Sentinel-1 and -2 time-series data-fusion for olive tree identification in heterogeneous land surfaces using Google Earth Engine

Abstract

Olive, a crucial crop for the economies of Mediterranean countries, is expanded to Aegean, Mediterranean, Marmara, South-East and Black Sea regions of Turkey. Identification of olive trees in heterogeneous land surfaces, particularly in mountainous regions is essential for exploitation of un-grafted olive trees. In this study, several samples of olive tree, agriculture, bare-land, urban, forest and sparse vegetation fields located between Bayındır and Tire districts of Izmir province in Turkey, are randomly selected. Independent two sample sets are generated to train the classifier (70%) and for the validation (30%). Several data fusion combinations of time series of Sentinel-1 and Sentinel-2 satellite data with various spectral indices are performed with random forest classifier on Google Earth Engine environment. A new spectral index, named as “DVI Red index (DVIR)” is generated and experimented in the study, as well. Results demonstrated that “Sentinel-1, Sentinel-2 and 10 indices” data fusion performed best overall accuracy (95.5%) as “Sentinel-1 and new ratio index (DVIR)” data fusion performed highest user’s accuracy (97.2%) for olive class. Of 10 spectral indices standalone classifications, DVIR ranked the first for overall accuracy (94.8%) and the third for olive class user’s accuracy (84.4%).