Publications

Feng, CJ; Zhang, XT; Wei, Y; Zhang, WY; Hou, N; Xu, JW; Yang, SY; Xie, XH; Jiang, B (2021). Estimation of Long-Term Surface Downward Longwave Radiation over the Global Land from 2000 to 2018. REMOTE SENSING, 13(9), 1848.

Abstract
It is of great importance for climate change studies to construct a worldwide, long-term surface downward longwave radiation (L-d, 4-100 mu m) dataset. Although a number of global L-d datasets are available, their low accuracies and coarse spatial resolutions limit their applications. This study generated a daily L-d dataset with a 5-km spatial resolution over the global land surface from 2000 to 2018 using atmospheric parameters, which include 2-m air temperature (Ta), relative humidity (RH) at 1000 hPa, total column water vapor (TCWV), surface downward shortwave radiation (S-d), and elevation, based on the gradient boosting regression tree (GBRT) method. The generated L-d dataset was evaluated using ground measurements collected from AmeriFlux, AsiaFlux, baseline surface radiation network (BSRN), surface radiation budget network (SURFRAD), and FLUXNET networks. The validation results showed that the root mean square error (RMSE), mean bias error (MBE), and correlation coefficient (R) values of the generated daily L-d dataset were 17.78 W m(-2), 0.99 W m(-2), and 0.96 (p < 0.01). Comparisons with other global land surface radiation products indicated that the generated L-d dataset performed better than the clouds and earth's radiant energy system synoptic (CERES-SYN) edition 4.1 dataset and ERA5 reanalysis product at the selected sites. In addition, the analysis of the spatiotemporal characteristics for the generated L-d dataset showed an increasing trend of 1.8 W m(-2) per decade (p < 0.01) from 2003 to 2018, which was closely related to Ta and water vapor pressure. In general, the generated L-d dataset has a higher spatial resolution and accuracy, which can contribute to perfect the existing radiation products.

DOI:
10.3390/rs13091848

ISSN: