shallow inland water No No
## 103 Shallow inland water No No
## 104 Shallow inland water No No
## 105 Shallow inland water No No
## 106 Shallow inland water No No
## 107 Shallow inland water No No
## 108 Shallow inland water No No
## 109 Shallow inland water No No
## 110 Shallow inland water No No
## 111 Shallow inland water No No
## 112 Shallow inland water No No
## 113 Shallow inland water No No
## 114 Shallow inland water No No
## 115 Shallow inland water No No
## 116 Shallow inland water No No
## 117 Shallow inland water No No
## 118 Shallow inland water No No
## 119 Shallow inland water No No
## 120 Shallow inland water No No
## 121 Shallow inland water No No
## 122 Shallow inland water No No
## 123 Shallow inland water No No
## 124 Shallow inland water No No
## 125 Shallow inland water No No
## 126 Shallow inland water No No
## 127 Shallow inland water No No
## 128 Shallow inland water No No
## 129 Shallow inland water No No
## 130 Shallow inland water No No
## 131 Shallow inland water No No
## 132 Shallow inland water No No
## 133 Shallow inland water No No
## 134 Shallow inland water No No
## 135 Shallow inland water No No
## 136 Shallow inland water No No
## 137 Shallow inland water No No
## 138 Shallow inland water No No
## 139 Shallow inland water No No
## 140 Shallow inland water No No
## 141 Shallow inland water No No
## 142 Shallow inland water No No
## 143 Shallow inland water No No
## 144 Shallow inland water No No
## 145 Shallow inland water No No
## 146 Shallow inland water No No
## 147 Shallow inland water No No
## 148 Shallow inland water No No
## 149 Shallow inland water No No
## 150 Shallow inland water No No
## 151 Shallow inland water No No
## 152 Shallow inland water No No
## 153 Shallow inland water No No
## 154 Land (Nothing else but land) No Yes
## 155 Land (Nothing else but land) No Yes
## 156 Land (Nothing else but land) No Yes
## 157 Land (Nothing else but land) No Yes
## 158 Land (Nothing else but land) No Yes
## 159 Land (Nothing else but land) No Yes
## 160 Land (Nothing else but land) No Yes
## 161 Land (Nothing else but land) No Yes
## 162 Land (Nothing else but land) No Yes
## 163 Land (Nothing else but land) No Yes
## 164 Land (Nothing else but land) No Yes
## 165 Land (Nothing else but land) No Yes
## 166 Land (Nothing else but land) No Yes
## 167 Land (Nothing else but land) No Yes
## 168 Land (Nothing else but land) No Yes
## 169 Land (Nothing else but land) No Yes
## 170 Land (Nothing else but land) No Yes
## 171 Land (Nothing else but land) No Yes
## 172 Land (Nothing else but land) No Yes
## 173 Land (Nothing else but land) No Yes
## 174 Land (Nothing else but land) No Yes
## 175 Land (Nothing else but land) No Yes
## 176 Land (Nothing else but land) No Yes
## 177 Land (Nothing else but land) No Yes
## 178 Land (Nothing else but land) No Yes
## 179 Land (Nothing else but land) No Yes
## 180 Land (Nothing else but land) No Yes
## 181 Land (Nothing else but land) No Yes
## 182 Land (Nothing else but land) No Yes
## 183 Land (Nothing else but land) No Yes
## 184 Land (Nothing else but land) No Yes
## 185 Land (Nothing else but land) No Yes
## 186 Land (Nothing else but land) No Yes
## 187 Land (Nothing else but land) No Yes
## 188 Land (Nothing else but land) No Yes
## 189 Land (Nothing else but land) No Yes
## 190 Land (Nothing else but land) No Yes
## 191 Land (Nothing else but land) No Yes
## 192 Land (Nothing else but land) No Yes
## 193 Land (Nothing else but land) No Yes
## 194 Land (Nothing else but land) No Yes
## 195 Land (Nothing else but land) No Yes
## 196 Land (Nothing else but land) No Yes
## 197 Land (Nothing else but land) No Yes
## 198 Land (Nothing else but land) No Yes
## 199 Land (Nothing else but land) No Yes
## 200 Land (Nothing else but land) No Yes
## 201 Land (Nothing