Free access to monthly gap-free high resolution observations over the contiguous of United States combining images from the Landsat and the MODIS missions. HISTARFM provides the final reflectance estimates as well as the reliable estimation of the uncertainty associated them. Quantitative and qualitative evaluations of the generated products through comparison with other methods confirm the validity of the approach.

We present an improvement of Landsat temporal series from 2015 to 2019 free of clouds and noise reduction using HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM) algorithm developed on Google Earth Engine (GEE). HISTARFM combines the temporal series of Moderate Resolution Image Spectroradiometer (MODIS) at a coarse spatial resolution (500m) and Landsat missions providing fine-scale spatial resolution. Additionally, a mean monthly climatology of Landsat data of the 10 previous years and the blending of MODIS and Landsat observations are needed by the model. The HISTARFM algorithm relies on bias-aware Kalman filter method implemented in GEE. Quantitative and qualitative evaluations of the generated products through comparison with other methods confirm the validity of the approach.

Gap-free Landsat data can be explored without GEE account from https://almoma153.users.earthengine.app/view/explorehistarfm

and the data are free available by GEE users from 2015 to 2019 in https://code.earthengine.google.com/?asset=projects/KalmanGFwork/GFLandsat_V1

 

For more information about HISTARFM see: https://www.sciencedirect.com/science/article/pii/S0034425720302716?via%3Dihub


03.07.2020