Publications

Lang, Q; Zhao, W; Yu, WP; Ma, MG; Xiao, Y; Huang, YJ; Wang, LC (2023). An Iterative Method Initialized by ERA5 Reanalysis Data for All-Sky Downward Surface Shortwave Radiation Estimation Over Complex Terrain With MODIS Observations. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING, 61, 4106315.

Abstract
Accurate estimates of downward surface shortwave radiation (DSSR) are critical for hydrological, biogeochemical, and ecological studies, and remote sensing-based estimation of DSSR is an important way to derive DSSR at different spatiotemporal ranges. However, current estimation algorithms usually somewhat rely on atmospheric parameters or in situ measurements, further blocking the application of these methods. Inspired by the emerging DSSR reanalysis data from the model simulation, this study proposed an integrated method by initializing the estimation model with ERA5 reanalysis data and further refining the estimation through iterative training. The random forest regression method was applied in the estimation model to build the connection between DSSR with the MODIS top-of-atmosphere reflectance, cloud flag, geometry information, elevation, latitude, and coefficient of Sun-Earth distance as input features. To separately consider the impact from cloud cover, the estimation model was established for clear-and cloudy sky conditions. The proposed method was applied to estimate the instantaneous DSSR of MODIS daytime overpasses in the Southwest part of China in 2020. The comparison between the estimates of the initialized model and the finalized model shows that the iterative process improves the DSSR estimates on both spatial distribution and accuracy. Validated by the measurements from nine sites in the study area, the DSSR estimates of the finalized model show a 0.02 higher correlation coefficient (CC) and 7.35-W m-2 lower root-mean-squared error (RMSE) than that of the initialized model. To better evaluate the performance of the proposed method, three popular DSSR products, including ERA5, MCD18A1, and Himawari-8, were introduced to make an intercomparison with the estimation of this study. The validation results showed that the all-sky DSSR estimated in this study had the best accuracy, with a CC of 0.90, a mean bias error of 37.80 W m(-2), an RMSE of 125.30 W m(-2), and a relative RMSE of 42.73%. Obvious improvements can be observed under cloudy and clear-sky conditions. Because of the simplicity and reliable performance of the proposed method, it shows good potential for DSSR estimation.

DOI:
10.1109/TGRS.2023.3323033

ISSN:
1558-0644