The Moderate Imaging Spectroradiometer (MODIS) sensors onboard the NASA Terra and Aqua satellites provide the means for frequent measurement and monitoring of the status and seasonal variability in global vegetation phenology and productivity. However, while MODIS reflectance data are often interrupted by clouds, terrestrial processes like photosynthesis are continuous, so MODIS photosynthesis data must be able to cope with cloudy pixels. We developed cloud-correction algorithms to improve retrievals of the MODIS photosynthesis product (PSNnet) corresponding to clear sky conditions by proposing four alternative cloud-correction algorithms, which have different levels of complexity and correct errors associated with cloudy-pixel surface reflectance. The cloud-correction algorithms were applied at four weather stations, two fluxtower sites and the Pacific Northwest (PNW) region of the USA to test a range of cloud climatologies. Application of the cloud-correction algorithms increased the magnitude of both daily and annual MODIS PSNnet results. Our results indicate that the proposed cloud correction methods improve the current MODIS PSNnet product considerably at both site and regional scales and weekly to annual time steps for areas subjected to frequent cloud cover. The corrections can be applied as a post-processing interpolation of PSNnet, and do not require reprocessing of the MOD17A2 algorithm.