Detecting and quantifying residue burning in smallholder systems: An integrated approach using Sentinel-2 data

Abstract

• Smallholder agricultural emissions in Central India are largely unaccounted. • A method is devised to detect smallholder’s burned area using Sentinel-2 data. • Agricultural emissions are estimated using burned area derived at 10 m resolution. • A novel visual image interpretation is applied to collect ground truth data. • Our approach showed significant smallholder (<10 ha) emissions than global products. Accurate and rapid mapping of agriculture burned areas (ABA) is essential to track agricultural emissions for sustainable agronomic development, better air quality management, and mitigation of climate change. Despite its climate and environmental impacts, India is largely unexplored considering the magnitude and frequency of agricultural residue burning. This study focuses on Central India and explores the potential of Sentinel-2 (10 m) multispectral data to accurately estimate ABA, which can be used to calculate associated emissions. We used a locally adapted multi-temporal burned area detection methodology to quantify burned areas after the winter crop harvest in Central India from 2019 to 2021. A novel satellite data assisted virtual sampling method was used to collect burned and unburned training samples. Monthly ABAs were extracted at 10 m resolution and were compared with existing global burned area products. On average, the derived ABAs were larger (∼9179 km 2 per month) than those reported by global burned area products that are based on MODIS at 500 m and 250 m resolutions. We show that our approach maps ABA from smallholder farms more accurately than other global products, particularly for fields that are smaller than 10 ha. The derived ABAs were further used to estimate emissions due to agricultural residue burning, which can be used as an initial estimate of potential emissions due to ABA in Central India. Overall, the study demonstrates the ability of Sentinel-2 data to detect and quantify ABA in smallholder systems, which can be used to more accurately estimate emissions compared to existing global products.