2026-10-13T17:24:10Z https://www.scidb.cn/oai

10.57760/sciencedb.33191 2026-06-16T11:30:29Z

2026-06-16 XJ-SIF: a reconstructed 500-meter SIF dataset for revealing spatiotemporal vegetation dynamics in Xinjiang, China doi:10.57760/sciencedb.33191 en Vegetation photosynthesis in arid regions is critical for assessing ecosystem stability and carbon cycle under climate change. However, existing satellite-based Solar-induced Chlorophyll Fluorescence (SIF) products emphasize large-scale spatial continuity, resulting in overestimation of vegetation signals in arid and semi-arid areas and constraining their applicability for fine-scale vegetation dynamics monitoring. In this study, we reconstructed a long-term (2000–2024) and high-spatiotemporal-resolution (500 m, 8-day) SIF dataset for Xinjiang, China(XJ-SIF), by using a weighted stacking ensemble learning framework that integrates a dynamic land cover masking strategy to effectively eliminate background noise from non-vegetated surfaces. Model validation results show that XJ-SIF achieves superior accuracy, with an R² of 0.922 and an RMSE of 0.131 mW m-2 nm-1 sr-1. SIF; Xinjiang; spatiotemporal variations; 500-meter resolution Jie Zhang Fei Xing Hui Ma EMBARGO https://creativecommons.org/licenses/by-sa/4.0/ dataset Science Data Bank