2000-2018年全球1km陆表潜热通量8天数据

陆表潜热通量（Latent Heat Flux, LE）是指陆表土壤蒸发、植被截留蒸发以及植被蒸腾过程中由于水汽相变（水从液态到气态）向大气传输的热量通量，是陆地表层能量循环、水循环和碳循环中最难估算的分量，因此，如何估算高精度的陆表潜热通量，对于分析全球气候变化研究意义重大。目前全球潜热产品大多数采用的是单一的潜热算法，少数采用简单的多算法平均值方法，简单平均值方法的不确定性较大，严重影响了产品的精度。本研究采用贝叶斯方法融合MOD16、RRS-PM、PT-JPL、MS-PT以及UMD-SEMI五种潜热通量算法，结合AVHRR、MODIS和MERRA再分析数据生产1981-2018年高时空分辨率高精度的覆盖全球潜陆表空间连续的热通量遥感产品，产品时间分辨率为8天，空间分辨率2000年前为5km，2000年后为1km，并对收集全球覆盖5大洲大数植被类型的300多个通量站点对产品进行验证。结果表明该算法可以得到高时空分辨率高精度的潜热产品，具有较高的精度。

Land Surface Latent Heat Flux (LE) refers to the heat flux transferred to the atmosphere via water phase transition (from liquid to gaseous state) during soil evaporation from land surfaces, intercepted evaporation from vegetation, and vegetation transpiration. It is the most challenging component to estimate in the land surface energy, water, and carbon cycles, so accurately acquiring high-precision land surface latent heat flux is of great significance for global climate change research. Currently, most global latent heat flux products adopt a single latent heat algorithm, while a small number use a simple multi-algorithm averaging method, which has considerable uncertainty and severely impairs the product accuracy. In this study, we employed the Bayesian method to fuse five latent heat flux algorithms, namely MOD16, RRS-PM, PT-JPL, MS-PT, and UMD-SEMI, combined with AVHRR, MODIS, and MERRA reanalysis data to produce a globally continuous, high spatiotemporal resolution, high-precision remote sensing product of land surface heat fluxes for the period 1981–2018. The product has an 8-day temporal resolution, with a spatial resolution of 5 km before 2000 and 1 km after 2000. We validated the product using over 300 flux sites across five continents that cover most major vegetation types worldwide. The results demonstrate that the proposed method can generate latent heat products with high spatiotemporal resolution and excellent accuracy.

搜集汇总

背景与挑战

背景概述

该数据集提供了2000年至2018年全球陆表潜热通量的8天合成数据，空间分辨率为1公里，通过贝叶斯方法融合了五种潜热通量算法并结合多源遥感与再分析数据生成，确保了高时空分辨率和高精度，并经过全球300多个通量站点的广泛验证，适用于全球气候变化研究。