Remote sensing of earth’s energy budget:synthesis and review

The Earth’s climate is largely determined by its energy budget.Since the 1960s,satellite remote sensing has been used in estimating these energy budget components at both the top of the atmosphere(TOA)and the surface.Besides the broadband sensors that have been traditionally used for monitoring Earth’s Energy Budget(EEB),data from a variety of narrowband sensors aboard both polar-orbiting and geostationary satellites have also been extensively employed to estimate the EEB components.This paper provides a comprehensive review of the satellite missions,state-of-the art estimation algorithms and the satellite products,and also synthesizes current understanding of the EEB and spatiotemporal variations.The TOA components include total solar irradiance,reflected shortwave radiation/planetary albedo,outgoing longwave radiation,and energy imbalance.The surface components include incident solar radiation,shortwave albedo,shortwave net radiation,longwave downward and upwelling radiation,land and sea surface temperature,surface emissivity,all-wave net radiation,and sensible and latent heat fluxes.Some challenges,and outlook such as virtual constellation of different satellite sensors,temporal homogeneity tests of long time-series products,algorithms ensemble,and products intercomparison are also discussed.

A case study for intercomparison of land surface temperature retrieved from GOES and MODIS

In recent years,algorithms have been developed to derive land surface temperature(LST)from geostationary and polar satellite systems.However,few works have addressed the intercomparison between Geostationary Operational Environmental Satellites(GOES)and the available suite of polar sensors.In this study,differences in LSTs between GOES and MODerate resolution Imaging Spectroradiometer(MODIS)have been compared and also evaluated against ground observations.Due to the lack of split-window(SW)channels in the GOES M(12)-Q era,a dual-window algorithm using a mid-infrared 3.9µm channel is compared with traditional SW algorithm.It is found that the differences in LST between different platforms are bigger during daytime than those during nighttime.During daytime,LSTs from GOES with the dualwindow algorithm are warmer than MODIS LSTs,while LSTs from the SW algorithm are close to MODIS LSTs.The difference during daytime is found to be related to anisotropy in satellite viewing geometry,and land surface properties,such as vegetation cover and especially surface emissivity at middle infrared(MIR)channel.When evaluated against ground observations,the standard deviation(precision)error(2.35 K)from the dual window algorithm is worse than that(1.83 K)from the SW algorithm,indicating the lack of split-window channel in the GOES M(12)-Q era may degrade the performance of LST retrievals.