Programmatic interface to the ‘MODIS Land Products Subsets’ web services. Allows for easy downloads of ‘MODIS’ time series directly to your R workspace or your computer. When using the package please cite the manuscript as referenced below. Keep in mind that the original manuscript describes versions prior to release 1.0 of the package. Functions described in this manuscript do not exist in the current package, please consult the documentation to find matching functionality.
Installation
stable release
To install the current stable release use a CRAN repository:
install.packages("MODISTools") library("MODISTools")
development release
To install the development releases of the package run the following commands:
if(!require(devtools)){install.package("devtools")} devtools::install_github("khufkens/MODISTools") library("MODISTools")
Vignettes are not rendered by default, if you want to include additional documentation please use:
if(!require(devtools)){install.package("devtools")} devtools::install_github("khufkens/MODISTools", build_vignettes = TRUE) library("MODISTools")
Use
Downloading MODIS time series
To extract a time series of modis data for a given location and its direct environment use the mt_subset() function.
detailed parameter description (click to expand)
| Parameter | Description |
|---|---|
| product | a MODIS product |
| band | a MODIS product band (if NULL all bands are downloaded) |
| lat | latitude of the site |
| lon | longitude of the site |
| start | start year of the time series (data start in 1980) |
| end | end year of the time series (current year - 2 years, use force = TRUE to override) |
| internal | logical, TRUE or FALSE, if true data is imported into R workspace otherwise it is downloaded into the current working directory |
| out_dir | path where to store the data when not used internally, defaults to tempdir() |
| km_lr | force “out of temporal range” downloads (integer) |
| km_ab | suppress the verbose output (integer) |
| site_name | a site identifier |
| site_id | a site_id for predefined locations (not required) |
| progress | logical, TRUE or FALSE (show download progress) |
# load the library library(MODISTools) # download data subset <- mt_subset(product = "MOD11A2", lat = 40, lon = -110, band = "LST_Day_1km", start = "2004-01-01", end = "2004-02-01", km_lr = 1, km_ab = 1, site_name = "testsite", internal = TRUE, progress = FALSE) print(str(subset)) #> 'data.frame': 36 obs. of 21 variables: #> $ xllcorner : chr "-9370962.97" "-9370962.97" "-9370962.97" "-9370962.97" ... #> $ yllcorner : chr "4446875.49" "4446875.49" "4446875.49" "4446875.49" ... #> $ cellsize : chr "926.625433055834" "926.625433055834" "926.625433055834" "926.625433055834" ... #> $ nrows : int 3 3 3 3 3 3 3 3 3 3 ... #> $ ncols : int 3 3 3 3 3 3 3 3 3 3 ... #> $ band : chr "LST_Day_1km" "LST_Day_1km" "LST_Day_1km" "LST_Day_1km" ... #> $ units : chr "Kelvin" "Kelvin" "Kelvin" "Kelvin" ... #> $ scale : chr "0.02" "0.02" "0.02" "0.02" ... #> $ latitude : num 40 40 40 40 40 40 40 40 40 40 ... #> $ longitude : num -110 -110 -110 -110 -110 -110 -110 -110 -110 -110 ... #> $ site : chr "testsite" "testsite" "testsite" "testsite" ... #> $ product : chr "MOD11A2" "MOD11A2" "MOD11A2" "MOD11A2" ... #> $ start : chr "2004-01-01" "2004-01-01" "2004-01-01" "2004-01-01" ... #> $ end : chr "2004-02-01" "2004-02-01" "2004-02-01" "2004-02-01" ... #> $ complete : logi TRUE TRUE TRUE TRUE TRUE TRUE ... #> $ modis_date : chr "A2004001" "A2004009" "A2004017" "A2004025" ... #> $ calendar_date: chr "2004-01-01" "2004-01-09" "2004-01-17" "2004-01-25" ... #> $ tile : chr "h09v05" "h09v05" "h09v05" "h09v05" ... #> $ proc_date : chr "2015212185706" "2015212201022" "2015212213103" "2015213005429" ... #> $ pixel : int 1 1 1 1 2 2 2 2 3 3 ... #> $ value : int 13135 13120 13350 13354 13123 13100 13324 13331 13098 13069 ... #> NULL
The output format is a tidy data frame, as shown above. When witten to a csv with the parameter internal = FALSE this will result in a flat file on disk.
Note that when a a region is defined using km_lr and km_ab multiple pixels might be returned. These are indexed using the pixel column in the data frame containing the time series data. The remote sensing values are listed in the value column. When no band is specified all bands of a given product are returned, be mindful of the fact that different bands might require different multipliers to represent their true values. To list all available products, bands for particular products and temporal coverage see function descriptions below.
Batch downloading MODIS time series
When a large selection of locations is needed you might benefit from using the batch download function mt_batch_subset(), which provides a wrapper around the mt_subset() function in order to speed up large download batches. This function has a similar syntax to mt_subset() but requires a data frame defining site names (site_name) and locations (lat / lon) (or a comma delimited file with the same structure) to specify a list of download locations.
Below an example is provided on how to batch download data for a data frame of given site names and locations (lat / lon).
