Water Quality Monitoring of In-Land Lakes in East Africa: How Open Source Tethys and Google Earth Engine Platforms are Improving Water Quality Data Analysis, Visualization and Decision Making

Data driven decision-making processes on trans-boundary in-land lakes in East Africa are highly dependent on data availability and skills necessary for processing large volumes of water quality data. Remote sensing data products from space-borne sensors provide spatial and frequent temporal monitoring of East African in-land lakes where in-situ data collection challenges are evident. Skill gaps are also addressed by providing intuitive web applications that provide user interfaces that are easier to use. In this paper, we explore the different capabilities of the innovative cloud-computing repository of remote sensing data, Google Earth Engine (GEE), to (1) reduce data processing computing time on multi-sensor collections, (2) reduce remote sensing skill-gap for water quality professionals by providing click options for initiating server-side system calls to GEE, (3) provide options for analysis of remote sensing data to monitor water quality, floating vegetation and land use land cover in the lake basins, (4) and provide a platform for participatory validation of water quality remote sensing products of trans-boundary water resources which is an effort towards bridging the in-situ data sharing challenges. It will also explore methods for communicating water quality products to different user groups of water quality information using the Tethys Platform by (a) increasing user interactions with complex, technical-based web applications, and (b) by improving the web experience of users through easy to use functionalities and data processing work-flows.