Evergreen conifers are key components of temperate broadleaf and mixed forests, playing a significant role in shaping ecosystem structure, function, and resilience to climate change. While very high-resolution (VHR) satellite imagery enables accurate classification of evergreen conifers and creation of reference fractional cover maps, scaling this capability to regional levels using coarser-resolution time-series satellite data remains challenging. Traditional machine learning approaches are limited by their inability to fully exploit the spatial detail of VHR imagery and capture sequential patterns in satellite time series. To address these limitations, we developed a deep learning-based framework for mapping evergreen conifer fractional cover at 30 m resolution in mountainous forests. The framework integrates a 3D U-Net model to extract spatial and spectral features from bi-temporal WorldView imagery—while mitigating terrain shadows—and a long short-term memory (LSTM) network to learn sequential dependencies from Landsat time series for regression. We compared our framework against a random forest baseline. Independent spatial and temporal transferability assessments showed that our approach achieved an R 2 of 0.71 and an RMSE of 0.14, outperforming the benchmark method. To further interpret the spatial predictions, we quantified the spatial configuration of evergreen conifers using landscape metrics across areas with varying conifer cover. Our findings demonstrate the value of combining multi-source, multi-resolution imagery with deep learning models tailored for spatial and temporal complexity. This framework improves the accuracy and transferability of fractional cover mapping and offers a scalable solution for ecosystem monitoring in topographically complex forested landscapes. • Developed a deep learning framework to map evergreen conifer cover at 30 m resolution. • Integrated 3D U-Net and LSTM models using WorldView and Landsat time-series data. • Achieved high accuracy (R 2 = 0.71, RMSE = 14 %) in spatial and temporal transferability. • Produced the first evergreen conifer fractional cover map across giant panda habitats. • Supports habitat restoration and biodiversity conservation in temperate mixed forests.

We developed a Time-series-based Image Fusion (TIF) algorithm to generate 10-m surface reflectance time series by synthesizing Landsats 8/9 and Sentinel-2 A/B data. Unlike traditional methods that rely on image pairs or thematic maps, TIF extracts all valid pixel-level observation pairs across time to build per-pixel linear regression models. This approach captures the spectral relationships between sensors while accounting for land surface dynamics. A temporal weighting scheme and an iterative refinement strategy improves the fusion process, yielding reusable coefficients that support efficient, scalable 10-m time-series generation. TIF was applied to all Landsat multispectral bands, using native 10-m Sentinel-2 bands (Blue, Green, Red) and resampled bands (NIR and SWIR1/2) for visual assessment, with quantitative accuracy evaluated at the original Sentinel-2 resolutions. Experiments across five U.S. sites show TIF consistently outperforms state-of-the-art methods like STARFM, FSDAF 2.0, Sen2Like, and ESRCNN. For instance, TIF demonstrated a reduction in RMSE by 24 % and an increase in SSIM by 6 % compared to FSDAF 2.0 and ESRCNN, and outclassed STARFM and Sen2Like, which showed weaker results across all metrics. In multi-date change detection, TIF-predicted images achieved a mean F1 score of 0.70 and a mean disagreement rate of 0.05 against reference maps. TIF offers a potential practical and efficient pathway for creating 10-m versions of NASA's HLS products, opening new opportunities for fine-scale, time-sensitive Earth observations. • A novel satellite spatial-temporal fusion algorithm called TIF is proposed. • TIF creates 10-m harmonized Landsat and Sentinel-2 surface reflectance time series. • TIF outperforms other state-of-the-art methods in spectral and spatial accuracy. • TIF operates purely on time-series data, without requiring image pairs. • TIF predictions can be used for detecting land changes.