India state-level NTL with Bhuvan

ISRO’s Bhuvan NTL portal provides pre-aggregated mean radiance for all Indian states from 2012 to 2024, and WMS raster tiles for any custom region, with no authentication required.

All states, all years

library(lightson)

df <- bhuvan_stats()
#> Warning: Your request spans a collection break in the Bhuvan NTL data:
#> 2012-2023 uses VNP46A4 Collection 1.0 and 2024 onwards uses Collection 2.0.
#> Radiance estimates are not directly comparable across this boundary. Treat any
#> 2023-to-2024 trend as an artefact of the collection change.
dim(df)
#> [1] 481   3
head(df)
#>               state year mean_radiance
#> 1 Andaman & Nicobar 2012          2.28
#> 2    Andhra Pradesh 2012         12.05
#> 3 Arunachal Pradesh 2012          0.78
#> 4             Assam 2012         10.81
#> 5             Bihar 2012          8.36
#> 6        Chandigarh 2012       1100.69

Single state time series

bihar <- bhuvan_stats(state = "BIHAR")
#> Warning: Your request spans a collection break in the Bhuvan NTL data:
#> 2012-2023 uses VNP46A4 Collection 1.0 and 2024 onwards uses Collection 2.0.
#> Radiance estimates are not directly comparable across this boundary. Treat any
#> 2023-to-2024 trend as an artefact of the collection change.
bihar
#>    state year mean_radiance
#> 1  Bihar 2012          8.36
#> 2  Bihar 2013          9.12
#> 3  Bihar 2014         12.18
#> 4  Bihar 2015         16.45
#> 5  Bihar 2016         22.05
#> 6  Bihar 2017         24.64
#> 7  Bihar 2018         35.56
#> 8  Bihar 2019         41.00
#> 9  Bihar 2020         41.43
#> 10 Bihar 2021         47.97
#> 11 Bihar 2022         55.75
#> 12 Bihar 2023         60.59
#> 13 Bihar 2024         79.68
library(ggplot2)
plot_ntl_trend(bihar,
  region = "Bihar",
  caption = "Source: ISRO Bhuvan NTL portal"
)

Radiance nearly 10x from 2012 to 2024.

Compare states

states <- c("Bihar", "Maharashtra", "Tamil Nadu", "Uttar Pradesh", "Kerala")
plot_ntl_trend(
  df[df$state %in% states, ],
  region   = states,
  subtitle = "Bhuvan state mean radiance (nW/cm^2/sr)",
  caption  = "Source: ISRO Bhuvan NTL portal"
)

Nighttime India from space, 2012 to 2024

states_sf <- get_india_admin("state")

years_sel <- c(2012, 2015, 2018, 2021, 2024)
rasters <- tryCatch(
  bhuvan_raster("IND", years = years_sel, bbox_pad = c(0, 0, 0, 3.5)),
  error = function(e) list()
)
#> Warning: Your request spans a collection break in the Bhuvan NTL data:
#> 2012-2023 uses VNP46A4 Collection 1.0 and 2024 onwards uses Collection 2.0.
#> Radiance estimates are not directly comparable across this boundary. Treat any
#> 2023-to-2024 trend as an artefact of the collection change.
#> Using cached: bhuvan_v2_5a2264df7a53_2012.tif
#> Using cached: bhuvan_v2_5a2264df7a53_2015.tif
#> Using cached: bhuvan_v2_5a2264df7a53_2018.tif
#> Using cached: bhuvan_v2_5a2264df7a53_2021.tif
#> Using cached: bhuvan_v2_5a2264df7a53_2024.tif
plot_ntl_panel(
  rasters,
  polygons = states_sf,
  title    = "India at night, 2012 to 2024",
  subtitle = "Bhuvan NTL radiance classes. Boundaries: LGD/SoI.",
  caption  = "Source: ISRO Bhuvan NTL portal",
  ncol     = 3L
)

All panels are drawn at the same radiance scale (reference zero). The sharpest gains are in Bihar, UP, and Odisha after 2015, broadly tracking the Saubhagya electrification scheme (2017-2019).

Filter by year range

post_2015 <- bhuvan_stats(years = 2015:2024)
#> Warning: Your request spans a collection break in the Bhuvan NTL data:
#> 2012-2023 uses VNP46A4 Collection 1.0 and 2024 onwards uses Collection 2.0.
#> Radiance estimates are not directly comparable across this boundary. Treat any
#> 2023-to-2024 trend as an artefact of the collection change.
nrow(post_2015)
#> [1] 370