# create data frame with a site_name, lat and lon column # holding the respective names of sites and their location df <- data.frame("site_name" = paste("test",1:2)) df$lat <- 40 df$lon <- -110 # test batch download subsets <- mt_batch_subset(df = df, product = "MOD11A2", band = "LST_Day_1km", internal = TRUE, start = "2004-01-01", end = "2004-02-01") print(str(subsets)) #> 'data.frame': 8 obs. of 21 variables: #> $ xllcorner : chr "-9370036.39" "-9370036.39" "-9370036.39" "-9370036.39" ... #> $ yllcorner : chr "4447802.08" "4447802.08" "4447802.08" "4447802.08" ... #> $ cellsize : chr "926.625433055834" "926.625433055834" "926.625433055834" "926.625433055834" ... #> $ nrows : int 1 1 1 1 1 1 1 1 #> $ ncols : int 1 1 1 1 1 1 1 1 #> $ band : chr "LST_Day_1km" "LST_Day_1km" "LST_Day_1km" "LST_Day_1km" ... #> $ units : chr "Kelvin" "Kelvin" "Kelvin" "Kelvin" ... #> $ scale : chr "0.02" "0.02" "0.02" "0.02" ... #> $ latitude : num 40 40 40 40 40 40 40 40 #> $ longitude : num -110 -110 -110 -110 -110 -110 -110 -110 #> $ site : chr "test 1" "test 1" "test 1" "test 1" ... #> $ product : chr "MOD11A2" "MOD11A2" "MOD11A2" "MOD11A2" ... #> $ start : chr "2004-01-01" "2004-01-01" "2004-01-01" "2004-01-01" ... #> $ end : chr "2004-02-01" "2004-02-01" "2004-02-01" "2004-02-01" ... #> $ complete : logi TRUE TRUE TRUE TRUE TRUE TRUE ... #> $ modis_date : chr "A2004001" "A2004009" "A2004017" "A2004025" ... #> $ calendar_date: chr "2004-01-01" "2004-01-09" "2004-01-17" "2004-01-25" ... #> $ tile : chr "h09v05" "h09v05" "h09v05" "h09v05" ... #> $ proc_date : chr "2015212185706" "2015212201022" "2015212213103" "2015213005429" ... #> $ pixel : int 1 1 1 1 1 1 1 1 #> $ value : int 13098 13062 13297 13323 13098 13062 13297 13323 #> NULL
Listing products
To list all available products use the mt_products() function.
products <- mt_products() head(products) #> product #> 1 Daymet #> 2 MCD12Q1 #> 3 MCD12Q2 #> 4 MCD15A2H #> 5 MCD15A3H #> 6 MCD19A3 #> description #> 1 Daily Surface Weather Data (Daymet) on a 1-km Grid for North America, Version 3 #> 2 MODIS/Terra+Aqua Land Cover Type (LC) Yearly L3 Global 500 m SIN Grid #> 3 MODIS/Terra+Aqua Land Cover Dynamics (LCD) Yearly L3 Global 500 m SIN Grid #> 4 MODIS/Terra+Aqua Leaf Area Index/FPAR (LAI/FPAR) 8-Day L4 Global 500 m SIN Grid #> 5 MODIS/Terra+Aqua Leaf Area Index/FPAR (LAI/FPAR) 4-Day L4 Global 500 m SIN Grid #> 6 MODIS/Terra+Aqua BRDF Model Parameters (MAIAC) 8-Day L3 Global 1 km SIN Grid #> frequency resolution_meters #> 1 1 day 1000 #> 2 1 year 500 #> 3 1 year 500 #> 4 8 day 500 #> 5 4 day 500 #> 6 8 day 1000
Listing bands
To list all available bands for a given product use the mt_bands() function.
bands <- mt_bands(product = "MOD11A2") head(bands) #> band description valid_range fill_value #> 1 Clear_sky_days Day clear-sky coverage 1 to 255 0 #> 2 Clear_sky_nights Night clear-sky coverage 1 to 255 0 #> 3 Day_view_angl View zenith angle of day observation 0 to 130 255 #> 4 Day_view_time Local time of day observation 0 to 240 255 #> 5 Emis_31 Band 31 emissivity 1 to 255 0 #> 6 Emis_32 Band 32 emissivity 1 to 255 0 #> units scale_factor add_offset #> 1 <NA> <NA> <NA> #> 2 <NA> <NA> <NA> #> 3 degree 1 -65 #> 4 hrs 0.1 0 #> 5 <NA> 0.002 0.49 #> 6 <NA> 0.002 0.49
References
Tuck et al. (2014). MODISTools - downloading and processing MODIS remotely sensed data in R Ecology & Evolution, 4(24), 4658 - 4668.
Acknowledgements
Original development was supported by the UK Natural Environment Research Council (NERC; grants NE/K500811/1 and NE/J011193/1), and the Hans Rausing Scholarship. Refactoring was supported through the Belgian Science Policy office COBECORE project (BELSPO; grant BR/175/A3/COBECORE). Logo design elements are taken from the FontAwesome library according to these terms, where the globe element was inverted and intersected.