Among all the natural disasters, drought is one of the most difficult to understand and predict. It can lead to enormous decrease in crop production and the amount in poultry and livestock, and thus endangering food securities and economics. Due to the great impacts drought exerted upon agriculture, more and more scientists and researchers have their attention focused onto the cause and outcomes of agricultural drought. And they are yearning for a tool that would enable easy data downloading/accessing, calculation, analysis, and decision-making.
Huge amount of satellite data, station-based observations, and drought related statistical information are stored separately in different servers (ftp, http or others) by the data providing agencies or groups. Researchers spend lots of time downloading data, and multiple Terabytes of space storing them. These data are often isolated from each other, and not always reusable (after being calculated or analyzed for a single index). Besides the big data challenge brought to drought researchers, methods that each agency used for calculating drought indices and analyzing drought are different, and most of them have not been known by the general public. There are hundreds of drought indicators in the field, yet not a platform exists for users to view how each of the existing indicators is being formed – we have to look into publications or individual user manuals for the detailed information of the data source, processing methods, calculation formula and other meta-data. Imagine how much time can be saved if we can use a platform that displays how each index is being made at each procedure from data collection to result analysis. A one-stop self-service drought information cluster can facilitate farmers and decision-makers, and the general public to have basic understanding of agricultural drought, and build up their own drought indicators from templates. And such a cluster will become an excellent model that carries out partnership and interoperability.
The system is to facilitate users to build up their own drought indicators in 3 steps: (1) Choose Dimension from Vegetation Conditions, Soil Moisture Conditions, Surface Temperature, and Crop Phenology, etc. (2) Choose Indicators from Vegetation Condition Index (VCI), Vegetation Health Index (VHI), Normalized Difference Water Index (NDWI), etc. (3) Choose verification source.
The goal of agricultural drought information cluster is for users to define drought tailoring their own needs targeted for various applications, and to enable data, technology, and drought information sharing among different groups. It is to provide a comprehensive monitoring and forecasting system that incorporates all principal components necessary for the analysis of agricultural drought.