全球25km每日潜热通量和显热通量数据集（2010-2020）

该数据集为全球每日潜热通量和显热通量数据集，空间分辨率为0.25°(约25 km)，时间覆盖范围为2010年1月1日~2020年12月31日。地表潜热通量和显热通量是地球表面的关键物理参量，是地球资源监测中能量收支平衡、水分收支平衡、生态系统过程等研究的重要变量。该数据集采用机器学习方法估算Priestley-Taylor系数，进而得到潜热通量，并基于能量平衡方程估算出显热通量。模型的输入数据包括MODIS的植被指教产品、GLASS的叶面积指数产品、ERA5的辐射和气象变量产品以及自主研发的全球空气动力学粗糙度产品等。对比FLUXNET观测结果，该数据集的潜热通量验证RMSE为~22 W/m2，显热通量验证RMSE为~26 W/m2。数据集以NetCDF格式存储，包括4个变量：经度（lon）、纬度（lat）、潜热通量（Latent heat flux）和显热通量（Sensible heat flux）。数据坐标系统为WGS84，经度范围为180°W~180°E，纬度范围为90°N~90°S。潜热通量和显热通量单位为W/m2。

This dataset is a global daily latent and sensible heat flux dataset with a spatial resolution of 0.25° (approximately 25 km), covering the period from January 1, 2010 to December 31, 2020. Surface latent and sensible heat fluxes are key physical parameters of the Earth's surface, and serve as important variables for studies such as energy budget, water budget, and ecosystem processes in Earth resource monitoring. This dataset estimates the Priestley-Taylor coefficient using machine learning methods to derive latent heat flux, and then calculates sensible heat flux based on the energy balance equation. The input data of the model include MODIS vegetation index products, GLASS leaf area index products, ERA5 radiation and meteorological variable products, and the self-developed global aerodynamic roughness product, among others. Compared with FLUXNET observations, the validated RMSE of latent heat flux in this dataset is approximately 22 W/m², while the validated RMSE of sensible heat flux is approximately 26 W/m². The dataset is stored in NetCDF format, containing four variables: longitude (lon), latitude (lat), latent heat flux, and sensible heat flux. The data coordinate system is WGS84, with longitude ranging from 180°W to 180°E and latitude ranging from 90°N to 90°S. The units of latent and sensible heat fluxes are W/m².

搜集汇总

背景与挑战

背景概述

该数据集提供了2010年至2020年全球每日的潜热通量和显热通量数据，空间分辨率约为25公里，以NetCDF格式存储。它采用机器学习方法结合多源遥感数据估算，验证误差较小（潜热通量RMSE约22 W/m²，显热通量RMSE约26 W/m²），适用于地球表面能量和水分收支平衡研究。