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Downward longwave radiation (DLR) at the earth's surface is a major component of surface radiation budget and thus the climate, and remote sensing provides the most effective method to get surface DLR on a large scale. This paper presents a comparison of several DLR algorithms for both clear-sky and cloudy-sky conditions. These algorithms were applied to MODIS Terra data and extensively validated using one year's ground data at 13 stations around globe. For clear sky conditions, two algorithms using atmospheric parameters, two algorithms using satellite thermal radiances, and an algorithm that combined using satellite thermal data and atmospheric parameters were compared. The validation result indicated that the first type of algorithms often underestimated DLRs over high altitude regions, while the second type of algorithms performed well over these regions but had significant positive errors over arid regions. The third type of algorithm had acceptable results over all types of regions. Furthermore, the study found that using NCEP derived atmospheric parameters could effectively improve the performance of the first algorithms over high altitude regions, compared with MODIS atmospheric product. For cloudy conditions, three parametric algorithms that determined cloud radiative effect by cloud base temperature, and an empirical algorithm that employed cloud water path and cloud ice path were compared. The validation results indicated that the empirical algorithm had best results in most of the sites, while the three parametric algorithms were greatly influenced by the uncertainties of cloud parameters.