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lightson

Download and analyse nighttime lights satellite data from R.

lightson supports two data sources:

  • ISRO Bhuvan NTL: pre-aggregated state radiance for India (2012-2024); WMS raster tiles for any region.
  • NASA VIIRS Black Marble (VNP46A4): annual composites from 2012, global coverage.

The ISRO Bhuvan NTL source does not require any authentication. VIIRS requires a free NASA Earthdata account.

India at night, 2012-2024
India at night, 2012-2024

Installation 

pak::pak("saketlab/lightson")

Usage 

library(lightson)

df <- bhuvan_stats()          # all states, 2012-2024
head(df)
#>        state year mean_radiance
#> 1 Andhra Pradesh 2012      22.4

bihar <- bhuvan_stats(state = "BIHAR")
plot_ntl_trend(bihar, region = "Bihar", caption = "Source: ISRO Bhuvan")

Compare states 

states <- c("Bihar", "Maharashtra", "Tamil Nadu", "Uttar Pradesh", "Kerala")
plot_ntl_trend(
  df[df$state %in% states, ],
  region   = states,
  subtitle = "Bhuvan NTL luminance (ITU-R BT.709)",
  caption  = "Source: ISRO Bhuvan NTL portal"
)

Raster maps 

library(sf)
library(terra)

states_sf <- get_india_admin("state")   # bundled LGD/SoI boundaries, full J&K

rasters <- bhuvan_raster("IND", years = 2022, bbox_pad = c(0, 0, 0, 3.5))
plot_ntl_map(rasters[["2022"]], polygons = states_sf, dark = TRUE)
plot_ntl_map(rasters[["2022"]], polygons = states_sf, palette = "bhuvan")

District-level panel 

districts <- sf::read_sf("https://bharatviz.org/India-bhuvan-districts.geojson")

rasters <- bhuvan_raster(districts, years = 2018:2023)
panel   <- extract_panel(rasters, districts, id_col = "district_name")

head(panel)
#>   region_id year mean_radiance n_pixels
#> 1  Adilabad 2018          14.2     3821

plot_ntl_trend(panel, region = c("Mumbai", "Delhi", "Chennai", "Bengaluru"))

bhuvan_raster() accepts any sf object (including your own shapefile/geojson).

VIIRS

VIIRS gives physical radiance in nW/cm^2/sr, which is useful when you need calibrated values or coverage outside India.

Getting a token:

  1. Create a free account at https://urs.earthdata.nasa.gov
  2. Accept the MERIS and Sentinel EULAs under Profile > EULAs
  3. Go to Profile > Generate Token and copy the EDL-... string
  4. Add it to ~/.Renviron: EARTHDATA_TOKEN=EDL-xxxxxxxxxxxxxxxxxxxx 
# read EARTHDATA_TOKEN from environment
token <- earthdata_token()   
rasters <- ntl_download("viirs", region = "IND", years = 2020:2023, token = token)
panel <- extract_panel(rasters, get_india_admin("state"), id_col = "state_name")
plot_ntl_trend(panel, region = c("Bihar", "Maharashtra"))

You can also set EARTHDATA_USERNAME and EARTHDATA_PASSWORD in ~/.Renviron instead and earthdata_token() will fetch the token for you.

Units and comparability

Bhuvan WMS rasters are RGB visualisation tiles. bhuvan_raster() converts them to ITU-R BT.709 luminance (0.2126R + 0.7152G + 0.0722B). The values are dimensionless and can be used for trend analysis, but not in physical radiance units. Don’t mix them with VIIRS radiance in a regression without normalising first.

Bhuvan NTL data from 2024 onwards uses VNP46A4 Collection 2.0; 2012-2023 uses Collection 1.0. The two collections use different calibration algorithms, so estimates aren’t directly comparable across that boundary. lightson warns automatically when a request spans both.