作者:Zhou, SG (Zhou, Shugui)[ 1,2,3 ] ; Cheng, J (Cheng, Jie)[ 1,2,3,4 ]

REMOTE SENSING

卷: 10  期: 2

文献号: 253

DOI: 10.3390/rs10020253

出版年: FEB 2018

文献类型:Article

摘要

Surface-upwelling longwave radiation (LWUP) is an important component of the surface radiation budget. Under the general framework of the hybrid method, the linear models and the multivariate adaptive regression spline (MARS) models are developed to estimate the 750 m instantaneous clear-sky LWUP from the top-of-atmosphere (TOA) radiance of the Visible Infrared Imaging Radiometer Suite (VIIRS) channels M14, M15, and M16. Comprehensive radiative transfer simulations are conducted to generate a huge amount of representative samples, from which the linear model and the MARS model are derived. The two models developed are validated by the field measurements collected from seven sites in the Surface Radiation Budget Network (SURFRAD). The bias and root-mean-square error (RMSE) of the linear models are -4.59 W/m(2) and 16.15 W/m(2), whereas those of the MARS models are -5.23 W/m(2) and 16.38 W/m(2), respectively. The linear models are preferable for the production of the operational LWUP product due to its higher computational efficiency and acceptable accuracy. The LWUP estimated by the linear models developed from VIIRS is compared to that retrieved from the Moderate Resolution Imaging Spectroradiometer (MODIS). They agree well with each other with bias and RMSE of -0.15 W/m(2) and 25.24 W/m(2) respectively. This is the first time that the hybrid method has been applied to globally estimate clear-sky LWUP from VIIRS data. The good performance of the developed hybrid method and consistency between VIIRS LWUP and MODIS LWUP indicate that the hybrid method is promising for producing the long-term high spatial resolution environmental data record (EDR) of LWUP.

通讯作者地址: Cheng, J (通讯作者)

Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

通讯作者地址: Cheng, J (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China.

通讯作者地址: Cheng, J (通讯作者)

Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing 100875, Peoples R China.

通讯作者地址: Cheng, J (通讯作者)

ARS, USDA, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USA.

地址:

[ 1 ] Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, Beijing 100875, Peoples R China

[ 3 ] Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing 100875, Peoples R China

[ 4 ] ARS, USDA, Hydrol & Remote Sensing Lab, Beltsville, MD 20705 USA