Downloading MODIS data

MODIS data can be acquired through multiple services, and it can be confusing to find out what to get where. This website has a lot of information on MODIS resources.

Websites are great for preliminary data exploration, they are not ideal for accessing large number of files or when automatic updates are required. Manually downloading also makes workflows less reproducible. So once you know what data you need, it is better to use automatic downloading methods. Here we show how to use R package luna to download MODIS data for a geographic area and time period.

A large number of MODIS products is available. We first need to find data for the product that we deem best suited for our study.

library(terra)
library(luna)
# lists all products that are currently searchable
prod <- getProducts()
head(prod)
##      provider             concept_id                          short_name
## 1        GHRC             C1000-GHRC                            dc8capac
## 5       CDDIS      C1000000000-CDDIS              CDDIS_DORIS_data_cycle
## 18 LANCEAMSR2 C1000000000-LANCEAMSR2                      A2_RainOcn_NRT
## 19  NSIDC_ECS  C1000000000-NSIDC_ECS                            NmHRIR3H
## 23  ORNL_DAAC  C1000000000-ORNL_DAAC GLOBAL_MICROBIAL_BIOMASS_C_N_P_1264
## 25      SEDAC      C1000000000-SEDAC               CIESIN_SEDAC_EPI_2012
##    version
## 1        1
## 5        1
## 18       0
## 19       1
## 23       1
## 25 2012.00
# to find the MODIS products
modis <- getProducts("^MOD|^MYD|^MCD")
head(modis)
##        provider             concept_id short_name version
## 806  LPDAAC_ECS C1000000120-LPDAAC_ECS     MOD44B     051
## 1433 LPDAAC_ECS C1000000400-LPDAAC_ECS   MCD43D10     006
## 1447 LPDAAC_ECS C1000000401-LPDAAC_ECS   MCD43D33     006
## 1452 LPDAAC_ECS C1000000402-LPDAAC_ECS   MCD43D45     006
## 1454 LPDAAC_ECS C1000000403-LPDAAC_ECS   MCD43D26     006
## 1456 LPDAAC_ECS C1000000404-LPDAAC_ECS   MCD43D49     006

We will use “MOD09A1” for this turorial.

product <- "MOD09A1"

To learn more about a specific product you can launch a webpage

productInfo(product)

Note that the entire MODIS archive is regularly re-processed for overall imporvement and revisions. We use version 6 or later for our analysis.

Once we finalize the product we want to use, we define some parameters for the data we want: product name, start and end date, and area of interest.

start <- "2010-01-01"
end <- "2010-01-07"

We will download an example MODIS 8-day composite tile. Our area of interest is Marsabit county, Kenya. To define the area of interest, we can define a spatial extent, or use an object that has an extent. Here we use a polygon for Marsabit.

ken <- geodata::gadm("Kenya", level=1, path=".")
ken
##  class       : SpatVector
##  geometry    : polygons
##  dimensions  : 47, 11  (geometries, attributes)
##  extent      : 33.90959, 41.92622, -4.720417, 5.061166  (xmin, xmax, ymin, ymax)
##  coord. ref. : lon/lat WGS 84 (EPSG:4326)
##  names       :   GID_1 GID_0 COUNTRY  NAME_1 VARNAME_1 NL_NAME_1 TYPE_1
##  type        :   <chr> <chr>   <chr>   <chr>     <chr>     <chr>  <chr>
##  values      : KEN.1_1   KEN   Kenya Baringo        NA        NA County
##                KEN.2_1   KEN   Kenya   Bomet        NA        NA County
##                KEN.3_1   KEN   Kenya Bungoma        NA        NA County
##  ENGTYPE_1  CC_1 HASC_1 ISO_1
##      <chr> <chr>  <chr> <chr>
##     County    30  KE.BA KE-01
##     County    36  KE.BO KE-02
##     County    39  KE.BN KE-03

ken is a SpatVector of polygons. We can subset it get the polygon for Marsabit:

i <- ken$NAME_1 == "Marsabit"
aoi <- ken[i,]

And the check our results we make a map

plot(ken, col="light gray")
lines(aoi, col="red", lwd=2)

image0

Let’s now find out what MODIS data is available for this area. We can search the data available from a NASA server

mf <- luna::getModis(product, start, end, aoi=aoi, download = FALSE)
mf
## [1] "MOD09A1.A2009361.h21v08.061.2021149144347.hdf"
## [2] "MOD09A1.A2010001.h21v08.061.2021150093948.hdf"
## [3] "MOD09A1.A2009361.h21v08.006.2015198070255.hdf"
## [4] "MOD09A1.A2010001.h21v08.006.2015198080805.hdf"

To download the tiles, usually you would download them to a folder where you save the data for your project. Here we use the temporary directory. You should use a specific direcory of your choice instead.

datadir <- file.path(dirname(tempdir()), "_modis")
dir.create(datadir, showWarnings=FALSE)

You also need to provide the username and password for your (free) EOSDIS account. If you do not have an account, you can sign up here. My passwords are stored in a file that I read below (sorry, I cannot show you the values).

up <- readRDS("../../../../pwds.rds")
up <- up[up$service == "EOSDIS", ]

Now we are ready to download the data

mf <- luna::getModis(product, start, end, aoi=aoi, download=TRUE,
                     path=datadir, username=up$user, password=up$pwd)
mf
## [1] "c:/temp/_modis/MOD09A1.A2009361.h21v08.061.2021149144347.hdf"
## [2] "c:/temp/_modis/MOD09A1.A2010001.h21v08.061.2021150093948.hdf"
## [3] "c:/temp/_modis/MOD09A1.A2009361.h21v08.006.2015198070255.hdf"
## [4] "c:/temp/_modis/MOD09A1.A2010001.h21v08.006.2015198080805.hdf"

In the next chapters we will use the downloaded files.