Abstract Statistical time series models are increasingly being used to fit medium resolution time series provided by satellite sensors, such as Landsat, for terrestrial monitoring. Cloud and shadows, combined with low satellite repeat cycles, reduce surface observation availability. In addition, only a single year of data can be used where there is high inter-annual variation, for example, over many croplands. These factors reduce the ability to fit time series models and reduce model fitting accuracy. In solution, we propose a novel fill-and-fit (FF) approach for application to medium resolution satellite time series. In the ‘fill’ step, gaps are filled using a recently published algorithm developed to fill large-area gaps in satellite time series using no other satellite data. In the ‘fit’ step, a linear harmonic model is fitted to the gap-filled time series. The FF approach, and the conventional harmonic model fitting without gap filling, termed the F approach, are demonstrated using seven months of Landsat-7 and -8 surface reflectance Analysis Ready Data (ARD) over agricultural regions in North Dakota, Minnesota, Michigan, and Kansas. Synthetic model-predicted reflectance for days through the growing season are examined, and assessed quantitatively by comparison with an independent Landsat surface reflectance data set. The six Landsat reflective band root-mean-square difference (RMSD) between the predicted and the independent reflectance, considering millions of pixel observations for each ARD tile, show that the FF approach is more accurate than the F approach. The mean FF RMSD values varied from 0.025 to 0.026 for the four tiles, whereas the mean F RMSD values varied from 0.026 to 0.047. These mean FF RMSD values are
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- Publication year
2020
- Venue
Remote Sensing of Environment
- Publication date
2020-05-01
- Fields of study
Mathematics, Environmental Science
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