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- VIIRS - an overview | ScienceDirect Topics
VIIRS is a key instrument for satellite missions like S-NPP and JPSS, designed to enhance and expand the capabilities of existing research and operational instruments It consists of an electronics module and an optomechanical module, and is used for making observations in various spectral bands to capture data about Earth's surface and atmosphere From: Comprehensive Remote Sensing, 2018
- Evaluation of MODIS, MISR, and VIIRS daily level-3 aerosol optical . . .
This study presents a comprehensive evaluation of eight aerosol optical depth (AOD) products from the latest MODIS C6 1 DT, DB and DTB, MISR V23, and …
- Global evaluation of NOAA-20 VIIRS dark target aerosol products over . . .
NOAA-20 VIIRS aerosol products are introduced in the newly released VIIRS DT V2 0 dataset for the first time This study provides a comprehensive validation of NOAA-20 DT AOD (over land and ocean) and AE (over ocean) products for the period 2018 to 2022, using measurements from 605 AERONET sites and 133 MAN cruises
- Validation, inter-comparison, and usage recommendation of six latest . . .
Validation, inter-comparison, and usage recommendation of six latest VIIRS and MODIS aerosol products over the ocean and land on the global and regional scales
- Quantitative characterization of global nighttime light: A method for . . .
Quantitative characterization of global nighttime light: A method for measuring energy intensity based on radiant flux and SNPP-VIIRS data
- Retrieval uncertainty and consistency of Suomi-NPP VIIRS Deep Blue and . . .
Retrieval accuracy and stability of two operational aerosol retrieval algorithms, Deep Blue (DB) and Dark Target (DT), applied on Visible Infrared Ima…
- A decade-long chlorophyll-a data record in lakes across China from . . .
The algorithm was applied to VIIRS images to produce a data record of spatial and temporal variations in Chl-a for China's large lakes over the past decade The VIIRS-derived data record showed that China's lakes have an average Chl-a of 9 5 μg L −1 and are to 45 5% eutrophic
- Seamless observations of chlorophyll-a from OLCI and VIIRS measurements . . .
The deep neural network algorithm outperformed several state-of-the-art algorithms in Chl - a estimates from OLCI images (23 % bias) The spatial and temporal patterns of OLCI and VIIRS-derived Chl-a presented an excellent consistency with ∼20 % difference, suggesting the feasibility of seamless OLCI-VIIRS observations in Chl-a for lakes
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