A pan-tropical 5-km monthly L-band vegetation optical depth dataset from pan-sharpening-based downscaling

Abstract. L-band Vegetation optical depth (L-VOD), as a microwave-derived vegetation indicator, has been widely applied in the monitoring of vegetation dynamics. However, the spatial resolution of 25-km or coarser in existing L-band VOD products limits their applications in ecological monitoring requiring a higher level of spatial details. To mitigate this limitation, we introduce a pan-sharpening-based downscaling method to improve the spatial resolution of L-VOD. By fusing the spatial structural features of the aggregated 5-km resolution European Space Agency Climate Change Initiative (ESA CCI) aboveground biomass (AGB) product, the SMOS L-VOD product over tropical regions was downscaled to generate a monthly 5-km resolution L-VOD dataset spanning 2015 to 2021. The downscaling model demonstrated high accuracy, with a correlation coefficient (R²) of 0.95 and a root mean square error (RMSE) of 0.11 when comparing the simulated 25-km L-VOD (L-VOD_25km^sim) with the original L-VOD (L-VOD_25km) product. Spatially, the 5-km resolution L-VOD (L-VOD_5km) yielded a strong correlation with above-ground biomass (R=0.91, R²=0.86), and temporally dynamics, it accurately characterized the LAI variations of short vegetation and forest area loss at the pixel level over the study period. The results demonstrate that our downscaling method can effectively enhance the spatial resolution of L-VOD while preserving its original spatiotemporal dynamics, and is capable of capturing forest disturbance. This dataset can be downloaded at https://doi.org/10.11888/RemoteSen.tpdc.303391 (Shi and Fan, 2026).