Estimating High Spatial Resolution Clear-Sky Land Surface Upwelling Longwave Radiation From MODIS Data

Surface upwelling longwave radiation (LWUP) is an important component in the surface radiation budget. Existing satellite-derived LWUP data sets are too coarse to support high-resolution numerical models, and their accuracy needs to be improved. In this paper, we evaluate three methods for estimating clear-sky land LWUP from the Moderate Resolution Imaging Spectroradiometer (MODIS) data at I-km spatial resolution. The three methods are as follows: 1) the temperature-emissivity method; 2) the linear model method; and 3) the artificial neural network (ANN) model method. Methods 2 and 3 are new methods based on extensive radiative transfer simulations and statistical analysis. We explicitly considered surface emissivity effects by incorporating the University of California Santa Barbara emissivity library in the radiative transfer simulation. The three methods were evaluated using ground-measured LWUP from six SURFRAD sites. Although methods 2 and 3 were developed using MODIS Terra atmospheric profiles, they were applied to both Terra and Aqua data because the designs of the two sensors are similar. The root mean squared errors (rmses) of the ANN model method are smaller than that or the other two methods at all sites. The averaged rmses of the ANN model method are 15.89 W/m(2) (Terra) and 14.57 W/m(2) (Aqua); the averaged biases are -8.67 W/m(2) (Terra) and -7.21 W/m(2) (Aqua). The biases and rmses for Aqua are similar to 1.3 W/m(2) smaller than that of Terra. The biases and rmses of the ANN model method are similar to 5 W/m(2) smaller than that of the temperature-emissivity method and similar to 2.5 W/m(2) smaller than that of the linear model method.