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Open AccessArticle

Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations

Department of Statistics and Operations Research, Public University of Navarre, 31006 Pamplona, Spain
Institute for Advanced Materials (InaMat), Public University of Navarre, 31006 Pamplona, Spain
Department of Mathematics, UNED Pamplona, 31006 Pamplona, Spain
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(3), 398;
Received: 22 December 2017 / Revised: 11 February 2018 / Accepted: 27 February 2018 / Published: 5 March 2018
(This article belongs to the Special Issue Analysis of Multi-temporal Remote Sensing Images)
Multitemporal imagery is by and large geometrically and radiometrically accurate, but the residual noise arising from removal clouds and other atmospheric and electronic effects can produce outliers that must be mitigated to properly exploit the remote sensing information. In this study, we show how ground-truth data from rain gauge stations can improve the quality of satellite imagery. To this end, a simulation study is conducted wherein different sizes of outlier outbreaks are spread and randomly introduced in the normalized difference vegetation index (NDVI) and the day and night land surface temperature (LST) of composite images from Navarre (Spain) between 2011 and 2015. To remove outliers, a new method called thin-plate splines with covariates (TpsWc) is proposed. This method consists of smoothing the median anomalies with a thin-plate spline model, whereby transformed ground-truth data are the external covariates of the model. The performance of the proposed method is measured with the square root of the mean square error (RMSE), calculated as the root of the pixel-by-pixel mean square differences between the original data and the predicted data with the TpsWc model and with a state-space model with and without covariates. The study shows that the use of ground-truth data reduces the RMSE in both the TpsWc model and the state-space model used for comparison purposes. The new method successfully removes the abnormal data while preserving the phenology of the raw data. The RMSE reduction percentage varies according to the derived variables (NDVI or LST), but reductions of up to 20% are achieved with the new proposal. View Full-Text
Keywords: kriging; spatial statistics; thin-plate splines; outliers; smoothing kriging; spatial statistics; thin-plate splines; outliers; smoothing
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MDPI and ACS Style

Militino, A.F.; Ugarte, M.D.; Pérez-Goya, U. Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations. Remote Sens. 2018, 10, 398.

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