  Crop residue burning is a major source of air pollution in many parts of the
world, notably South Asia. Policymakers, practitioners and researchers have
invested in both measuring impacts and developing interventions to reduce
burning. However, measuring the impacts of burning or the effectiveness of
interventions to reduce burning requires data on where burning occurred. These
data are challenging to collect in the field, both in terms of cost and
feasibility. We take advantage of data from ground-based monitoring of crop
residue burning in Punjab, India to explore whether burning can be detected
more effectively using accessible satellite imagery. Specifically, we used 3m
PlanetScope data with high temporal resolution (up to daily) as well as
publicly-available Sentinel-2 data with weekly temporal resolution but greater
depth of spectral information. Following an analysis of the ability of
different spectral bands and burn indices to separate burned and unburned plots
individually, we built a Random Forest model with those determined to provide
the greatest separability and evaluated model performance with ground-verified
data. Our overall model accuracy of 82-percent is favorable given the
challenges presented by the measurement. Based on insights from this process,
we discuss technical challenges of detecting crop residue burning from
satellite imagery as well as challenges to measuring impacts, both of burning
and of policy interventions.
