Using rvi and ndvi time series for cropland mapping with time-weighted dynamic time warping

Abstract. Monitoring and management of agricultural lands are essential due to reasons affecting agriculture, such as increasing population and global climate. With the increase in the temporal resolution of satellite systems, time-series classifications have become popular in cropland mapping. Because annual plants can give similar spectral reflectance values on the same date. In this context, agricultural land (∼500 km2) was selected in the south of South Dakota in the United States. The area includes alfalfa, corn, soybeans, winter wheat plants, developed, grassland/pasture, herbaceous wetlands, and open water areas. The study aims to map croplands with vegetation indices produced by annual Sentinel-1 and Sentinel-2 satellites. In this context, Radar Vegetation Index (RVI) produced from 25 Sentinel-1, and the Normalized Difference Vegetation Index (NDVI) produced from 26 Sentinel-2 satellites were used for 2020. We used the Time-Weighted Dynamic Time Warping (TWDTW) algorithm, which separates and classifies the similarities between two time series with variable speeds with time constraints. For mapping, the indices were classified both individually and combined. The highest overall accuracy (77.2%) was obtained with the combined use of NDVI and RVI. Among the plant classes, the lowest accuracy (83.71%) was found, and it was determined that the plant classes did not mix much. Sentinel-2 satellite is not available before April due to weather conditions in the region. For this reason, since the Sentinel-1 satellite is not affected by weather conditions, it is thought that the use of two satellites together will be beneficial in time series analysis.