else but land) No Yes
## 202 Land (Nothing else but land) No Yes
## 203 Land (Nothing else but land) No Yes
## 204 Land (Nothing else but land) No Yes
## 205 Land (Nothing else but land) No Yes
## 206 Land (Nothing else but land) No Yes
## 207 Land (Nothing else but land) No Yes
## 208 Land (Nothing else but land) No Yes
## 209 Land (Nothing else but land) No Yes
## 210 Land (Nothing else but land) No Yes
## 211 Land (Nothing else but land) No Yes
## 212 Land (Nothing else but land) No Yes
## 213 Land (Nothing else but land) No Yes
## 214 Land (Nothing else but land) No Yes
## 215 Land (Nothing else but land) No Yes
## 216 Land (Nothing else but land) No Yes
## 217 Land (Nothing else but land) No Yes
## 218 Land (Nothing else but land) No Yes
## 219 Land (Nothing else but land) No Yes
## 220 Ocean coastlines and lake shorelines No Yes
## 221 Ocean coastlines and lake shorelines No Yes
## 222 Ocean coastlines and lake shorelines No Yes
## 223 Ocean coastlines and lake shorelines No Yes
## 224 Ocean coastlines and lake shorelines No Yes
## 225 Ocean coastlines and lake shorelines No Yes
## 226 Ocean coastlines and lake shorelines No Yes
## 227 Ocean coastlines and lake shorelines No Yes
## 228 Ocean coastlines and lake shorelines No Yes
## 229 Ocean coastlines and lake shorelines No Yes
## 230 Ocean coastlines and lake shorelines No Yes
## 231 Ocean coastlines and lake shorelines No Yes
## 232 Ocean coastlines and lake shorelines No Yes
## 233 Ocean coastlines and lake shorelines No Yes
## 234 Ocean coastlines and lake shorelines No Yes
## 235 Ocean coastlines and lake shorelines No Yes
## 236 Ocean coastlines and lake shorelines No Yes
## 237 Ocean coastlines and lake shorelines No Yes
## 238 Ocean coastlines and lake shorelines No Yes
## 239 Ocean coastlines and lake shorelines No Yes
## 240 Ocean coastlines and lake shorelines No Yes
## 241 Ocean coastlines and lake shorelines No Yes
## 242 Ocean coastlines and lake shorelines No Yes
## 243 Ocean coastlines and lake shorelines No Yes
## 244 Ocean coastlines and lake shorelines No Yes
## 245 Ocean coastlines and lake shorelines No Yes
## 246 Ocean coastlines and lake shorelines No Yes
## 247 Ocean coastlines and lake shorelines No Yes
## 248 Ocean coastlines and lake shorelines No Yes
## 249 Ocean coastlines and lake shorelines No Yes
## 250 Ocean coastlines and lake shorelines No Yes
## 251 Ocean coastlines and lake shorelines No Yes
## 252 Ocean coastlines and lake shorelines No Yes
## 253 Ocean coastlines and lake shorelines No Yes
## 254 Ocean coastlines and lake shorelines No Yes
## 255 Ocean coastlines and lake shorelines No Yes
## 256 Ocean coastlines and lake shorelines No Yes
## 257 Ocean coastlines and lake shorelines No Yes
## 258 Ocean coastlines and lake shorelines No Yes
## 259 Ocean coastlines and lake shorelines No Yes
## 260 Shallow inland water No Yes
## 261 Shallow inland water No Yes
## 262 Shallow inland water No Yes
## 263 Shallow inland water No Yes
## 264 Shallow inland water No Yes
## 265 Shallow inland water No Yes
## 266 Shallow inland water No Yes
## 267 Shallow inland water No Yes
## 268 Shallow inland water No Yes
## 269 Shallow inland water No Yes
## 270 Shallow inland water No Yes
## 271 Shallow inland water No Yes
## 272 Shallow inland water No Yes
## 273 Shallow inland water No Yes
## 274 Shallow inland water No Yes
## 275 Shallow inland water No Yes
## 276 Shallow inland water No Yes
## 277 Shallow inland water No Yes
## 278 Shallow inland water No Yes
## 279 Shallow inland water No Yes
## 280 Shallow inland water No Yes
## 281 Shallow inland water No Yes
## 282 Shallow inland water No Yes
## 283 Shallow inland water No Yes
## 284 Shallow inland water No Yes
## 285 Shallow inland water No Yes
## 286 Shallow inland water No Yes
## 287 Shallow inland