1981-2018年全球0.05度陆表潜热通量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 through water phase transition (from liquid to gaseous state) during soil evaporation, vegetation interception evaporation and vegetation transpiration over land surfaces. As one of the most difficult components to estimate in the land surface energy cycle, water cycle and carbon cycle, high-precision estimation of LE is of great significance for global climate change research. At present, most global latent heat products adopt a single latent heat algorithm, while only a few use a simple multi-algorithm averaging method, which has large uncertainty and seriously affects the product accuracy. In this study, we adopted the Bayesian method to fuse five latent heat flux algorithms including MOD16, RRS-PM, PT-JPL, MS-PT and UMD-SEMI, combined with AVHRR, MODIS and MERRA reanalysis data to produce a high spatiotemporal resolution, high-precision and globally continuous land surface latent heat flux remote sensing product from 1981 to 2018. The temporal resolution of the product is 8 days, with the spatial resolution being 5 km before 2000 and 1 km after 2000. We also validated the product using more than 300 flux sites covering most vegetation types across five continents. The results show that the proposed method can obtain high spatiotemporal resolution and high-precision latent heat products with high accuracy.

搜集汇总

背景与挑战

背景概述

该数据集是一个高时空分辨率和高精度的全球陆表潜热通量遥感产品，覆盖1981年至2018年，采用贝叶斯模型平均方法整合多种算法和观测数据，显著提高了潜热通量估计的准确性。