Long-Term Evaluation of GCOM-C/SGLI Reflectance and Water Quality Products: Variability Among JAXA G-Portal and JASMES

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(2), P. 221 - 221

Published: Jan. 9, 2025

The Global Change Observation Mission-Climate (GCOM-C) satellite, launched in December 2017, is equipped with the Second-generation Imager (SGLI) sensor, featuring a moderate spatial resolution of 250 m and 19 spectral bands, including unique 380 nm band. After six years orbit, comprehensive evaluation SGLI products their temporal consistency needed. Remote sensing reflectance (Rrs) primary product for monitoring water quality, forming basis deriving key oceanic constituents such as chlorophyll-a (Chla) total suspended matter (TSM). Japan Aerospace Exploration Agency (JAXA) provides Rrs through two platforms, G-Portal JASMES, each employing different atmospheric correction methodologies assumptions. This study aims to evaluate full-resolution from JASMES at regional scales (Japan East Asia) assess globally between January 2018 2023. employs situ matchups NASA’s Aerosol Robotic Network-Ocean Color (AERONET-OC) cruise measurements. We also retrieval accuracy quality indices, Chla TSM. AERONET-OC data analysis reveals that systematically underestimates values shorter wavelengths, particularly 412 nm. While relatively low, G-Portal’s perform better than showing lower errors stronger correlations data. Both show agreement datasets wavelengths but demonstrate improved longer (530 nm, 565 670 nm). generates approximately 12% more matchup points G-Portal, likely due stricter thresholds exclude pixels high reflectance. In measurements indicate overall agreement, magnitudes concentrations below 5 mg/m3. underscores complexities challenges correction, optically complex coastal waters (Case 2 waters), which may require tailored methods standard approach. assessment seasonal variations shows both platforms effectively capture interannual trends maintain stability, 490 band onward, underscoring potential long-term environments.

Language: Английский

3LATNet: Attention based deep learning model for global Chlorophyll-a retrieval from GCOM-C satellite

ISPRS Journal of Photogrammetry and Remote Sensing, Journal Year: 2025, Volume and Issue: 220, P. 490 - 508

Published: Jan. 10, 2025

Language: Английский