Remote Sensing Nonlinear Model Supercomputing

The novelty of this algorithm is to apply multiple sources of remote sensing data combined with data of unmanned weather stations topography ground coverDEM and astronomy and calendar rules. The results indicated that the model has high accuracy reliability and generalization ability. Factors such as cloudiness ground vegetation and water vapor show little interference so the model seems suitable for large area retrieving under natural conditions. The required high-performance computation was achieved by a CPU + GPU isomery and synergy parallel computation system that improved computing speed by more than 1000-fold with easily extendable computing capability. We found that the current algorithm is superior to seven major split-window algorithms and their best combined algorithms based on prediction errors root-meansquare errors and the percentage of data points with