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

Manuel Graña,Emmanouil A Varouchakis,Grzegorz Kowalik,Anna Kamińska-Chuchmała,Katerina Spanoudaki

doi:10.3390/s21093132

Abstract

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.

Highlights

Introduction published maps and institutional affilNowadays, remote sensors such as satellites located in a geostationary, medium or low Earth orbit have multiple applications in innovative research and monitoring Earth processes
The space–time regression or residual kriging (STRK) residuals form a weakly stationary random field [50], and such an approach is usually applied to approximate non-stationarity issues that arise in precipitation data [51,52]
This study of blending satellite and ground precipitation observations using geostatistics and machine learning output shows the advantages of this approach compared to previous applications in the area

Summary

Introduction

Nowadays, remote sensors such as satellites located in a geostationary, medium or low Earth orbit have multiple applications in innovative research and monitoring Earth processes. The accuracy of the measurements gives us the opportunity to achieve a global coverage with a resolution of up to several dozen centimeters. This is possible thanks to increasingly effective instruments mounted in the series of satellites, caused by the dynamic development of technology and permanent investments in the space industry. Satellite data have a wide range of applications in domains such as forestry, agriculture, land management, object detection, military, environment, geology, iations.

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