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This dataset supports the research article titled "Spatial scale-up modeling of forest canopy water storage capacity by using multi-source remote sensing data: A case study in southern Jiangxi province". 1. General Overview: This repository contains the comprehensive dataset used to quantify and upscale the forest canopy water storage capacity (CWSC) from the plot scale to the regional scale in southern Jiangxi province, China. The dataset integrates field-measured ecohydrological parameters, high-resolution Unmanned Aerial Vehicle LiDAR (UAV-LiDAR) metrics, and optical remote sensing indicators. It provides a solid data foundation for assessing regional forest water conservation functions. 2. Data File Descriptions: The uploaded compressed package includes three primary data files corresponding to the stepwise upscaling modeling process: File 1: Surface area and water-holding characteristics of leaves and branches for different tree types.xlsx This file contains the laboratory-measured specific water storage capacity data for leaves and branches of typical coniferous and broad-leaved tree species in the study area. The data was determined using the classical indoor water immersion method. It reveals the fundamental water retention differences at the microscopic organ scale. File 2: Measured values and ALS variables from 52 foundation plots.xlsx This file provides the plot-scale modeling dataset. It includes the accurate CWSC measured values (calculated by integrating specific water storage capacity with Handheld Laser Scanning (HLS) derived branch/leaf surface areas) and the corresponding 3D structural and topographic parameters extracted from UAV-LiDAR data (such as canopy volume, average canopy area, canopy closure, leaf area index, elevation, etc.) across 52 representative forest plots. File 3: Optical remote sensing indicators for regional projection.xlsx This file serves the final regional upscaling process. It pairs the CWSC values predicted at the UAV scale with macroscopic optical remote sensing indicators (including NDVI, FVC, optical LAI, Aboveground Biomass (AGB), Forest Dominant Height (FDH), and topographic factors like Elevation and Slope). These data were used to construct and validate the robust regional-scale spatial extrapolation model. 3. Application: This dataset can be used to reproduce the stepwise multiple linear regression models presented in the manuscript, validate the scaling effects of ecohydrological parameters, and provide data support for machine learning-based forest canopy water retention predictions.