The Identification of Irrigated Crop Types Using Support Vector Machine, Random Forest and Maximum Likelihood Classification Methods with Sentinel-2 Data in 2018: Tashkent Province, Uzbekistan

Abstract

Accurately mapping land use and land cover including agricultural use and the state of crops at various stages is important to address specific agro-ecological challenges, to implement sustainable agricultural practices, and monitor crops periodically. This study aims to provide a timely and accurate main irrigated crop types mapping at 10m resolution for Tashkent province based on multi-temporal Sentinel-2 data acquired for the growing season in 2018. This paper shows the potential use of multitemporal Sentinel-2 satellite data to derive an up-to-date irrigated crop types classification map of the study area. As single-date satellite imagery does not allow proper cropland classification, multitemporal and high-resolution Sentinel-2 data was used to capture small cropland fields and specific crop types for the vegetation period (April to October 2018). NDVI monthly profiles of crop types as well as additional 10 m resolution bands 2 and 3 were used as input data to perform and assess three classification algorithms: Support Vector Machine (SVM), Random Forest (RF), and Maximum Likelihood Classification (MLC). Accuracy assessment results showed that SVM showed the highest Overall Accuracy (OA) and Kappa Accuracy (KA). KA of classified images for SVM were 0.90 and 0.89 for the RF algorithm. Both performed well with close values. But MLC showed a lower result of KA 0.60. The paper also compares the area of derived irrigated cropland area with data from the State Committee for Statistics of Uzbekistan for selected crop types. Values for the crops "cotton" and "wheat" derived by SVM and RF methods show a high correlation with the provided statistical data. Based on the results, the SVM classification method is recommended for further mapping and monitoring of irrigated crop types in the region when Sentinel-2 data is used.