Survey of India states + LGD/Bharatmaps districts (DataMeet / ramSeraph mirror)fetched 9 Nov, 08:47 IST · fetch 44144
0.161
Short-wave infrared 1 darkened (dB11)
0.167
Short-wave infrared 2 darkened (dB12)
0.100
Burned-area index after (BAIS2)
0.85
Brightness after
0.29
Greenness before, after (NDVI)
0.22, 0.13
Burn ratio change (dNBR), NBR after
0.13, −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.
Weather between the passes
Open-Meteo best-match model, 120 of 120 hours
Afternoon wind, mean6 km/h
Afternoon mixing height, mean562 m
Rain in the window0.0 mm
Wind at 10 m, km/hMixing height, m
Arrows point where the wind blew. A low mixing height keeps smoke near the ground.
Sentinel-2 scenesS2B_T43SDR_20261107T054750_L2A and S2C_T43SDR_20261112T054715_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).
