环境科学
水蒸气
长波
天空
卫星
栏(排版)
大气科学
气象学
云量
遥感
辐射
云计算
地质学
计算机科学
物理
天文
光学
电信
操作系统
帧(网络)
作者
Tianxing Wang,Jiancheng Shi,Ya Ma,Husi Letu,Xingcai Li
出处
期刊:Isprs Journal of Photogrammetry and Remote Sensing
日期:2020-03-01
卷期号:161: 52-60
被引量:18
标识
DOI:10.1016/j.isprsjprs.2020.01.011
摘要
Abstract Remotely sensed surface longwave downward radiation (LWDR) plays an essential role in studying the surface energy budget and greenhouse effect. Most existing satellite-based methods or products depend on variables that are not readily available from space such as, liquid water path, air temperature, vapor pressure and/or cloud-base temperature etc., which seriously restrict the wide applications of satellite data. In this paper, new nonlinear parameterizations and a machine learning-based model for deriving all-sky LWDR are proposed based only on land surface temperature (LST), column water vapor and cloud-top temperature (CTT), that are relatively readily available day and night for most satellite missions. It is the first time to incorporate the CTT in the parameterizations for estimating LWDR under the cloudy-sky conditions. The results reveal that the new models work well and can derive all-sky global LWDR with reasonable accuracies (RMSE
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