water No Yes
## 288 Shallow inland water No Yes
## 289 Shallow inland water No Yes
## 290 Shallow inland water No Yes
## 291 Shallow inland water No Yes
## 292 Shallow inland water No Yes
## 293 Shallow inland water No Yes
## 294 Shallow inland water No Yes
## 295 Shallow inland water No Yes
## 296 Shallow inland water No Yes
## 297 Shallow inland water No Yes
## 298 Shallow inland water No Yes
## 299 Shallow inland water No Yes
## 300 Shallow inland water No Yes
## 301 Shallow inland water No Yes
## 302 Shallow inland water No Yes
## 303 Shallow inland water No Yes
## 304 Shallow inland water No Yes
## 305 Shallow inland water No Yes
## 306 Shallow inland water No Yes
## 307 Shallow inland water No Yes
## 308 Shallow inland water No Yes
## 309 Shallow inland water No Yes
## 310 Shallow inland water No Yes
## 311 Shallow inland water No Yes
## 312 Shallow inland water No Yes
## 313 Shallow inland water No Yes
## 314 Shallow inland water No Yes
## 315 Shallow inland water No Yes
## 316 Shallow ocean Yes Yes
## 317 Moderate or continental ocean Yes Yes
Page through the table above to see the quality information contained in the LUT. Value refers to the pixel values, followed by the specific bit beginning with MODLAND. AppEEARS decodes each binary quality bit-field into more meaningful information for the user.
The case study used the following quality parameters to define their screening process:
-
MODLAND_QA flag = 0 or 1
- 0 = good quality
- 1 = check other QA
-
VI usefulness <= 11
- 0000 Highest quality = 0
- 0001 Lower quality = 1
- 0010 Decreasing quality = 2
- ...
- 1011 Decreasing quality = 11
-
Adjacent cloud detected, Mixed Clouds, and Possible shadow = 0
- 0 = No
-
Aerosol Quantity = 1 or 2
- 1 = low aerosol
- 2 = average aerosol
Below, use the pixel quality flag parameters outlined above in order to filter the data. Here, search the dataframe by column and value, and only keep the specific values that relate to the parameters outlined above.
Notice in the line:
v6_QA_lut <- v6_QA_lut[v6_QA_lut$MODLAND %in% modland,], it is including all rows where the MODLAND column is either 'VI_produced, good quality' or 'VI produced, but check other QA'.However, in the line:
v6_QA_lut <- v6_QA_lut[!v6_QA_lut$VI.Usefulness %in% VIU,], by adding the!in front of the command, the dataframe will exclude the values in the VIU variable.
# Exclude poor quality based on MODLAND
modland <- c('VI produced, good quality', 'VI produced, but check other QA')
v6_QA_lut <- v6_QA_lut[v6_QA_lut$MODLAND %in% modland,]
# Include better quality VI usefulness
VIU <- c("Lowest quality","Quality so low that it is not useful","L1B data faulty","Not useful for any other reason/not processed")
v6_QA_lut <- v6_QA_lut[!v6_QA_lut$VI.Usefulness %in% VIU,]
# Exclude climatology or high aerosol
AQ <- c('Low','Average')
v6_QA_lut <- v6_QA_lut[v6_QA_lut$Aerosol.Quantity %in% AQ,]
# Include where adjacent cloud, mixed clouds, or possible shadow were not detected
v6_QA_lut <- v6_QA_lut[v6_QA_lut$Adjacent.cloud.detected == 'No',]
v6_QA_lut <- v6_QA_lut[v6_QA_lut$Mixed.Clouds == 'No', ]
v6_QA_lut <- v6_QA_lut[v6_QA_lut$Possible.shadow == 'No',]
v6_QA_lut
## Value MODLAND VI.Usefulness
## 6 2112 VI produced, good quality Higest quality
## 8 2116 VI produced, good quality Lower quality
## 10 2177 VI produced, but check other QA Higest quality
## 11 2181 VI produced, but check other QA Lower quality
## 13 2185 VI produced, but check other QA Decreasing quality (0010)
## 65 4160 VI produced, good quality Higest quality
## 67 4164 VI produced, good quality Lower quality
## 69 4225 VI produced, but check other QA Higest quality
## 70 4229 VI produced, but check other QA Lower quality
## 72 4233 VI produced, but check other QA Decreasing quality (0010)
## 105 6208 VI produced, good