[
• Overview
•
Installation
•
Get started
•
Data collections
available
• Foundational
framework
•
Comparison with similar R
packages
• Citation
•
Future developments
•
Contributing
•
Acknowledgments
modisfast is an R package designed for easy and fast
downloads of
MODIS
Land products,
VIIRS
Land products, and GPM (Global
Precipitation Measurement Mission) Earth Observation data.
modisfast uses the abilities offered by the
OPeNDAP framework (Open-source
Project for a Network Data Access Protocol) to download a subset of
Earth Observation data cube, along spatial, temporal or any other data
dimension (depth, …). This way, it reduces downloading time and disk
usage to their minimum : no more 1° x 1° MODIS tiles with 10 bands when
your region of interest is only 30 km x 30 km wide and you need 2 bands
! Moreover, modisfast enables parallel downloads of data.
This package is hence particularly suited for retrieving MODIS or VIIRS data over long time series and over areas, rather than short time series and points.
Importantly, the robust, sustainable, and cost-free foundational
framework of modisfast, both for the data
provider (NASA) and the software (R, OPeNDAP, the tidyverse and GDAL
suite of packages and software), guarantees the long-term reliability
and open-source nature of the package.
By enabling to download subsets of data cubes, modisfast facilites the
access to Earth science data for R users in places where internet
connection is slow or expensive and promotes digital sobriety for our
research work.
You can install the released version of modisfast from
CRAN with :
install.packages("modisfast")or the development version (to get a bug fix or to use a feature from the development version) with :
if(!require(devtools)){install.packages("devtools")}
devtools::install_github("ptaconet/modisfast")Accessing and opening MODIS data with modisfast is a simple 3-steps
workflow. This example shows how to download and import a one-year-long
monthly time series of MODIS Normalized Difference Vegetation Index
(NDVI) at 1 km spatial resolution over the whole country of Madagascar.
1/ First, define the variables of interest (ROI, time frame, collection, and bands) :
# Load the packages
library(modisfast)
library(sf)
library(terra)
# ROI and time range of interest
roi <- st_as_sf(data.frame(id = "madagascar", geom = "POLYGON((41.95 -11.37,51.26 -11.37,51.26 -26.17,41.95 -26.17,41.95 -11.37))"), wkt = "geom", crs = 4326) # a ROI of interest, format sf polygon
time_range <- as.Date(c("2023-01-01", "2023-12-31")) # a time range of interest
# MODIS collections and variables (bands) of interest
collection <- "MOD13A3.061" # run mf_list_collections() for an exhaustive list of collections available
variables <- c("_1_km_monthly_NDVI") # run mf_list_variables("MOD13A3.061") for an exhaustive list of variables available for the collection "MOD13A3.061"2/ Then, get the URL of the data and download them :
## Login to Earthdata servers with your EOSDIS credentials.
# To create an account (free) go to : https://urs.earthdata.nasa.gov/.
log <- mf_login(credentials = c("username", "password")) # set your own EOSDIS username and password
## Get the URLs of the data
urls <- mf_get_url(
collection = collection,
variables = variables,
roi = roi,
time_range = time_range
)
## Download the data. By default the data is downloaded in a temporary directory, but you can specify a folder
res_dl <- mf_download_data(urls, parallel = TRUE)3/ And finally, import the data in R as a terra::SpatRaster object
using the function mf_import_data() ( terra::rast(), in the context of modisfast) :
r <- mf_import_data(
path = dirname(res_dl$destfile[1]),
collection = collection,
proj_epsg = 4326
)
terra::plot(r, col = rev(terrain.colors(20)))
modisfast
et voilà !
Want more examples ? modisfast provides three long-form documentations
and examples to learn more about the package :
- a “Get started” article describing the core features of the package;
- a “Get data on several regions or periods of interest simultaneously”
article
detailing advanced functionalities of
modisfast(for multi-time frame or multi-regions data access); - a “Full use case” article showcasing an example of use of the package in a scientific context (here: landscape epidemiology).
Currently modisfast supports download of 77 data collections,
extracted from the following meta-collections :
- MODIS land products made available by the NASA / USGS LP DAAC ( ➡️ source OPeNDAP server) ;
- VIIRS land products made available by the NASA / USGS LP DAAC ( ➡️ source OPeNDAP server)
- Global Precipitation Measurement (GPM) made available by the NASA / JAXA GES DISC ( ➡️ source OPeNDAP server).
