Package {modisfast}

Title:	Fast and Efficient Access to MODIS Earth Observation Data
Version:	2.0.0
Description:	Programmatic interface to several NASA Earth Observation 'OPeNDAP' servers (Open-source Project for a Network Data Access Protocol) (https://www.opendap.org/). Allows for easy downloads of MODIS subsets, as well as other Earth Observation datacubes, in a time-saving and efficient way : by sampling it at the very downloading phase (spatially, temporally and dimensionally).
License:	GPL (≥ 3)
URL:	https://github.com/ptaconet/modisfast
BugReports:	https://github.com/ptaconet/modisfast/issues
Depends:	R (≥ 3.5)
Imports:	curl, dplyr, httr, jsonlite, lubridate, magrittr, parallel, purrr, rvest, sf, stringr, terra, xml2, cli
Suggests:	ggplot2, knitr, mapview, rmarkdown, spelling, testthat
Encoding:	UTF-8
LazyData:	true
Language:	en-US
Config/testthat/start-first:	mf_list_collections, mf_list_variables, mf_get_url, mf_download_data, mf_import_data
Config/roxygen2/version:	8.1.0
NeedsCompilation:	no
Packaged:	2026-09-29 14:14:54 UTC; ptaconet
Author:	Paul Taconet [aut, cre, cph], Nicolas Moiroux [fnd], French National Research Institute for Sustainable Development, IRD [fnd]
Maintainer:	Paul Taconet <paul.taconet@gmail.com>
Repository:	CRAN
Date/Publication:	2026-09-30 21:00:08 UTC

modisfast: Fast and Efficient Access to MODIS Earth Observation Data

Description

Programmatic interface to several NASA Earth Observation 'OPeNDAP' servers (Open-source Project for a Network Data Access Protocol) (https://www.opendap.org/). Allows for easy downloads of MODIS subsets, as well as other Earth Observation datacubes, in a time-saving and efficient way : by sampling it at the very downloading phase (spatially, temporally and dimensionally).

Author(s)

Maintainer: Paul Taconet paul.taconet@gmail.com (ORCID) [copyright holder]

Authors:

• Paul Taconet paul.taconet@gmail.com (ORCID) [copyright holder]

Other contributors:

• Nicolas Moiroux nicolas.moiroux@ird.fr (ORCID) [funder]

• French National Research Institute for Sustainable Development, IRD [funder]

See Also

Useful links:

• https://github.com/ptaconet/modisfast

• Report bugs at https://github.com/ptaconet/modisfast/issues

Example dataset containing abundances of mosquitoes vectors of malaria. Used in article 'use_case'.

Description

Example dataset containing abundances of mosquitoes vectors of malaria. Used in article 'use_case'.

Usage

entomological_data

Format

## 'entomological_data' A data frame with 232 rows and 6 columns:

mission

number of the entomological survey

date

date of the survey

village

3-digit code for the village of the survey

X, Y

longitude and latitude of the center of the village

n

number of mosquitoes collected

Source

<https://doi.org/10.15468/v8fvyn>

Download several datasets given their URLs and destination path

Description

This function enables to download datasets. In a data import workflow, this function is typically used after a call to the mf_get_url function. The output value of mf_get_url can be used as input of parameter df_to_dl of mf_download_data.

The download can the parallelized.

Usage

mf_download_data(
  df_to_dl,
  path = tempfile("modisfast_"),
  parallel = FALSE,
  num_workers = parallel::detectCores() - 1,
  verbose = "inform",
  min_filesize = 5000
)

Arguments

Details

Parameter df_to_dl must be a data.frame with the following minimal structure :

id_roi

An id for the ROI (character string)

collection

Collection (character string)

name

url

URL of the file to download (character string)

Value

a data.frame with the same structure of the input data.frame df_to_dl + columns providing details of the data downloaded. The additional columns are :

fileDl

Booloean (dataset downloaded or failure)

dlStatus

Download status : 1 = download ok ; 2 = download error ; 3 = dataset was already existing in destination file

fileSize

File size on disk (in bites)

