Estimation of CDOM in Inland Waters via Water Bio-Optical Properties Using a Remote Sensing Approach

• Main Author: Li, Jiwei
• Format: Text
• Language: unknown
• Published: ScholarWorks@UMass Amherst 2018
• Subjects: CDOM; carbon cycle; remote sensing; land cover; Landsat 8; Arctic; Biogeochemistry; Environmental Monitoring; Geochemistry
• Online Access: https://scholarworks.umass.edu/dissertations_2/1278; https://scholarworks.umass.edu/cgi/viewcontent.cgi?article=2331&context=dissertations_2

Monitoring of Colored dissolved organic matter (CDOM) in inland waters provides important information for tracing carbon cycle at the land-water interface and studying aquatic ecosystem. Remote sensing estimation of CDOM in the inland waters offers an alternative approach to field samplings in examining CDOM spatial-temporal dynamics. However, CDOM retrieval is a challenge due to the lack of algorithm for resolving bottom effect in shallow inland waters. Moreover, an effective approach based on multi-spectral, high spatial resolution and global coverage satellite images is in urgent need. To resolve these challenges, shallow water bio-optical properties (SBOP) algorithm was developed to overcome bottom reflectance effect on the total water leaving reflectance in shallow inland water. SBOP algorithm included the bottom reflectance in building underwater light transfer model. It was designed based on the field spectral data from four cruises in Lake Huron. SBOP algorithm had an obviously advantage over previous deep water CDOM algorithm (e.g. QAA-CDOM). In this study, Landsat-8 multi-spectral satellite imagery was selected to derive CDOM spatial-temporal dynamics in lake and river waters. The coastal blue band (443 nm), global coverage and high spatial resolution (30 m) of Landsat-8 images offered suitable data for inland water CDOM mapping. The SBOP algorithm was applied on Landsat-8 images in broad ranges of inland waters with high accuracy (Lake Huron (R2 = 0.87), 14 northeastern freshwater lakes (R2 = 0.80), and 6 large Arctic Rivers (R2 = 0.87)). Both the spatial patterns and seasonal dynamics were derived to study the multiple factors’ impact on terrestrially derived CDOM input to the rivers and lakes, including river discharge, watershed landcover, and temperature. This new satellite approach of CDOM estimation in inland waters provided high accuracy spatial-temporal information for studying land-water carbon cycle and aquatic environment.