Interpolation of the Mean Anomalies for Cloud Filling in Land Surface Temperature and Normalized Difference Vegetation Index

Ana F. Militino, M. Dolores Ugarte, Unai Pérez-Goya, Marc G. Genton

Research output: Contribution to journalArticlepeer-review

18 Scopus citations


When monitoring time series of remote sensing data, it is advisable to fill gaps, i.e., missing or distorted data, caused by atmospheric effects or technical failures. In this paper, a new method for filling these gaps called interpolation of the mean anomalies (IMA) is proposed and compared with some competitors. The method consists of: 1) defining a neighborhood for the target image from previous and subsequent images across previous and subsequent years; 2) computing the mean target image of the neighborhood; 3) estimating the anomalies in the target image by subtracting the mean image from the target image; 4) filtering the anomalies; 5) averaging the anomalies over a predefined window; 6) interpolating the averaged anomalies; and 7) adding the interpolated anomalies to the mean image. To assess the performance of the IMA method, both a real example and a simulation study are conducted with a time series of Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA and MODIS AQUA images captured over the region of Navarre (Spain) from 2011 to 2013. We analyze the land surface temperature (LST) day and night, and the normalized difference vegetation index (NDVI). In the simulation study, seven sizes of artificial clouds are randomly introduced to each image in the studied time series. The square root of the mean-squared prediction error (RMSE) between the original and the filled data is chosen as an indicator of the goodness of fit. The results show that the IMA method outperforms Timesat, Hants, and Gapfill (GF) in filling small, moderate, and big cloud gaps in both the day and night LST and NDVI data, reaching RMSE reductions of up to 23%.
Original languageEnglish (US)
Pages (from-to)6068-6078
Number of pages11
JournalIEEE Transactions on Geoscience and Remote Sensing
Issue number8
StatePublished - 2019

Bibliographical note

KAUST Repository Item: Exported on 2020-10-01
Acknowledgements: This work was supported in part by project MTM2017-82553-R (AEI/FEDER, UE), in part by projects PI015-2016 and PI043-2017 (Government of Navarra, Spain), and in part by the Fundación CANObra Social Caixa-UNED Pamplona 2016 and 2017.


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