Examples

## Not run: 

### Configure an Earthdata bearer token for LP DAAC Cloud
Sys.setenv(EARTHDATA_TOKEN = "your Earthdata bearer token")

### Set-up parameters of interest
coll <- "VJ121A2.002"

bands <- c("LST_Day_1KM", "LST_Night_1KM")

time_range <- as.Date(c("2026-01-01", "2026-01-30"))

roi <- sf::st_as_sf(
  data.frame(
    id = "roi_test",
    geom = "POLYGON ((-5.82 9.54, -5.42 9.55, -5.41 8.84, -5.81 8.84, -5.82 9.54))"
  ),
  wkt = "geom", crs = 4326
)

### Get the URLs of the data
(urls_vj121a2 <- mf_get_url(
  collection = coll,
  variables = bands,
  roi = roi,
  time_range = time_range
))

### Download the data
res_dl <- mf_download_data(urls_vj121a2)

### Import the data as terra::SpatRast
modis_ts <- mf_import_data(dirname(res_dl$destfile[1]), collection = coll)

### Plot the data
terra::plot(modis_ts)

## End(Not run)

Find download URLs for MODIS, VIIRS and GPM data

Description

Find the files covering your area and dates, and create URLs to download only the selected bands and area. Use [mf_download_data()] to download them.

Usage

mf_get_url(
  collection,
  variables = NULL,
  roi,
  time_range,
  collection_id = NULL,
  verbose = "inform"
)

Arguments

Details

Set 'EARTHDATA_TOKEN' to your Earthdata token before calling this function.

Value

A data frame with the download URLs for [mf_download_data()].

Examples

## Not run: 
Sys.setenv(EARTHDATA_TOKEN = "your Earthdata bearer token")
roi <- sf::st_as_sf(data.frame(id = "test", geom =
  "POLYGON ((3.8 43.5, 4 43.5, 4 43.7, 3.8 43.7, 3.8 43.5))"),
  wkt = "geom", crs = 4326)
time_range <- as.Date(c("2026-01-01", "2026-01-30"))
urls_vj121a2 <- mf_get_url("VJ121A2.002",
  c("LST_Day_1KM", "LST_Night_1KM"), roi, time_range)
mf_download_data(urls_vj121a2)

## End(Not run)

Import datasets downloaded using modisfast as a terra::SpatRaster object

Description

Import datasets downloaded using modisfast as a terra::SpatRaster object

Usage

mf_import_data(
  path,
  collection,
  output_class = "SpatRaster",
  proj_epsg = NULL,
  roi_mask = NULL,
  vrt = FALSE,
  verbose = "inform",
  ...
)

Arguments

Value

a terra::SpatRast object

Note

Although the data downloaded through modisfast could be imported with any netcdf-compliant R package (terra, stars, ncdf4, etc.), care must be taken. In fact, depending on the collection, some “issues” were raised. These issues are independent from modisfast : they result most of time of a lack of full implementation of the OPeNDAP framework by the data providers. Namely, these issues are :

• for MODIS and VIIRS collections : CRS has to be provided

• for GPM collections : EPSG:4326 is assigned to the grid

The function mf_import_data includes the processing that needs to be done at the data import phase in order to safely use the data as terra objects.

Also note that reprojecting over large ROIs using the argument proj_epsg might take long. In this case, setting the argument vrt to TRUE might be a solution.

Examples

## Not run: 

### Configure an Earthdata bearer token for LP DAAC Cloud
Sys.setenv(EARTHDATA_TOKEN = "your Earthdata bearer token")

### Set-up parameters of interest
coll <- "VJ121A2.002"

bands <- c("LST_Day_1KM", "LST_Night_1KM")

time_range <- as.Date(c("2026-01-01", "2026-01-30"))

roi <- sf::st_as_sf(
  data.frame(
    id = "roi_test",
    geom = "POLYGON ((-5.82 9.54, -5.42 9.55, -5.41 8.84, -5.81 8.84, -5.82 9.54))"
  ),
  wkt = "geom", crs = 4326
)

### Get the URLs of the data
(urls_vj121a2 <- mf_get_url(
  collection = coll,
  variables = bands,
  roi = roi,
  time_range = time_range
))

### Download the data
res_dl <- mf_download_data(urls_vj121a2)

### Import the data as terra::SpatRast
modis_ts <- mf_import_data(dirname(res_dl$destfile[1]), collection = coll)

### Plot the data
terra::plot(modis_ts)

## End(Not run)

Get the collections available for download with the modisfast package

Description

Get the collections available for download using the package and a set of related information

