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
276
Near-infrared darkened (dB8A)
0.162
Short-wave infrared 1 darkened (dB11)
0.215
Short-wave infrared 2 darkened (dB12)
0.095
Burned-area index after (BAIS2)
0.96
Brightness after
0.25
Greenness before, after (NDVI)
0.22, 0.10
Burn ratio change (dNBR), NBR after
0.20, −0.18
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 MODIS3.2 km
Tue 3 Nov, 16:06 IST · 72.3 MW · high confidence
Beyond the match radius
Suomi NPP VIIRS3.4 km
Sat 31 Oct, 13:01 IST · 4.5 MW · nominal confidence
Sentinel-2 scenesS2C_T43RFP_20261102T053957_L2A and S2B_T43RFP_20261104T053705_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).