Details of each product available for download are provided in the
tables below or through the function mf_list_collections().
MODIS and VIIRS data collections accessible with modisfast (click to expand)
| Collection | Source | Name | Type |
|---|---|---|---|
| MCD12Q1.061 | MODIS | MODIS/Terra+Aqua Land Cover Type Yearly L3 Global 500 m SIN Grid | Land cover |
| MCD15A2H.061 | MODIS | MODIS/Terra+Aqua Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid | Leaf area index |
| MCD15A3H.061 | MODIS | MODIS/Terra+Aqua Leaf Area Index/FPAR 4-Day L4 Global 500 m SIN Grid | Leaf area index |
| MCD43A1.061 | MODIS | MODIS/Terra and Aqua BRDF/Albedo Model Parameters Daily L3 Global 500 m SIN Grid | Albedo |
| MCD43A2.061 | MODIS | MODIS/Terra and Aqua BRDF/Albedo Quality Daily L3 Global 500 m SIN Grid | Albedo |
| MCD43A3.061 | MODIS | MODIS/Terra and Aqua Albedo Daily L3 Global 500 m SIN Grid | Albedo |
| MCD43A4.061 | MODIS | MODIS/Terra and Aqua Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid | Surface reflectance |
| MCD64A1.061 | MODIS | MODIS/Terra+Aqua Burned Area Monthly L3 Global 500 m SIN Grid | Burned areas |
| MOD09A1.061 | MODIS | MODIS/Terra Surface Reflectance 8-Day L3 Global 500 m SIN Grid | Surface reflectance |
| MOD09GA.061 | MODIS | MODIS/Terra Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid | Surface reflectance |
| MOD09GQ.061 | MODIS | MODIS/Terra Surface Reflectance Daily L2G Global 250 m SIN Grid | Surface reflectance |
| MOD09Q1.061 | MODIS | MODIS/Terra Surface Reflectance 8-Day L3 Global 250 m SIN Grid | Surface reflectance |
| MOD11A1.061 | MODIS | MODIS/Terra Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid v061 | Land surface temperature |
| MOD11A2.061 | MODIS | MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid v061 | Land surface temperature |
| MOD11B2.061 | MODIS | MODIS/Terra Land Surface Temperature/Emissivity 8-Day L3 Global 6 km SIN Grid | Land surface temperature |
| MOD11B3.061 | MODIS | MODIS/Terra Land Surface Temperature/Emissivity Monthly L3 Global 6 km SIN Grid | Land surface temperature |
| MOD13A1.061 | MODIS | MODIS/Terra Vegetation Indices 16-Day L3 Global 500 m SIN Grid | Vegetation indices |
| MOD13A2.061 | MODIS | MODIS/Terra Vegetation Indices 16-Day L3 Global 1 km SIN Grid | Vegetation indices |
| MOD13A3.061 | MODIS | MODIS/Terra Vegetation Indices Monthly L3 Global 1 km SIN Grid | Vegetation indices |
| MOD13Q1.061 | MODIS | MODIS/Terra Vegetation Indices 16-Day L3 Global 250m SIN Grid v061 | Vegetation indices |
| MOD15A2H.061 | MODIS | MODIS/Terra Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid | Leaf area index |
| MOD16A2.061 | MODIS | MODIS/Terra Net Evapotranspiration 8-Day L4 Global 500m SIN Grid v061 | Evapotranspiration |
| MOD16A2GF.061 | MODIS | MODIS/Terra Net Evapotranspiration Gap-Filled 8-Day L4 Global 500 m SIN Grid | Evapotranspiration |
| MOD16A3GF.061 | MODIS | MODIS/Terra Net Evapotranspiration Gap-Filled Yearly L4 Global 500 m SIN Grid | Evapotranspiration |
| MOD17A2H.061 | MODIS | MODIS/Aqua Gross Primary Productivity 8-Day L4 Global 500 m SIN Grid | Primary Productivity |
| MOD17A2HGF.061 | MODIS | MODIS/Terra Gross Primary Productivity Gap-Filled 8-Day L4 Global 500 m SIN Grid | Primary Productivity |
