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
133
Near-infrared darkened (dB8A)
0.169
Short-wave infrared 1 darkened (dB11)
0.183
Short-wave infrared 2 darkened (dB12)
0.098
Burned-area index after (BAIS2)
0.89
Brightness after
0.32
Greenness before, after (NDVI)
0.22, 0.09
Burn ratio change (dNBR), NBR after
0.16, −0.12
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 VIIRS4.9 km
Sun 1 Nov, 13:45 IST · 2.0 MW · nominal confidence
Outside the burn window
NOAA-20 VIIRS5.0 km
Sun 1 Nov, 13:03 IST · 4.5 MW · nominal confidence
Outside the burn window
NOAA-20 VIIRS5.0 km
Sun 1 Nov, 13:03 IST · 7.3 MW · high 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_T43RDQ_20261102T053957_L2A and S2A_T43RDQ_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).
