Pre-Launch Assessment of Simulated CHIME and LSTM Data for Water-Quality Monitoring in a Virtual Constellation Framework
Year: 2026
Type: article
Abstract: The use of Earth Observation data for monitoring of aquatic and coastal ecosystems has expanded significantly, yet gaps remain in the spectral and thermal capabilities needed to detect and characterize liquid discharges. This study evaluates the potential of simulated pre-launch CHIME and LSTM observations, integrated with operational Sentinel-2 and Landsat 8/9 data, for water-quality anomaly detection across diverse aquatic environments, demonstrating that the simulated datasets are sufficiently realistic to reproduce key water-quality and thermal indicators and support pre-operational application development prior to mission launch. A multi-stage workflow was applied: initial anomaly detection with multispectral imagery, thermal verification using LSTM-simulated brightness temperatures processed via linear and quadratic split-window algorithms, and spectral characterization of turbidity and spectral signatures with atmospherically corrected CHIME-simulated data. Results showed consistent spatial patterns of turbidity and surface temperature between simulated and operational products, with CHIME resolving finer spectral features of suspended matter and the quadratic LSTM model achieving low RMSE relative to Landsat thermal references. These findings demonstrate the cross-sensor consistency and complementary information provided by simulated CHIME and LSTM products, supporting their use in the pre-launch development of future multisensor workflows for detecting and characterizing optical and thermal water anomalies. Identification of specific contaminants and attribution of anomalies to individual discharge sources were not assessed in this study.
Source: Remote Sensing
DOI: 10.3390/rs18223891
Language: English
Subfield: Water Science and Technology
Field: Environmental Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $3,258
OpenAlex ID: W7225906265