文献类型：期刊文献

英文题名：Satellite-based estimation of terrestrial latent heat in China based on fusion algorithm

作者：Zhang, Nannan[1]; Liu, Yang[1]; Zhao, Hang[1]; Zou, Liqun[1]; Yao, YunJun[2]; Dong, Wentong[1]; Guo, Hongyan[1]; Zhou, Hongying[1]; Huang, Miaofen[3]

机构：[1] Research Institute of Petroleum Exploration and Development, PetroChina, Beijing, 100083, China; [2] State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, 100875, China; [3] Faculty of Mathematics and Computer Science, Guangdong Ocean University, Guangdong, 524088, China

年份：2018

卷号：2018-July

起止页码：4973

外文期刊名：International Geoscience and Remote Sensing Symposium (IGARSS)

收录：EI(收录号：20191206669237)

语种：英文

外文摘要：Different application conditions applied for different models used in satellite-based terrestrial latent heat estimation. Therefore, great uncertainties exist in large-scale application of such methods. BMA fusion algorithm, which has combined three commonly used models (including Penman Monteith LE algorithm, Priestly-Taylor LE algorithm and Semi-empirical Penman LE algorithm), is then carried out in this study. It can effectively reduce the uncertainty and improve the accuracy of terrestrial latent heat estimation comparing with single algorithm itself after testing with 190 eddy covariance tower site data (Fluxnet site data). The error of mean square root (RMSE) has decreased by 5W/m2 and the value of average correlation coefficient (R2) has increased by 0.05 for most of observation points in this test. The fusion model has applied in China to carry out a monthly-based latent heat estimation and mapping for data achieved from 1989 to 2006. The estimation result, after analyzed quantitatively, returns sound precision and stability, which can make up the shortage of current latent heat products. Meanwhile, the spatial distribution analysis shows that: latent heat spatial distribution is the combined contribution of temperature, precipitation and vegetation together. The temporal distribution of latent heat has obvious seasonal characteristic, which is low in winter and high in summer. The latent heat value is declined by 0.07 W/m2 per year for past 18 years. ? 2018 IEEE