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Communication

Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation

1
School of Environmental Engineering, Technical University of Crete, 73100 Chania, Greece
2
Department of Computer Science and Systems Engineering, Wroclaw University of Science and Technology, 50-370 Wroclaw, Poland
3
Institute of Applied and Computational Mathematics, Foundation for Research and Technology, GR-700 13 Heraklion, Greece
4
Computational Intelligence Group, Computer Science Faculty, University of the Basque Country, UPV/EHU, 00685 San Sebastián, Spain
*
Authors to whom correspondence should be addressed.
Academic Editor: Qiangqiang Yuan
Sensors 2021, 21(9), 3132; https://doi.org/10.3390/s21093132
Received: 1 April 2021 / Revised: 22 April 2021 / Accepted: 28 April 2021 / Published: 30 April 2021
The wide availability of satellite data from many distributors in different domains of science has provided the opportunity for the development of new and improved methodologies to aid the analysis of environmental problems and to support more reliable estimations and forecasts. Moreover, the rapid development of specialized technologies in satellite instruments provides the opportunity to obtain a wide spectrum of various measurements. The purpose of this research is to use publicly available remote sensing product data computed from geostationary, polar and near-polar satellites and radar to improve space–time modeling and prediction of precipitation on Crete island in Greece. The proposed space–time kriging method carries out the fusion of remote sensing data with data from ground stations that monitor precipitation during the hydrological period 2009/10–2017/18. Precipitation observations are useful for water resources, flood and drought management studies. However, monitoring stations are usually sparse in regions with complex terrain, are clustered in valleys, and often have missing data. Satellite precipitation data are an attractive alternative to observations. The fusion of the datasets in terms of the space–time residual kriging method exploits the auxiliary satellite information and aids in the accurate and reliable estimation of precipitation rates at ungauged locations. In addition, it represents an alternative option for the improved modeling of precipitation variations in space and time. The obtained results were compared with the outcomes of similar works in the study area. View Full-Text
Keywords: satellite data; geostatistics; space–time residual kriging; machine learning; sum-metric satellite data; geostatistics; space–time residual kriging; machine learning; sum-metric
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MDPI and ACS Style

Varouchakis, E.A.; Kamińska-Chuchmała, A.; Kowalik, G.; Spanoudaki, K.; Graña, M. Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation. Sensors 2021, 21, 3132. https://doi.org/10.3390/s21093132

AMA Style

Varouchakis EA, Kamińska-Chuchmała A, Kowalik G, Spanoudaki K, Graña M. Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation. Sensors. 2021; 21(9):3132. https://doi.org/10.3390/s21093132

Chicago/Turabian Style

Varouchakis, Emmanouil A., Anna Kamińska-Chuchmała, Grzegorz Kowalik, Katerina Spanoudaki, and Manuel Graña. 2021. "Combining Geostatistics and Remote Sensing Data to Improve Spatiotemporal Analysis of Precipitation" Sensors 21, no. 9: 3132. https://doi.org/10.3390/s21093132

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