quality Higest quality
## 107 6212 VI produced, good quality Lower quality
## 109 6273 VI produced, but check other QA Higest quality
## 111 6277 VI produced, but check other QA Lower quality
## 113 6281 VI produced, but check other QA Decreasing quality (0010)
## Aerosol.Quantity Adjacent.cloud.detected Atmosphere.BRDF.Correction
## 6 Low No No
## 8 Low No No
## 10 Average No No
## 11 Average No No
## 13 Average No No
## 65 Low No No
## 67 Low No No
## 69 Average No No
## 70 Average No No
## 72 Average No No
## 105 Low No No
## 107 Low No No
## 109 Average No No
## 111 Average No No
## 113 Average No No
## Mixed.Clouds Land.Water.Mask Possible.snow.ice
## 6 No Land (Nothing else but land) No
## 8 No Land (Nothing else but land) No
## 10 No Land (Nothing else but land) No
## 11 No Land (Nothing else but land) No
## 13 No Land (Nothing else but land) No
## 65 No Ocean coastlines and lake shorelines No
## 67 No Ocean coastlines and lake shorelines No
## 69 No Ocean coastlines and lake shorelines No
## 70 No Ocean coastlines and lake shorelines No
## 72 No Ocean coastlines and lake shorelines No
## 105 No Shallow inland water No
## 107 No Shallow inland water No
## 109 No Shallow inland water No
## 111 No Shallow inland water No
## 113 No Shallow inland water No
## Possible.shadow
## 6 No
## 8 No
## 10 No
## 11 No
## 13 No
## 65 No
## 67 No
## 69 No
## 70 No
## 72 No
## 105 No
## 107 No
## 109 No
## 111 No
## 113 No
6. Set Custom QA/QC Filtering (Mask)
In this section, demonstrate how to take the list of possible values (based on quality requirements) above, and mask the EVI and quality arrays to only include pixels where the quality value is in the list of 'good quality' values based on the parameters from the case study.
# Print list of possible QA values based on parameters above
v6_QA_mask <- v6_QA_lut$Value
v6_QA_mask
## [1] 2112 2116 2177 2181 2185 4160 4164 4225 4229 4233 6208 6212 6273 6277
## [15] 6281
So above, only pixels with those specific _1_km_16_days_VI_Quality values will be included after masking below.
Below, mask the first observation for the datasets. Notice the ! in front of the array, meaning that you don't want to exclude the list of values above, but rather exclude every other value.
# Apply QA mask to the EVI data
v6_EVI[!v6_QA %in% v6_QA_mask,]<- NA
v6_EVI_qamasked <- v6_EVI
7. Visualize NetCDF-4 Data
In this section, begin by highlighting the functionality of plotting in R using the Raster package. Start by making a basic plot of the EVI data, then add some additional parameters to the plot, and ultimately export the finished plot to an image file.
# Visualize a basic plot:
plot(v6_EVI_original)
Above is a basic Raster plot of the EVI array over the Amazon basin.
Next, add some additional parameters to the visualization.
Here, use cellStats() from the Raster package to calculate statistics on an entire raster array.
# Min, Max, and Mean
print(paste0('Min NDVI:', cellStats(v6_EVI_original, stat='min', na.rm=TRUE)))
## [1] "Min NDVI:-0.2"
print(paste0('Max NDVI:', cellStats(v6_EVI_original, stat='max', na.rm=TRUE)))
## [1] "Max NDVI:0.9059"
print(paste0('Mean NDVI:',cellStats(v6_EVI_original, stat='mean', na.rm=TRUE)))
## [1] "Mean NDVI:0.415054825025268"
# Import additional colormaps
library(RColorBrewer)
# Create custom colormap
YlGn <- brewer.pal(9, "YlGn")
# Plot the unfiltered data for time step 1, using a colormap and setting a custom linear stretch
plot(v6_EVI_original, zlim=c(-0.2,1.0), maxpixels = 20000000, col = YlGn, xaxt='n', yaxt='n')
Above is a better visualization of the data, but it is still missing important items such as a title and a colormap legend.
Below, plot the quality filtered data, add a figure title, subtitle, colormap legend, and export the result to a .png file in the output directory.
