Calibrated Weak Supervision for Post-Harvest Burned-Cropland Mapping Under Label Scarcity

Raunak Bhagate; Maitri Polisetty; R I Minu

cs.LGOct 4, 2026

Calibrated Weak Supervision for Post-Harvest Burned-Cropland Mapping Under Label Scarcity

Authors: Raunak Bhagate, Maitri Polisetty, R I Minu

Organizations: SRM Institute of Science & Technology

Abstract

Mapping post-harvest burned cropland is difficult when fires are small and fragmented and reliable labels are scarce. We developed a calibrated weak-supervision framework for Punjab, India, using Sentinel-2 spectral change, VIIRS active-fire context, and MODIS MCD64A1 as a coarse external calibration and agreement reference. Three pseudo-label recipes, five feature representations, and linear, tree-based, boosted, and neural classifiers were evaluated using nested district-held-out cross-validation over three seeds and five folds. NBR and dNBR were excluded from classifier inputs. The best configuration used the very-strict recipe, a multilayer perceptron, and the full optical feature set (mean Cohen's kappa 0.395, AUROC 0.753, F1 0.747, balanced accuracy 0.703); Random Forest, XGBoost, and LightGBM were practically tied. Higher agreement with held-out pseudo-labels did not establish improved label correctness or independent burned-area accuracy. For deployment, a Random Forest with the very-strict recipe and full optical features was retained. An externally calibrated threshold of 0.60 yielded district-level MODIS agreement of R-squared 0.636, a mapped-to-MODIS burned-area ratio of 1.005, and Spearman correlation of 0.779 with district fire counts. Pixel-level MODIS agreement remained modest (F1 0.205, kappa 0.093). Zero-shot transfer to Haryana was promising (mean kappa 0.641), but Punjab cross-year stability was weak, and Sentinel-1/Sentinel-2 feature concatenation did not improve the optical baseline. Optical observations ended before the seasonal fire context, limiting coverage of late burns. The framework supports district-scale burden assessment and hotspot screening, with limited support for exact scar boundaries or temporally stable annual mapping.

Figures & tables

Panel of Figure 1 — Figure 1: Study area and active-fire context. (a) Punjab and Haryana in northwestern India. (b) Punjab districts and the ESA WorldCover cropland mask used to restrict analysis. (c) High-confidence VIIRS active-fire density over Punjab cropland from 1 October–29 November 2024.

Figure 2: End-to-end weak-supervision and calibration workflow. Sentinel-2 preprocessing, VIIRS-anchored pseudo-labels, matched district-held-out evaluation, statewide RF inference, external deployment-threshold calibration, and robustness testing are kept as distinct stages. The schematic’s pre/post labels denote the earlier/later optical composites; its October–November season denotes the broader reference period. NBR and dNBR are used for label construction but excluded from classifier inputs.

Panel of Figure 3 — Figure 3: Weak-label construction. Cropland and VIIRS fire context are intersected with burn-consistent spectral gates to produce weak positives, weak negatives, and unlabeled pixels. The displayed parameter block corresponds to the exact very-strict configuration.

Recipe	d NBR	Near km	$dB$ 12	HN d	HN b	Far km	NDVI
Moderate	0.18	3.0	$>200$	0.06	120	8	None
Strict	0.22	2.0	$>200$	0.05	100	8	None
Very-strict	0.28	1.5	$>250$	0.04	80	10	$>0.10$

Table 1: Weak-label recipe parameters. Near- and far-fire distances are in km; HN denotes hard-negative, with

d

and

b

denoting dNBR and

dB12

. The recipes share the same inputs and seasonal windows and differ only in spatial and spectral selection constraints.

Set	Size	Composition	Analytical purpose
A	39	Earlier, later, and delta values for 10 Sentinel-2 bands plus three indices	Full optical production representation
B	13	Delta-only optical variables	Tests whether change alone is sufficient
C	26	Earlier optical state plus delta variables	Retains baseline context with reduced redundancy
D	33	Full optical set without SWIR-related predictors	Primary circularity ablation
E	45	Set A plus Sentinel-1 VV/VH earlier, later, and delta	Simple optical–radar fusion test

Table 2: Feature-set definitions. NBR and dNBR are excluded from every classifier feature set because they are used in weak-label construction.

Rank	Recipe	Model	Set	Mean $\kappa$	SD $\kappa$	AUC	$F_{1}$	Bal. acc.
1	Very-strict	MLP	A	0.395	0.049	0.753	0.747	0.703
2	Very-strict	RF	B	0.395	0.053	0.747	0.744	0.703
3	Very-strict	XGBoost	A	0.394	0.053	0.740	0.747	0.703
4	Very-strict	RF	A	0.393	0.056	0.744	0.746	0.701
5	Very-strict	LightGBM	B	0.393	0.052	0.740	0.748	0.701

Table 3: Leading learned configurations. Values are means across 15 district-held-out evaluations (three seeds

\times

five folds).

Figure 4: Quantity–selectivity pattern across pseudo-label recipes. The very-strict recipe produced fewer positive weak labels and higher mean district-held-out agreement with its own pseudo-labels. Evaluation labels and sampled populations varied across recipes, so this pattern does not establish improved label correctness or independent burned-area accuracy.

Item	Value
Production classifier	Random Forest (300 trees)
Pseudo-label recipe	Very-strict
Feature set	A (39 optical variables)
Median internal CV threshold	0.30
Externally calibrated threshold	0.60
District $R^{2}$ vs. MODIS	0.636

Table 4: Production configuration and selected-threshold calibration/agreement statistics.

Figure 5: External deployment-threshold calibration for RF/very-strict/Set A. The internal threshold (0.30) and calibrated threshold (0.60) are compared across district MODIS fit, aggregate area ratio, fire-count rank correlation, and pixel MODIS $F_{1}$ . The selected cutoff balances the predefined criteria rather than maximizing a single metric.

Experiment	Configuration/protocol	Mean $\kappa$	Interpretation
Haryana zero-shot	Punjab-trained RF/very-strict/A; thresholds 0.30, 0.40, 0.30; no Haryana fitting or calibration	0.641	Promising neighboring-state transfer
Punjab 2023	Same seasonal framework	0.662	Stronger year-specific performance
Punjab 2024	Primary experiment	0.393	Weaker year-specific performance
S1 + S2 fusion	Set E minus optical Set A	$-0.015\,\Delta\kappa$	Simple concatenation did not improve performance

Table 5: Robustness and transfer summary. Cross-year reporting is limited to Punjab 2023 and 2024.

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