Usage

mf_list_collections()

Value

A data.frame with the collections available, and a set of related information for each one. Main columns are :

collection

Collection short name

source

Data provider

long_name

Collection long name

doi

DOI of the collection

start_date

First available date for the collection

url_opendapserver

URL of the OPeNDAP server of the data

Examples

(head(mf_list_collections()))

Check LP DAAC MODIS and VIIRS collections on Earthdata Cloud OPeNDAP

Description

CMR is paginated to discover every LPCLOUD collection supported by modisfast. For each collection, the function tests up to 'n_granules' granules (newest first), including a single pixel data request. A successful sample does not imply that every granule or date is available.

Usage

mf_list_collections_cloud(
  collections = NULL,
  time_range = NULL,
  n_granules = 2L,
  verbose = "inform"
)

Arguments

Value

A data.frame with collection, collection_id, status, tested, succeeded, checked_at, and detail. Status is 'operational' when all sampled granules served one pixel, 'partial' when only some did, 'unavailable' when none did, and 'no_granules' when CMR found none for the chosen dates. 'unverified' means no suitable two-dimensional variable was found. These statuses apply only to the granules sampled at 'checked_at'.

Examples

## Not run: 
Sys.setenv(EARTHDATA_TOKEN = "your Earthdata bearer token")
status <- mf_list_collections_cloud("VJ121A2.002")
subset(status, status == "operational")
mf_list_collections_cloud("VJ121A2.002",
  time_range = as.Date(c("2026-01-01", "2026-01-30")))

## End(Not run)

Get information for the variables (bands) available for a given collection

Description

Get the variables available for a given collection, along with a set of related information for each.

Usage

mf_list_variables(
  collection,
  verbose = "inform",
  backend = c("auto", "cloud", "legacy"),
  time_range = NULL,
  collection_id = NULL,
  n_granules = 10L
)

Arguments

Value

A data.frame with the variables available for the collection, and a set of related information for each variable. The variables marked as "extractable" in the column "extractable_with_modisfast" can be provided as input parameter variables of mf_get_url. Cloud metadata comes from one accessible granule's DDS; it lists its two-dimensional fields but does not supply long names or units.

Examples

## Not run: 
# Configure an Earthdata bearer token for Cloud OPeNDAP.
Sys.setenv(EARTHDATA_TOKEN = "your Earthdata bearer token")

time_range <- as.Date(c("2026-01-01", "2026-01-30"))
bands <- mf_list_variables("VJ121A2.002", time_range = time_range)
subset(bands, name %in% c("LST_Day_1KM", "LST_Night_1KM"))

## End(Not run)

Download (and possibly import) MODIS, VIIRS and GPM Earth Observation data

Description

Download and possibly import MODIS, VIIRS and GPM Earth Observation data quickly and efficiently. This function is a wrapper for mf_get_url, mf_download_data and mf_import_data. Whenever possible, users should prefer executing the functions mf_get_url, mf_download_data and mf_import_data sequentially rather than using this high-level function

Usage

mf_modisfast(
  collection,
  variables,
  roi,
  time_range,
  path = tempfile("modisfast_"),
  parallel = FALSE,
  verbose = "inform",
  import = TRUE,
  ...
)

Arguments

Value

if the parameter import is set to TRUE, a terra::SpatRast object ; else a data.frame providing details of the data downloaded (see output of mf_download_data).

See Also

mf_get_url, mf_download_data, mf_import_data

Examples

## Not run: 

### Set-up parameters of interest
coll <- "VJ121A2.002"

bands <- c("LST_Day_1KM", "LST_Night_1KM")

time_range <- as.Date(c("2026-01-01", "2026-01-30"))

roi <- sf::st_as_sf(
  data.frame(
    id = "roi_test",
    geom = "POLYGON ((-5.82 9.54, -5.42 9.55, -5.41 8.84, -5.81 8.84, -5.82 9.54))"
  ),
  wkt = "geom", crs = 4326
)

### Download and import the data
Sys.setenv(EARTHDATA_TOKEN = "your Earthdata bearer token")
modis_ts <- mf_modisfast(
  collection = coll,
  variables = bands,
  roi = roi,
  time_range = time_range
 )

### Plot the data
terra::plot(modis_ts)

## End(Not run)