# Plot the masked data
plot(v6_EVI, zlim=c(-0.2,1.0), maxpixels = 20000000, col = YlGn, xaxt='n', yaxt='n', legend.args=list(text='EVI', side=4, font=2, line=2.5, cex=0.8))
title("MODIS Version 6 Enhanced Vegetation Index Quality Filtered Data\nAmazon Rainforest: 07-12-2005", line = 0.21)
# Export difference map to a png image file.
file_name <- strsplit(file_list[2], '.nc')[[1]]
invisible(dev.copy(png, paste0(out_dir,file_name,'_EVI.png'),width = 700, height = 600))
invisible(dev.off())
8. Mask by Land Cover Type
The case study also limited the analysis to only "forest" land cover types (LCT). Below, you can also limit your analysis based on LCT.
# Use MCD12Q1.051 to define land cover types
file_list
## [1] "MCD12Q1.051_500m_aid0001.nc" "MOD13A2.006_1km_aid0001.nc"
# Set to 'MCD12Q1.051_aid0001.nc' and open
lct_file <- nc_open(file_list[1])
# Print a list of variables in file
attributes(lct_file$var)$names
## [1] "crs" "Land_Cover_Type_1" "Land_Cover_Type_QC"
# Print a list of dimensions in file
attributes(lct_file$dim)$names
## [1] "time" "lat" "lon"
# Show the variable metadata
lct_info <- ncatt_get(lct_file, "Land_Cover_Type_1")
lct_info
## $`_FillValue`
## [1] 255
##
## $coordinates
## [1] "time lat lon"
##
## $grid_mapping
## [1] "crs"
##
## $valid_min
## [1] 0
##
## $valid_max
## [1] 254
##
## $long_name
## [1] "Land_Cover_Type_1"
##
## $units
## [1] "class number"
##
## $water
## [1] 0
##
## $evergreen_needleleaf_forest
## [1] 1
##
## $evergreen_broadleaf_forest
## [1] 2
##
## $deciduous_needleleaf_forest
## [1] 3
##
## $deciduous_broadleaf_forest
## [1] 4
##
## $mixed_forests
## [1] 5
##
## $closed_shrubland
## [1] 6
##
## $open_shrublands
## [1] 7
##
## $woody_savannas
## [1] 8
##
## $savannas
## [1] 9
##
## $grasslands
## [1] 10
##
## $permanent_wetlands
## [1] 11
##
## $croplands
## [1] 12
##
## $urban_and_built_up
## [1] 13
##
## $cropland_natural_vegetation_mosaic
## [1] 14
##
## $snow_and_ice
## [1] 15
##
## $barren_or_sparsely_vegetated
## [1] 16
##
## $unclassified
## [1] -2
Above, notice the class number assignments. Below, mask the data to only include forest LCTs.
In the study, the researchers used the MOD12Q1 Collection 5 Land Cover product to identify forest pixels in their study area. This tutorial uses the newer MCD12Q1 Version 5.1 Land Cover Type product to only include pixels in our study region that are classified as one of five forest types:
- evergreen_needleleaf_forest: 1
- evergreen_broadleaf_forest: 2
- deciduous_needleleaf_forest: 3
- deciduous_broadleaf_forest: 4
- mixed_forests: 5
# Set lat and lon arrays for LCT data
lat_LCT <- ncvar_get(lct_file, "lat")
lon_LCT <- ncvar_get(lct_file, "lon")
# Open the Land_Cover_Type_1 dataset
lct <- raster(t(ncvar_get(lct_file, "Land_Cover_Type_1")), xmn=min(lon_LCT), xmx=max(lon_LCT), ymn=min(lat_LCT), ymx=max(lat_LCT), crs=crs)
# Make a list of the forest land cover types outlined above
lct_forest <- c(1,2,3,4,5)
# Mask the LCT dataset to only include forest LCT
lct[!lct %in% lct_forest,]<- NA
# Close NetCDF-4 file
nc_close(lct_file)
rm(lat_LCT, lon_LCT)
Before using the newly created forest LCT mask to filter out non-forest pixels from the EVI data, the 500 m LCT data will need to be resampled to the same resolution as the EVI data (1000 m). Below, the resample() command is used to resample the data from 500 m to 1000 m using nearest neighbor resampling.
