MODISTools

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.

Please cite the package in your work as:

Koen Hufkens. (2023). bluegreen-labs/MODISTools: MODISTools v1.1.5. Zenodo. https://doi.org/10.5281/zenodo.7551164

Installation

stable release

To install the current stable release use a CRAN repository:

r install.packages("MODISTools") library("MODISTools")

development release

To install the development releases of the package run the following commands:

r if(!require(remotes)){install.package("remotes")} remotes::install_github("bluegreen-labs/MODISTools") library("MODISTools")

Vignettes are not rendered by default, if you want to include additional documentation please use:

r if(!require(remotes)){install.package("remotes")} remotes::install_github("bluegreen-labs/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.

``` r

load the library

library(MODISTools)

download data

subset <- mtsubset(product = "MOD11A2", lat = 40, lon = -110, band = "LSTDay1km", start = "2004-01-01", end = "2004-02-01", kmlr = 1, kmab = 1, sitename = "testsite", internal = TRUE, progress = FALSE) print(str(subset)) ```

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 kmlr and kmab 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).

``` r

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 <- mtbatchsubset(df = df, product = "MOD11A2", band = "LSTDay1km", internal = TRUE, start = "2004-01-01", end = "2004-02-01")

print(str(subsets)) ```

Listing products

To list all available products use the mt_products() function.

r products <- mt_products() head(products)

Listing bands

To list all available bands for a given product use the mt_bands() function.

r bands <- mt_bands(product = "MOD11A2") head(bands)

listing dates

To list all available dates (temporal coverage) for a given product and location use the mt_dates() function.

r dates <- mt_dates(product = "MOD11A2", lat = 42, lon = -110) head(dates)

References

Koen Hufkens. (2023). bluegreen-labs/MODISTools: MODISTools v1.1.5. Zenodo. https://doi.org/10.5281/zenodo.7551164

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.