Generating synthetic Landsat images based on all available Landsat data: Predicting Landsat surface reflectance at any given time

• All available Landsat data are used for predicting surface reflectance.
• Synthetic Landsat images can be generated for any given time.
• Model selection, LASSO, and temporally-adjusted RMSE are used for better modeling.
• Synthetic images are similar to real Landsat images for all image pairs.
• The prediction accuracy is similar in magnitude to the noise levels in Landsat data.

A new algorithm for generating synthetic Landsat images is developed based on all available Landsat data. This algorithm is capable of predicting Landsat surface reflectance for any desired date. It first excludes cloud, cloud shadow, and snow observations, and then uses the remaining clear observations to estimate time series models for each Landsat pixel. Three time series models (a simple model, advanced model, and full model) are used for estimating surface reflectance for each pixel, and the selection of a time series model is dependent on the number of clear observations available: the more clear observations, the more complex the model will be that is used. For each time series model there are three components (seasonality, trend, and breaks), that are used for modeling intra-annual and inter-annual differences and abrupt surface change. Abrupt surface changes are detected by differencing predicted and observed Landsat observations, and if the difference is larger than twice the Root Mean Square Error (RMSE) for six consecutive observations, it will be detected as a “break” in the time series model. The RMSE values are temporally adjusted to provide better threshold range. For each “synthetic” image, a Quality Assessment (QA) Band is provided that contains information on how the time series model was estimated and used for generating the synthetic data. We have applied this approach to six Landsat scenes within the United States. We visually compared the synthetic images with real Landsat images for different kinds of environments and they are similar for all image pairs. We also quantitatively assessed the accuracy of the synthetic data by calculating the RMSE value for all clear Landsat observations. The RMSE values for the three visible bands are the lowest (approximately 0.01), and the Short-wave Infrared (SWIR) bands are slightly higher in magnitude (between 0.01 and 0.02). The Near Infrared (NIR) band has the highest RMSE values (between 0.02 and 0.03). The goal of this paper is to provide Landsat images that are free of cloud, cloud shadow, snow, and Scan Line Corrector (SLC)-off gaps that can be used to derive land cover and bio-physical products.