# Resample by a factor of 2 with nearest neighbor interpolation:
resampled_lct <- resample(lct, v6_EVI, method='ngb')
rm(lct)
# Next, apply LCT mask to the EVI data
v6_EVI[!resampled_lct %in% lct_forest,]<- NA
Now, visualize the: (1) original data, (2) quality screened data, and (3) quality screened forest-only data by creating 3 subplots, and plotting them side by side.
par(mfrow=c(1,3),oma = c(0, 0, 5, 0), pty = 's')
image(v6_EVI_original,zlim=c(-0.2,1.0), col = YlGn, xlab = 'Longitude', ylab = 'Latitude', main = 'Original Data')
image(v6_EVI_qamasked,zlim=c(-0.2,1.0), col = YlGn,xlab = '', ylab = '', xaxt='n', yaxt='n', main ='Quality Filtered Data')
image(v6_EVI,zlim=c(-0.2,1.0), col = YlGn,xlab = '', ylab = '',xaxt='n', yaxt='n', main ='Quality Filtered Forest Data')
mtext("MODIS Version 6 Enhanced Vegetation Index (EVI) Quality Filtered Data\nAmazon Rainforest: 07-12-2005", outer = TRUE, cex = 1.5)
9. Visualize Data with GIS Layers
Next, import a package called rworldmap, which allows you to add other useful layers such as country boundaries and coastlines.
In the first example, define a colormap for the LCT data and plot with coastlines and country borders.
cmap1 <- c(rgb(0.149,0.451,0), rgb(0.220,0.659,0),rgb(0.298,0.902,0), rgb(0.639,1,0.451), rgb(0.266,0.537,0.439))
Below, import rworldmap, set up the lat/lon grid, generate the basemap, draw the desired borders, and ultimately plot the lct (forest only) data, assigning it to the colormap generated above.
# Add `dev.off()` if running directly in console to clear parameters from previous plot
invisible(library(rworldmap))
## ### Welcome to rworldmap ###
## For a short introduction type : vignette('rworldmap')
# Import country borders
data(countriesLow)
# Plot the data
image(resampled_lct, xlab = 'Longitude', ylab = 'Latitude',xlim = c(-83,-34), ylim = c(-20,13), useRaster = TRUE, col = cmap1, main = "Forest Land Cover Types of the Amazon Basin (2005)")
# Set the background color to light blue
rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = "lightblue")
# Plot the country borders filled in as white
plot(countriesLow ,col = 'white', add = TRUE)
# Plot the data again on top of the other layers
image(resampled_lct, add = T, useRaster = TRUE, col = cmap1)
# Plot the borders on top of the LCT data
plot(countriesLow, add = T)
10. Generate Statistics on Data
Below, look at the mean EVI values for the Amazon region from July 12, 2005. Notice that differences arise based on (1) quality filtering (or lack thereof) and (2) land cover type used in calculating the mean EVI.
# Call cellStats, set stat to mean, remove, NA values, and print results
print(paste0('Version 6 all quality mean EVI: ', cellStats(v6_EVI_original, stat='mean', na.rm=TRUE)))
## [1] "Version 6 all quality mean EVI: 0.415054825025268"
print(paste0('Version 6 quality filtered mean EVI: ', cellStats(v6_EVI_qamasked, stat='mean', na.rm=TRUE)))
## [1] "Version 6 quality filtered mean EVI: 0.41557945279398"
print(paste0('Version 6 quality filtered mean (forests) EVI: ', cellStats(v6_EVI, stat='mean', na.rm=TRUE)))
## [1] "Version 6 quality filtered mean (forests) EVI: 0.497680360773332"
rm(v6_EVI, v6_EVI_original,v6_EVI_qamasked)
11. Automate QA/QC Filtering
Here loop through and mask arrays by quality for multiple time steps in a NetCDF-4 file.
# Grab the EVI and VI Quality datasets and set to a variable
v6_EVI <- ncvar_get(file_in, "_1_km_16_days_EVI")
v6_QA <- ncvar_get(file_in, "_1_km_16_days_VI_Quality")
# See how many time steps the file contains
dim(v6_EVI)
## [1] 4200 3600 7
So, the NetCDF-4 file contains 6 timesteps, or layers. Below, use a 'for' loop and indexing (i) in order to loop through each layer and filter based on quality and land cover type.
