Wang, C.; Tang, B.-H.; Wu, H.; Tang, R.; Li, Z.-L. Estimation of Downwelling Surface Longwave Radiation under Heavy Dust Aerosol Sky. Remote Sens.2017, 9, 207.
Wang, C.; Tang, B.-H.; Wu, H.; Tang, R.; Li, Z.-L. Estimation of Downwelling Surface Longwave Radiation under Heavy Dust Aerosol Sky. Remote Sens. 2017, 9, 207.
The variation of aerosols, especially dust aerosol, in time and space plays an important role in climate forcing studies. Aerosols can effectively reduce land surface longwave emission and re-emit energy at a colder temperature, making estimation of downwelling surface longwave radiation (DSLR) with satellite data difficult. Using the latest atmospheric radiative transfer code (MODTRAN 5.0), we simulate the outgoing longwave radiation (OLR) and DSLR under different land surface and atmospheric profile conditions. The results show that dust aerosol has an obvious “warming” effect to longwave radiation compared with other aerosols, that aerosol longwave radiative forcing (ALRF) increased with increasing aerosol optical depth (AOD), and that the atmospheric water vapor content (WVC) is critical to the understanding of ALRF. A method is proposed to improve the accuracy of DSLR estimation from satellite data for the skies under heavy dust aerosols. The AOD and atmospheric WVC under cloud-free conditions with a relatively simple satellite-based radiation model that yields the high accurate DSLR under heavy dust aerosol are used explicitly as model input to reduce the effects of dust aerosol on the estimation of DSLR. Validations of the proposed model with satellites data and field measurements show that it estimates the DSLR accurately under heavy dust aerosol skies. The root mean square errors (RMSEs) are 20.4 W/M2 and 24.2 W/M2 for Terra and Aqua satellites, respectively, at the Yingke site, and the biases are 2.7 W/M2 and 9.6 W/M2, respectively. For the Arvaikheer site, the RMSEs are 23.2 W/M2 and 19.8 W/M2 for Terra and Aqua, respectively, and the biases are 7.8 W/M2 and 10.5 W/M2, respectively. The proposed method is especially applicable to acquire relatively high accurate DSLR under heavy dust aerosol using MODIS data with available WVC and AOD data.
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.