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Enhancing tree species composition mapping using Sentinel-2 and multi-seasonal deep learning fusion

Yuwei Cao,N. Coops,Brent Murray,Ian Sinclair

Published 2025 in International Journal of Remote Sensing

ABSTRACT

ABSTRACT Accurate wall-to-wall mapping of tree species composition (TSC) is essential for effective forest management. However, distinguishing species-level information from satellite imagery remains a challenge due to the coarse spatial resolution of open-access satellite imagery. In this study, we present the first systematic evaluation of spatial resolution enhancement and multi-seasonal data fusion for deep learning (DL)-based TSC mapping using Sentinel-2 imagery. Specifically, we assessed: (1) the impact of different spatial resolutions and enhancement methods, comparing native 20 m Sentinel-2 imagery against bilinear resampled imagery at 10 m and 5 m, super-resolution (SR)-enhanced imagery at 10 m and their combined use; (2) the contributions of multi-seasonal imagery and auxiliary environmental data (climate, topography); and (3) the effectiveness of a novel multi-source multi-seasonal fusion (MSMSF) method for integrating seasonal and environmental datasets. Our results demonstrated substantial improvements (7% higher $R_{adj}^2$Radj2) when increasing spatial resolution from 20 m to 10 m and achieved the best result (RMSE = 0.120, $R_{adj}^2$Radj2 = 0.731) by combining bilinear resampled 5 m and SR-enhanced 10 m datasets. Additionally, our proposed MSMSF module and multi-seasonal data outperformed the best single-season model by >5% in terms of $R_{adj}^2$Radj2. These findings establish a new benchmark for DL-based TSC mapping and highlight the novelty of combining resolution enhancement with a detail-preserving fusion strategy to enable scalable, high-precision forest inventories using freely available satellite data.

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