# Define a function to apply scale factor
apply_sf <- function(x){as.integer(x*10000)}
# Loop through all timesteps (observations) in the file, mask out poor quality and exclude non-forest pixels
for (i in 1:file_in$dim$time$len){
v6_EVI_1 <- raster(t(v6_EVI[,,i]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
v6_QA_1 <- raster(t(v6_QA[,,i]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
# Apply QA mask to the EVI data
v6_EVI_1[!v6_QA_1 %in% v6_QA_mask,]<- NA
rm(v6_QA_1)
# Next, apply LCT mask to the EVI data
v6_EVI_1[!resampled_lct %in% lct_forest,]<- NA
v6_EVI_1<- calc(v6_EVI_1, apply_sf)
v6_EVI[,,i] <- t(v6_EVI_1[,,1])
rm(v6_EVI_1)
}
rm(resampled_lct, v6_QA)
12. Visualize Time Series Data
Below, set up subplots to visualize all 6 timesteps (observations) on the same plot.
par(mfrow=c(2,3),oma = c(0, 0, 3, 0))
#title(, line = 0.21)
image(raster(t(v6_EVI[,,1]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn, xlab = 'Longitude', ylab = 'Latitude', main = '07-12-2005')
rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = "lightblue")
plot(countriesLow,col = 'white', add = TRUE)
image(raster(t(v6_EVI[,,1]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn, add = T)
plot(countriesLow, add = T)
image(raster(t(v6_EVI[,,2]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn,xlab = '', ylab = '', xaxt='n', yaxt='n', main ='07-28-2005')
rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = "lightblue")
plot(countriesLow,col = 'white', add = TRUE)
image(raster(t(v6_EVI[,,2]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn, add = T)
plot(countriesLow, add = T)
image(raster(t(v6_EVI[,,3]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn,xlab = '', ylab = '',xaxt='n', yaxt='n', main ='08-13-2005')
rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = "lightblue")
plot(countriesLow,col = 'white', add = TRUE)
image(raster(t(v6_EVI[,,3]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn, add = T)
plot(countriesLow, add = T)
image(raster(t(v6_EVI[,,4]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn, xlab = '', ylab = '', xaxt='n', yaxt='n', main = '08-29-2005')
rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = "lightblue")
plot(countriesLow,col = 'white', add = TRUE)
image(raster(t(v6_EVI[,,4]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn, add = T)
plot(countriesLow, add = T)
image(raster(t(v6_EVI[,,5]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn,xlab = '', ylab = '', xaxt='n', yaxt='n', main ='09-14-2005')
rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = "lightblue")
plot(countriesLow,col = 'white', add = TRUE)
image(raster(t(v6_EVI[,,5]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn, add = T)
plot(countriesLow, add = T)
image(raster(t(v6_EVI[,,6]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn,xlab = '', ylab = '',xaxt='n', yaxt='n', main ='09-30-2005')
rect(par("usr")[1],par("usr")[3],par("usr")[2],par("usr")[4],col = "lightblue")
plot(countriesLow,col = 'white', add = TRUE)
image(raster(t(v6_EVI[,,6]), xmn=min(lon_EVI), xmx=max(lon_EVI), ymn=min(lat_EVI), ymx=max(lat_EVI), crs=crs)
,zlim=c(-200,10000), col = YlGn, add = T)
plot(countriesLow, add = T)
mtext('MODIS Version 6 Enhanced Vegetation Index (EVI): 07-12-2005 to 09-30-2005', outer = TRUE, cex = 1.5)
13. Export NetCDF-4 Files
Lastly, demonstrate how to copy the original NetCDF-4 file, insert the quality and LCT-masked data, and save to a new NetCDF-4 file.
# Set up output file name
v6_outfile_name <- paste0(out_dir,strsplit(file_list[2], '.nc')[[1]] ,'_masked.nc')
# Copy the original file, insert quality filtered data, and close file
system(paste('cp',file_list[2],v6_outfile_name))
v6_file_out_new <- nc_open(v6_outfile_name, write = TRUE)
ncvar_put(v6_file_out_new, "_1_km_16_days_EVI", v6_EVI)
nc_close(v6_file_out_new)
Contact Information
Material written by Cole Krehbiel1
Contact: LPDAAC@usgs.gov
Voice: +1-605-594-6116
Organization: Land Processes Distributed Active Archive Center (LP DAAC)
Website: https://lpdaac.usgs.gov/
Date last modified: 12-06-2017
1 Innovate! Inc., contractor to the U.S. Geological Survey, Earth Resources Observation and Science (EROS) Center, Sioux Falls, South Dakota, USA. Work performed under USGS contract G15PD00467 for LP DAAC2.
2 LP DAAC Work performed under NASA contract NNG14HH33I.