DETECTION OF IRRIGATED AND RAINFED CROPS WITH MACHINE LEARNING MULTIVARIATE TIME-SERIES OBJECT-BASED CLASSIFICATION USING SENTINEL-2 IMAGERY

The aim of this paper is to classify irrigated crop fields in Kenya during the year 2021 using Machine-Learning (ML) techniques and Sentinel-2 time-series data, identifying the best performing classifier and combinations of spectral indices in terms of accuracy scores. To observe changes due to irrigation and monitoring vegetation status, remote sensing data can provide valuable information. By combining different spectral bands, high-resolution multispectral Sentinel-2 imagery can provide various vegetation indices, sensitive to certain aspects of crop status and moisture content. To distinguish irrigated fields using these indices, ML methods for multivariate time-series classification can be applied. By training a ML model on time-series data consisting of six different vegetation indices, two different classifiers were tested: the Time Series Forest (TSF) and the Weasel-Muse algorithm (one of the most promising according to Ruiz et al., 2020). In conclusion, both algorithms demonstrated to be reliable for multi-index Sentinel-2 time-series classification, and the Normalized Multiband Drought Index and the Modified Normalized Water Index were revealed as good indicators to detect irrigation.