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
427
Near-infrared darkened (dB8A)
0.254
Short-wave infrared 1 darkened (dB11)
0.263
Short-wave infrared 2 darkened (dB12)
0.134
Burned-area index after (BAIS2)
0.97
Brightness after
0.23
Greenness before, after (NDVI)
0.62, 0.16
Burn ratio change (dNBR), NBR after
0.30, −0.21
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.
The pass before the burn was hazy, which can exaggerate the darkening.
Aqua MODIS2.3 km
Sun 8 Nov, 16:00 IST · 21.1 MW · nominal confidence
Outside the burn window
Aqua MODIS2.5 km
Tue 3 Nov, 16:06 IST · 41.7 MW · high confidence
Outside the burn window
Aqua MODIS2.6 km
Sun 8 Nov, 16:00 IST · 79.5 MW · high confidence
Outside the burn window
NOAA-21 VIIRS2.7 km
Sun 8 Nov, 13:13 IST · 7.1 MW · nominal confidence
Outside the burn window
Aqua MODIS2.7 km
Sun 8 Nov, 16:00 IST · 8.5 MW · nominal confidence
Sentinel-2 scenesS2A_T43REP_20261104T055022_L2A and S2B_T43REP_20261107T054750_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).
