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
43
Near-infrared darkened (dB8A)
0.139
Short-wave infrared 1 darkened (dB11)
0.145
Short-wave infrared 2 darkened (dB12)
0.068
Burned-area index after (BAIS2)
0.88
Brightness after
0.27
Greenness before, after (NDVI)
0.21, 0.14
Burn ratio change (dNBR), NBR after
0.17, −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.
Suomi NPP VIIRS2.4 km
Sat 31 Oct, 14:41 IST · 3.6 MW · nominal confidence
Outside the burn window
Suomi NPP VIIRS2.5 km
Sat 31 Oct, 14:41 IST · 3.6 MW · nominal confidence
Outside the burn window
NOAA-21 VIIRS2.8 km
Sat 31 Oct, 14:05 IST · 6.2 MW · nominal confidence
Outside the burn window
NOAA-21 VIIRS4.6 km
Sun 1 Nov, 13:45 IST · 4.7 MW · low confidence
Outside the burn window
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 scenesS2C_T43SDR_20261102T053957_L2A and S2A_T43SDR_20261104T054237_L2A
State boundaries: Survey of India.
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).
