Name
Title | ||
|---|---|---|
Discrepancies in the Simulated Global Terrestrial Latent Heat Flux from GLASS and MERRA-2 Surface Net Radiation Products. |
Abstract | ||
|---|---|---|
Surface all-wave net radiation (Rn) is a crucial variable driving many terrestrial latent heat (LE) models that estimate global LE. However, the differences between different Rn products and their impact on global LE estimates still remain unclear. In this study, we evaluated two Rn products, Global LAnd Surface Satellite (GLASS) beta version Rn and Modern-Era Retrospective Analysis for Research and Applications-version 2 (MERRA-2) Rn, from 2007-2017 using ground-measured data from 240 globally distributed in-situ radiation measurements provided by FLUXNET projects. The GLASS Rn product had higher accuracy (R(2)increased by 0.04-0.26, and RMSE decreased by 2-13.3 W/m(2)) than the MERRA-2 Rn product for all land cover types on a daily scale, and the two Rn products differed greatly in spatial distribution and variations. We then determined the resulting discrepancies in simulated annual global LE using a simple averaging model by merging five diagnostic LE models: RS-PM model, SW model, PT-JPL model, MS-PT model, and SIM model. The validation results showed that the estimated LE from the GLASS Rn had higher accuracy (R(2)increased by 0.04-0.14, and RMSE decreased by 3-8.4 W/m(2)) than that from the MERRA-2 Rn for different land cover types at daily scale. Importantly, the mean annual global terrestrial LE from GLASS Rn was 2.1% lower than that from the MERRA-2 Rn. Our study showed that large differences in satellite and reanalysis Rn products could lead to substantial uncertainties in estimating global terrestrial LE. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.3390/rs12172763 | REMOTE SENSING |
Keywords | DocType | Volume |
surface net radiation,terrestrial latent heat flux,GLASS,MERRA-2,uncertainty | Journal | 12 |
Issue | Citations | PageRank |
17 | 0 | 0.34 |
References | Authors | |
0 | 12 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaozheng Guo | 1 | 1 | 3.42 |
| Yunjun Yao | 2 | 105 | 30.36 |
| Yuhu Zhang | 3 | 3 | 2.43 |
| Yi Lin | 4 | 53 | 12.43 |
| bo jiang | 5 | 42 | 7.73 |
| Kun Jia | 6 | 42 | 10.20 |
| Xiaotong Zhang | 7 | 159 | 27.16 |
| Xianhong Xie | 8 | 15 | 4.51 |
| Lilin Zhang | 9 | 6 | 3.56 |
| Ke Shang | 10 | 1 | 4.09 |
| Junming Yang | 11 | 1 | 3.42 |
| Xiangyi Bei | 12 | 1 | 3.75 |