Estimation of land surface radiation budget from MODIS data

Abstract

Land Surface Radiation Budget (SRB) is responsible for the available energy between the Earth and atmosphere system. Net radiation is the driving force for the transportation and exchange of all matter at the interface between the Earth's surface and the atmosphere, and therefore, significantly affects the climatic forming and change. Accurate estimation of shortwave net radiation (Sn), cloudy-sky allwave net radiation (Rn), and daily integrated Sn at high spatial resolution is essential in regional and global land surface models. The current SRB products have fine temporal and coarse spatial resolutions not suitable for land applications. New hybrid algorithm for Sn estimation has been developed in this study. Sn is estimated from MODIS data under both clear- and cloudy-sky conditions without requiring coarser resolution ancillary data. Therefore, estimated Sn retains the spatial resolution of the raw input data.

Surface all-wave (both shortwave and longwave) net radiation (Rn) controls the input of latent and sensible heat flux into the atmosphere over the Earth's surface. Meteorological datasets are spatially limited and satellite data have the advantage of global spatial coverage; however, difficulty in accurately estimating cloudy-sky longwave net radiation (Ln) undermines efforts to estimate cloudy-sky all-wave net radiation. This study presents methods for estimating cloudy-sky Rn using Sn and other surface variables at 1 km spatial resolution.

Daily integrated Sn is closely related to carbon, water and energy flux simulations. A daily integrated Sn product with a 1-km spatial resolution supports recent high resolution numerical climate and ecosystem simulations. This study describes a method for estimating daily integrated Sn in 1 km resolution based on instantaneous Sn data.

All these algorithms have been validated using seven sites of a SURFace RADiation budget observing network (SURFRAD) in United States, instantaneous Sn is also compared with GEWEX/SRB and ISCCP data.

The new hybrid algorithm developed in the study can be easily implemented to generate operational global products. These finer spatial resolution datasets capture the specific sequence of the redistribution of the available energy at the Earth's surface; therefore, they support recent high resolution land surface models.