Using Python® language for the validation of the CCI soil moisture products via SM2RAIN
Luca Ciabatta, Christian Massari, Luca Brocca, Christoph Reimer, Sebastian Hann, Christoph Paulik, Wouter Arnoud Dorigo, Wolfgang Wagner · 2016
Remote sensing techniques provide a new way to obtain hydrological variables (i.e. rainfall and soil moisture), mainly in poorly instrumented areas that are fundamental for natural hazard assessment and mitigation. The ever increasing availability of satellite derived products characterized by high temporal and spatial coverage requires the development of techniques and instruments for big data volume managing. Moreover, the use of open source systems is highly encouraged in order to increase their use by the scientific community. In this study, the application of the SM2RAIN algorithm to the CCI soil moisture product is proposed as a case study. A number of Python® classes and methods have been developed for this purpose, with the aim of creating an open-source web validation tool for SM dataset, within the Earth Observation Data Centre for Water Resources Monitoring (EODC).