Applying Deep Learning to Clear-Sky Radiance Simulation for VIIRS with Community Radiative Transfer Model—Part 1: Develop AI-Based Clear-Sky Mask
نویسندگان
چکیده
A fully connected deep neural network (FCDN) clear-sky mask (CSM) algorithm (FCDN_CSM) was developed to assist the FCDN-based Community Radiative Transfer Model (FCDN_CRTM) reproduce Visible Infrared Imaging Radiometer Suite (VIIRS) radiances in five thermal emission M (TEB/M) bands. The model design referenced and enhanced from its earlier version (version 1), trained tested global ocean domain using six dispersion days’ data 2019 2020 as inputs a modified NOAA Advanced Clear-Sky Processor over Ocean (ACSPO) CSM product reference labels. improved FCDN_CSM 2) further by including daytime data, which not collected 1. then employed predict VIIRS multiple days an accuracy stability check. results were validated against biases between sensor observations CRTM calculations (O-M). objectives (1) enhance performance include analysis, improve stability, accuracy, efficiency; (2) understand based on combination of statistics physical interpretation. According analyses F-score, prediction result showed ~96% ~97% for day night, respectively. type Cloud most accurate, followed Clear-Sky. O-M mean are comparable ACSPO all bands, both night. standard deviations (STD) slightly degraded long wave IRs (M14, M15, M16), mainly due contamination 3% misclassification Cloud, may require be fine-tuned future. However, consistent means STDs persist throughout period, suggesting that 2 is robust does have significant overfitting. Given high F-scores, spatial long-term efficiency, acceptable STDs, deemed ready use FCDN_CRTM.
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ژورنال
عنوان ژورنال: Remote Sensing
سال: 2021
ISSN: ['2315-4632', '2315-4675']
DOI: https://doi.org/10.3390/rs13020222