Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations
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...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-03-01
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| Series: | Remote Sensing |
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| Online Access: | http://www.mdpi.com/2072-4292/10/3/398 |
| _version_ | 1819003776389873664 |
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| author | Ana F. Militino M. Dolores Ugarte Unai Pérez-Goya |
| author_facet | Ana F. Militino M. Dolores Ugarte Unai Pérez-Goya |
| author_sort | Ana F. Militino |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T23:26:22Z |
| format | Article |
| id | doaj.art-ddaef9962ba1401d92a77d241cde886d |
| institution | Directory Open Access Journal |
| issn | 2072-4292 |
| language | English |
| last_indexed | 2024-12-20T23:26:22Z |
| publishDate | 2018-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Remote Sensing |
| spelling | doaj.art-ddaef9962ba1401d92a77d241cde886d2022-12-21T19:23:24ZengMDPI AGRemote Sensing2072-42922018-03-0110339810.3390/rs10030398rs10030398Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge StationsAna F. Militino0M. Dolores Ugarte1Unai Pérez-Goya2Department of Statistics and Operations Research, Public University of Navarre, 31006 Pamplona, SpainDepartment of Statistics and Operations Research, Public University of Navarre, 31006 Pamplona, SpainDepartment of Statistics and Operations Research, Public University of Navarre, 31006 Pamplona, SpainMultitemporal 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.http://www.mdpi.com/2072-4292/10/3/398krigingspatial statisticsthin-plate splinesoutlierssmoothing |
| spellingShingle | Ana F. Militino M. Dolores Ugarte Unai Pérez-Goya Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations Remote Sensing kriging spatial statistics thin-plate splines outliers smoothing |
| title | Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations |
| title_full | Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations |
| title_fullStr | Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations |
| title_full_unstemmed | Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations |
| title_short | Improving the Quality of Satellite Imagery Based on Ground-Truth Data from Rain Gauge Stations |
| title_sort | improving the quality of satellite imagery based on ground truth data from rain gauge stations |
| topic | kriging spatial statistics thin-plate splines outliers smoothing |
| url | http://www.mdpi.com/2072-4292/10/3/398 |
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