remote sensing Article Identification of Mung Bean in a Smallholder Farming Setting of Coastal South Asia Using Manned Aircraft Photography and Sentinel-2 Images Mustafa Kamal 1 , Urs Schulthess 2,* and Timothy J. Krupnik 1 1 CIMMYT-Bangladesh, House 10/B. Road 53, Gulshan-2, Dhaka 1213, Bangladesh;
[email protected] (M.K.);
[email protected] (T.J.K.) 2 CIMMYT-Henan Collaborative Innovation Center, Henan Agricultural University, Zhengzhou 450002, China * Correspondence:
[email protected] Received: 1 October 2020; Accepted: 3 November 2020; Published: 10 November 2020 Abstract: Mung bean (Vigna radiata) plays an important role providing protein in the rice-based diet of the people in Bangladesh. In the coastal division of Barisal, our study area, the average farm size is less than 0.5 ha and individual fields measure about 0.10 ha. The availability of free Sentinel-2 optical satellite data acquired at a 10 m ground sampling distance (GSD) may offer an opportunity to generate crop area estimates in smallholder farming settings in South Asia. We combined different sources of in situ data, such as aerial photographs taken from a low flying manned aircraft, data collected on the ground, and data derived from satellite images to create a data set for a segment based classification of mung bean. User’s accuracy for mung bean was 0.98 and producer’s accuracy was 0.99. Hence, the accuracy metrics indicate that the random tree classifier was able to identify mung bean based on 10 m GSD data, despite the small size of individual fields.