Bias correction of Sentinel-2 with unmanned aerial vehicle multispectral data for use in monitoring walnut fruit forest in western Tien Shan, Kyrgyzstan

A bias correction method using unmanned aerial vehicle (UAV) multispectral data significantly improved the accuracy of Sentinel-2 satellite imagery for monitoring walnut fruit forests in Western Tien Shan, Kyrgyzstan. The study found that Sentinel-2 overestimates vegetation indices like NDVI in open terrains and underestimates them in tree-dominated areas due to subpixel mixing effects. Applying this correction increased the correlation between Sentinel-2 derived vegetation condition index (VCI) and ground-based precipitation data by an average of 3%, enhancing drought monitoring capabilities. The approach leverages submeter-resolution UAV imagery to refine coarse-scale satellite data, offering a scalable solution for ecological studies in remote, challenging terrains like the Tian Shan.

Isaev, E., Kulikov, M., Shibkov, E., & Sidle, R. C. (2022). Bias correction of Sentinel-2 with unmanned aerial vehicle multispectral data for use in monitoring walnut fruit forest in western Tien Shan, Kyrgyzstan. Journal of Applied Remote Sensing, 17(02), 1–15. https://doi.org/10.1117/1.jrs.17.022204