| MOD17A3HGF.061 | MODIS | MODIS/Terra Net Primary Production Gap-Filled Yearly L4 Global 500 m SIN Grid | Primary Productivity |
| MODOCGA.061 | MODIS | MODIS/Terra Ocean Reflectance Daily L2G-Lite Global 1 km SIN Grid | Ocean Reflectance |
| MODTBGA.061 | MODIS | MODIS/Terra Thermal Bands Daily L2G-Lite Global 1 km SIN Grid | Thermal Bands |
| MYD09A1.061 | MODIS | MODIS/Aqua Surface Reflectance 8-Day L3 Global 500 m SIN Grid | Surface reflectance |
| MYD09GA.061 | MODIS | MODIS/Aqua Surface Reflectance Daily L2G Global 1 km and 500 m SIN Grid | Surface reflectance |
| MYD09GQ.061 | MODIS | MODIS/Aqua Surface Reflectance Daily L2G Global 250 m SIN Grid | Surface reflectance |
| MYD09Q1.061 | MODIS | MODIS/Aqua Surface Reflectance 8-Day L3 Global 250 m SIN Grid | Surface reflectance |
| MYD11A1.061 | MODIS | MODIS/Aqua Land Surface Temperature/Emissivity Daily L3 Global 1km SIN Grid v061 | Land surface temperature |
| MYD11A2.061 | MODIS | MODIS/Aqua Land Surface Temperature/Emissivity 8-Day L3 Global 1 km SIN Grid v061 | Land surface temperature |
| MYD11B2.061 | MODIS | MODIS/Aqua Land Surface Temperature/Emissivity 8-Day L3 Global 6 km SIN Grid | Land surface temperature |
| MYD11B3.061 | MODIS | MODIS/Aqua Land Surface Temperature/Emissivity Monthly L3 Global 6 km SIN Grid | Land surface temperature |
| MYD13A1.061 | MODIS | MODIS/Aqua Vegetation Indices 16-Day L3 Global 500 m SIN Grid | Vegetation indices |
| MYD13A2.061 | MODIS | MODIS/Aqua Vegetation Indices 16-Day L3 Global 1 km SIN Grid | Vegetation indices |
| MYD13A3.061 | MODIS | MODIS/Aqua Vegetation Indices Monthly L3 Global 1 km SIN Grid | Vegetation indices |
| MYD13Q1.061 | MODIS | MODIS/Aqua Vegetation Indices 16-Day L3 Global 250m SIN Grid v061 | Vegetation indices |
| MYD15A2H.061 | MODIS | MODIS/Aqua Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid | Leaf area index |
| MYD16A2.061 | MODIS | MODIS/Aqua Net Evapotranspiration 8-Day L4 Global 500m SIN Grid v061 | Evapotranspiration |
| MYD16A2GF.061 | MODIS | MODIS/Aqua Net Evapotranspiration Gap-Filled 8-Day L4 Global 500 m SIN Grid | Evapotranspiration |
| MYD16A3GF.061 | MODIS | MODIS/Aqua Net Evapotranspiration Gap-Filled Yearly L4 Global 500 m SIN Grid | Evapotranspiration |
| MYD17A2H.061 | MODIS | MODIS/Terra Gross Primary Productivity 8-Day L4 Global 500 m SIN Grid | Primary Productivity |
| MYD17A2HGF.061 | MODIS | MODIS/Aqua Gross Primary Productivity Gap-Filled 8-Day L4 Global 500 m SIN Grid | Primary Productivity |
| MYD17A3HGF.061 | MODIS | MODIS/Aqua Net Primary Production Gap-Filled Yearly L4 Global 500 m SIN Grid | Primary Productivity |
| MYDOCGA.061 | MODIS | MODIS/Aqua Ocean Reflectance Daily L2G-Lite Global 1 km SIN Grid | Ocean Reflectance |
| MYDTBGA.061 | MODIS | MODIS/Aqua Thermal Bands Daily L2G-Lite Global 1 km SIN Grid | Thermal Bands |
| VNP09A1.001 | VIIRS | VIIRS/NPP Surface Reflectance 8-Day L3 Global 1 km SIN Grid | Surface reflectance |
| VNP09H1.001 | VIIRS | VIIRS/NPP Surface Reflectance 8-Day L3 Global 500 m SIN Grid | Surface reflectance |
| VNP13A1.001 | VIIRS | VIIRS/NPP Vegetation Indices 16-Day L3 Global 500 m SIN Grid | Vegetation indices |
| VNP13A2.001 | VIIRS | VIIRS/NPP Vegetation Indices 16-Day L3 Global 1 km SIN Grid | Vegetation indices |
