5 Feb 2026

GeoClimate Intelligence Platform: A Web-Based Framework for Environmental Data Analysis

Abstract: Environmental science education faces a critical barrier: programming requirements prevent students, novice researchers, and domain experts from accessing planetary-scale datasets. This study presents the GeoClimate Intelligence Platform, a web-based framework powered by Google Earth Engine (GEE) that eliminates programming barriers while maintaining research-grade analytical capabilities. The platform comprises five integrated modules: GeoData Explorer for climate dataset access, Climate Analytics implementing 20+ ETCCDI-compliant climate indices, Hydrology Analyzer for precipitation analysis and return periods, Product Selector for dataset validation, and Data Visualizer for interactive analysis. This modular design supports integrated workflows while maintaining analytical independence across specialized functions. Development was motivated by workshops where students found programming barriers insurmountable despite strong motivation. Educational validation through university coursework demonstrated effectiveness. Performance evaluation shows robust scalability from educational to research-scale applications. The platform requires only a GEE account and operates through web browsers, eliminating software installation. This accessibility transformation enables broader participation in data-driven environmental problem-solving with scientific rigor, democratizing sophisticated environmental analysis for educational and research communities.

7 Aug 2024

A Broadscale Assessment of Sentinel-2 Imagery and the Google Earth Engine for the Nationwide Mapping of Chlorophyll a

Abstract: Harmful algal blooms degrade water quality and can adversely impact human and wildlife health. Monitoring these at scale is difficult due to the lack of coincident data. Additionally, traditional field collection methods are labor- and cost-prohibitive, resulting in disparate data collection in capable of capturing the physical and biological variations within waterbodies or regions. This research attempts to alleviate this by leveraging large, public, water quality databases and open-access Google Earth Engine-derived Sentinel-2 imagery to evaluate the practical usability of four common chlorophyll a algorithms as a proxy for detecting and mapping algal blooms nationwide. Chlorophyll a data were aggregated from spatially diverse sites across the continental US between 2019 and 2022. The 2BDA and the NDCI algorithms were the most viable for broadscale mapping of chlorophyll a, which performed moderately well, encompassing highly diverse spatial, temporal, and physical conditions. The most compatible field data acquisition method was the chlorophyll a, water, trichromatic method, uncorrected. Resulting data indicate the feasibility of utilizing band ratio algorithms for broadscale detection and mapping of chlorophyll a as a proxy for HABs, which is valuable when coincident data are unavailable or limited.