ESSDD Earth System Science Data Discussions ESSDD Earth Syst. Sci. Data Discuss. 1866-3591 Göttingen, Germany 10.5194/essd-2026-193 A pan-tropical 5-km monthly L-band vegetation optical depth dataset from pan-sharpening-based downscaling Shi Jinan 1 Li Jiaxin https://orcid.org/0000-0002-1237-542X 4 Gao Lianru 5 Liu Siyu 6 Fensholt Rasmus https://orcid.org/0000-0003-3067-4527 6 Ciais Philippe https://orcid.org/0000-0001-8560-4943 7 Li Xiaojun https://orcid.org/0000-0002-3831-4852 8 Yuan Qiangqiang https://orcid.org/0000-0001-7140-2224 9 Wigneron Jean-Pierre https://orcid.org/0000-0001-5345-3618 10 Fan Lei https://orcid.org/0000-0002-1834-5088 2 3 School of Mathematical and Physical Sciences, Chongqing University of Science and Technology, Chongqing, 401331, China Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, School of Geographical Sciences, Southwest University, Chongqing 400715, China Chongqing Engineering Research Center for Remote Sensing Big Data Application, School of Geographical Sciences, Southwest University, Chongqing 400715, China School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China Key Laboratory of Computational Optical Imaging Technology, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, 100094, China Department of Geosciences and Natural Resource Management, University of Copenhagen, Copenhagen 1350, Denmark Laboratoire des Sciences du Climat et de l'Environnement, LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris Saclay, Gif-sur-Yvette 91191, France Faculty of Geosciences and Engineering, Southwest Jiaotong University, Chengdu, 611756, China School of Geodesy and Geomatics, Wuhan University, Wuhan, 430079, China ISPA, UMR 1391, INRAE Nouvelle-Aquitaine, Université de Bordeaux, Villenave d'Ornon F-33140, France 26 05 2026 2026 1 32 Copyright: © 2026 Jinan Shi et al. 2026 This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/ This article is available from https://essd.copernicus.org/preprints/essd-2026-193/ The full text article is available as a PDF file from https://essd.copernicus.org/preprints/essd-2026-193/essd-2026-193.pdf

L-band Vegetation optical depth<span> (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 (R<sup>2</sup></span><span>) of 0.95 and a root mean square error (RMSE</span><span>) of 0.11 when comparing the simulated 25-km L-VOD (L-VOD<sub>25km</sub><sup>sim</sup></span><span>) with the original L-VOD (L-VOD<sub>25km</sub></span><span>) product. Spatially, the 5-km resolution L-VOD (L-VOD<sub>5km</sub></span>) yielded a strong correlation with above-ground biomass (R=0.91, R<sup>2</sup>=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 <a href="https://doi.org/10.11888/RemoteSen.tpdc.303391" target="_blank" rel="noopener">https://doi.org/10.11888/RemoteSen.tpdc.303391</a> (Shi and Fan, 2026).