| VNP13A3.001 | VIIRS | VIIRS/NPP Vegetation Indices Monthly L3 Global 1 km SIN Grid | Vegetation indices |
| VNP14A1.001 | VIIRS | VIIRS/NPP Thermal Anomalies/Fire Daily L3 Global 1 km SIN Grid | Thermal Anomalies/Fire |
| VNP15A2H.001 | VIIRS | VIIRS/NPP Leaf Area Index/FPAR 8-Day L4 Global 500 m SIN Grid | Leaf area index |
| VNP21A1D.001 | VIIRS | VIIRS/NPP Land Surface Temperature and Emissivity Daily L3 Global 1 km SIN Grid Day | Land surface temperature |
| VNP21A1N.001 | VIIRS | VIIRS/NPP Land Surface Temperature and Emissivity Daily L3 Global 1 km SIN Grid Night | Land surface temperature |
| VNP21A2.001 | VIIRS | VIIRS/NPP Land Surface Temperature and Emissivity 8-Day L3 Global 1 km SIN Grid | Land surface temperature |
| VNP43IA2.001 | VIIRS | VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 500 m SIN Grid | Albedo |
| VNP43IA3.001 | VIIRS | VIIRS/NPP Albedo Daily L3 Global 500 m SIN Grid | Albedo |
| VNP43IA4.001 | VIIRS | VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 500 m SIN Grid | Surface reflectance |
| VNP43MA1.001 | VIIRS | VIIRS/NPP BRDF/Albedo Model Parameters Daily L3 Global 1 km SIN | Albedo |
| VNP43MA2.001 | VIIRS | VIIRS/NPP BRDF/Albedo Quality Daily L3 Global 1 km SIN Grid | Albedo |
| VNP43MA3.001 | VIIRS | VIIRS/NPP Albedo Daily L3 Global 1 km SIN Grid | Albedo |
| VNP43MA4.001 | VIIRS | VIIRS/NPP Nadir BRDF-Adjusted Reflectance Daily L3 Global 1 km SIN | Surface reflectance |
Other (non-MODIS or VIIRS) data collections accessible with modisfast (click to expand)
| Collection | Source | Name | Type |
|---|---|---|---|
| GPM_3IMERGDE.06 | GPM | GPM IMERG Early Precipitation L3 1 day 0.1 degree x 0.1 degree V06 | Rainfall |
| GPM_3IMERGDF.06 | GPM | GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V06 | Rainfall |
| GPM_3IMERGDF.07 | GPM | GPM IMERG Final Precipitation L3 1 day 0.1 degree x 0.1 degree V07 | Rainfall |
| GPM_3IMERGDL.06 | GPM | GPM IMERG Late Precipitation L3 1 day 0.1 degree x 0.1 degree V06 | Rainfall |
| GPM_3IMERGHH.06 | GPM | GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06 | Rainfall |
| GPM_3IMERGHH.07 | GPM | GPM IMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V07 | Rainfall |
| GPM_3IMERGHHE.06 | GPM | GPM IMERG Early Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06 | Rainfall |
| GPM_3IMERGHHL.06 | GPM | GPM IMERG Late Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06 | Rainfall |
| GPM_3IMERGM.06 | GPM | GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V06 | Rainfall |
| GPM_3IMERGM.07 | GPM | GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V07 | Rainfall |
Technically, modisfast is a programmatic interface (R wrapper) to
several NASA OPeNDAP servers. OPeNDAP is the
acronym for Open-source Project for a Network Data Access Protocol and
designates both the software, the access protocol, and the corporation
that develops them. The OPeNDAP is designed to simplify access to
structured and high-volume data, such as satellite products, over the
Web. It is a collaborative effort involving multiple institutions and
companies, with open-source code, free software, and adherence to the
Open Geospatial Consortium (OGC) standards. It
is widely used by NASA, which partly finances it.
