Fully automated drought analysis from the products of the moderate resolution imaging spectroradiometer (MODIS)

Abstract

Remote sensing data have become one of the important data sources to monitor and analyze drought. However, how to access, retrieve, process, and analyze Earth observation data, and discover drought from them in an automated manner is a big challenge to researchers. In this study, one of the most common drought monitoring and analysis methods was implemented to streamline and automate the process of accessing to and downloading from the data server, pre-processing the data, calculating drought indices, and producing the drought maps following the well-known hierarchical concept: Data, Information, and Knowledge. All developed modules can act as independent components. They can also be seamlessly integrated into the process or reused by other researchers. Several open-source libraries in Python such as Geospatial Data Abstraction Library (GDAL) and Numerical Python (NumPy) were extensively exploited in the implementation. With the help of these libraries, one of the satellite data-derived vegetation indices named the Vegetation Health Index was calculated.