Survey of India states + LGD/Bharatmaps districts (DataMeet / ramSeraph mirror)fetched 9 Nov, 08:47 IST · fetch 44144
Why the rule calls it a burn
Rule s2burn-rules-2
Pixels in the scar
18
Near-infrared darkened (dB8A)
0.132
Short-wave infrared 1 darkened (dB11)
0.151
Short-wave infrared 2 darkened (dB12)
0.080
Burned-area index after (BAIS2)
0.87
Brightness after
0.27
Greenness before, after (NDVI)
0.26, 0.19
Burn ratio change (dNBR), NBR after
0.13, −0.08
The detection rule
Sentinel-2 L2A, cropland only: NIR and SWIR darkened since the previous clear pass (dB8A>0.07, dB11>0.07, dB12>0.03) and the field now looks charred (BAIS2>=0.78, brightness<=0.34); passes at most 6 days apart; clusters of at least 0.52 ha whose mean post-burn NBR is below 0.0 (drops flooded fields and cloud shadows). Candidates: tillage can look similar.
When a burn counts as unseen
unseen = no FIRMS detection within max(1 km, pixel half-diagonal + 200 m) of the scar between the clear pass before it and 3 h after the pass that shows it
Known look-alikes: cloud and smoke-plume shadows in partly cloudy passes and flooded fields with mean post NBR just below 0 still pass, often with high confidence; tillage of wet soil can mimic char; a hazy pre pass can exaggerate the darkening.
NOAA-21 VIIRS3.0 km
Sat 24 Oct, 01:38 IST · 2.0 MW · nominal confidence
Beyond the match radius
Suomi NPP VIIRS3.0 km
Sat 24 Oct, 02:16 IST · 1.2 MW · nominal confidence
Beyond the match radius
NOAA-21 VIIRS3.2 km
Sat 24 Oct, 01:38 IST · 2.0 MW · nominal confidence
Beyond the match radius
Suomi NPP VIIRS3.2 km
Sat 24 Oct, 02:16 IST · 1.2 MW · nominal confidence
Beyond the match radius
NOAA-20 VIIRS3.5 km
Sun 25 Oct, 13:31 IST · 3.8 MW · nominal confidence
Beyond the match radius
An alert explains a scar when it falls within 1.0 km of it, inside the burn window.
Weather between the passes
No weather for this window: no stored weather grid covers this burn window
Sentinel-2 scenesS2A_T43RGM_20261022T053942_L2A and S2B_T43RGM_20261025T053358_L2A
Candidate unseen burns as a whole: audit precision 87% (95% CI 68%–96%, n = 23), checked by eye.
How often this rule is right: when people checked a random sample of candidate unseen burns by eye, the audit precision was 87% (95% CI 68%–96%, n = 23).