A key feature of OPeNDAP is its capability to apply filters at the data
download process, ensuring that only the necessary data is retrieved.
These filters, specified within a URL, can be spatial, temporal, or
dimensional. Although powerful, OPeNDAP URLs are not trivial to build.
modisfast facilitates this process by constructing the URL based on
the spatial, temporal, and dimensional filters provided by the user in
the function mf_get_url().
Let’s take an example to understand.
The following URL ⬇️
https://opendap.cr.usgs.gov/opendap/hyrax/MOD11A2.061/h17v08.ncml.nc4?MODIS_Grid_8Day_1km_LST_eos_cf_projection,LST_Day_1km[775:793][55:140][512:560],LST_Night_1km[775:793][55:140][512:560],QC_Day[775:793][55:140][512:560],QC_Night[775:793][55:140][512:560],time[775:793],YDim[55:140],XDim[512:560]
is a link to download the following subset of MOD11A2.061 data in netCDF :
- bands LST_Day_1km, LST_Night_1km, QC_Day, QC_Night ;
- each available date between the 2017-01-01 and the 2017-06-01 ;
- within the following bounding box (lon/lat): -5.41 8.84, -5.82 9.54.
The indices within the [] refer to values encoding for the spatial and
temporal filters.
These OPeNDAP URLs are not trivial to build. modisfast converts the
spatial, temporal and dimensional filters (R objects) provided by the
user through the function mf_get_url() into the appropriate OPeNDAP
URL(s). Subsequently, the function mf_download_data() allows for
downloading the data using the
httr and parallel
packages.
There are other R packages available for accessing MODIS data. Below is a comparison of modisfast with other packages available for downloading chunks of MODIS or VIIRS data :
| Package | Data | Available on CRAN | Utilizes open standards for data access protocols | Spatial subsetting* | Dimensional subsetting* | Maximum area size allowed for download | Speed** |
|---|---|---|---|---|---|---|---|
modisfast |
MODIS, VIIRS, GPM | ✅ | ✅ | ✅ | ✅ | unlimited | ✅ |
appeears |
MODIS, VIIRS, and many others | ✅ | ✅ | ✅ | ✅ | unlimited | variable |
MODISTools |
MODIS, VIIRS | ✅ | ❌ | ✅ | ✅ | 200 km x 200 km | ✅ |
rgee |
MODIS, VIIRS, GPM, and many others | ✅ | ❌ | ✅ | ✅ | unlimited | not tested |
MODIStsp |
MODIS | ❌ | ❌ | ✅ | unlimited | NA | |
MODIS |
MODIS | ❌ | ❌ | ❌ | ❌ | NA | NA |
* at the downloading phase
** Take a look at the article “Comparison of performance with other
similar R
packages”
to get an overview of how modisfast compares to these packages in
terms of data access time.
This package is licensed under a GNU General Public License v3.0 or later license.
We thank in advance people that use modisfast for citing it in their
work / publication(s). For this, please use the following citation :
Taconet, P. & Moiroux N.(2024). modisfast: Fast and Efficient Access to MODIS Earth Observation Data. In CRAN: Contributed Packages. The R Foundation. https://doi.org/10.32614/cran.package.modisfast
Future developments of the package may include access to additional data collections from other OPeNDAP servers, and support for a variety of data formats as they become available from data providers through their OPeNDAP servers. Furthermore, the creation of an RShiny application on top of the package is being considered, as a means of further simplifying data access for users with limited coding skills.
All types of contributions are encouraged and valued. For more information, check out our Contributor Guidelines.
Please note that the modisfast project is released with a Contributor
Code of
Conduct.
By contributing to this project, you agree to abide by its terms.
We thank NASA and its partners for making all their Earth science data
freely available, and implementing open data access protocols such as
OPeNDAP. modisfast heavily builds on top of the OPeNDAP, so we thank
the non-profit OPeNDAP, Inc. for
developing the eponym tool in an open and collaborative way.
We also thank the contributors that have tested the package, reviewed the documentation and brought valuable feedbacks to improve the package : Florian de Boissieu, Julien Taconet.
This work has been developed over the course of several research projects (REACT 1, REACT 2, ANORHYTHM and DIV-YOO) funded by Expertise France and the French National Research Agency (ANR).