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
28
Near-infrared darkened (dB8A)
0.214
Short-wave infrared 1 darkened (dB11)
0.222
Short-wave infrared 2 darkened (dB12)
0.092
Burned-area index after (BAIS2)
0.98
Brightness after
0.23
Greenness before, after (NDVI)
0.24, 0.16
Burn ratio change (dNBR), NBR after
0.31, −0.22
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.
NOAA-20 VIIRS1.9 km
Sun 8 Nov, 14:13 IST · 8.6 MW · nominal confidence
Outside the burn window
Aqua MODIS1.9 km
Sun 8 Nov, 16:00 IST · 20.6 MW · high confidence
Outside the burn window
NOAA-20 VIIRS2.2 km
Sun 8 Nov, 14:13 IST · 9.2 MW · nominal confidence
Outside the burn window
Aqua MODIS2.3 km
Sat 31 Oct, 15:51 IST · 4.5 MW · nominal confidence
Outside the burn window
NOAA-21 VIIRS2.4 km
Sun 8 Nov, 13:13 IST · 3.2 MW · nominal confidence
Outside the burn window
NOAA-20 VIIRS2.5 km
Sat 7 Nov, 12:50 IST · 3.2 MW · nominal confidence
Sentinel-2 scenesS2C_T43REP_20261102T053957_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).
