Scientific article by our university’s Geoinformatics graduate published in a Q1 Remote Sensing Journal

19.08.2026

#Education #News


A scientific article by Oybek Tukhtamishev, a graduate of our university’s Master’s program in Geoinformatics, has been published in the Q1-ranked journal Remote Sensing.

The article, titled “A Comprehensive Machine Learning Approach for Crop Classification Using Multi-Sensor Satellite Datasets and Multiple Vegetation Indices,” was published on August 4, 2026. The research was conducted jointly by scientists from our university and collaborating researchers from Hungary, the United States, and Egypt.

The study focuses on the high-accuracy identification and mapping of wheat and cotton fields in the example of the Urta Chirchiq district of Tashkent Region, using satellite data and Machine Learning technologies. The researchers used data from Landsat-8, Sentinel-1, and Sentinel-2, as well as vegetation indices including NDVI, GNDVI, EVI, SAVI, MSAVI, and NDRE.

A key aspect of the study is the combined use of optical Sentinel-2 and radar Sentinel-1 data, along with a comparison of different machine learning algorithms. As a result, the Gradient Boosting Tree (GBT) algorithm achieved an accuracy of 97.06%, while Random Forest (RF) achieved 96.92%. Integrating Sentinel-1 and Sentinel-2 data improved classification accuracy by up to 2.38% compared with using optical data alone, and by up to 13.28% compared with using radar data alone.

The findings have significant practical value for rapid and accurate crop mapping, assessing agricultural land areas, improving irrigation and fertilization management, forecasting crop yields, and supporting decision-making aimed at ensuring food security.

Because the proposed approach is based on freely available satellite data, it can also be applied to agricultural monitoring across large areas.

The university community is proud to have talented and dedicated young researchers such as Oybek Tukhtamishev and wishes him continued success and new achievements in his future scientific career.