Earth reflectance image striping mitigation for NOAA-20 VIIRS visible and near-infrared bands

The Visible Infrared Imaging Radiometer Suite (VIIRS) is an Earth-observing satellite sensor. VIIRS data are used to generate about 30 products (see Ref. 3) for Earth studies and weather forecasting. Out of the 22 VIIRS spectral bands, 14 are the reflective solar bands (RSBs). Earth views from some of these RSBs onboard the NOAA-20 (N20) satellite show unexpected striping. Our investigation shows that the striping for the N20 VIIRS visible and near-infrared (VisNIR) bands comes from three sources: first, for bands M1 (412 nm) and M2 (445 nm), an obvious dependence on the sides of the half-angle-mirror (HAM), a result of a likely error in the HAM’s reflectivity extrapolated to the telescope solar diffuser (SD) view angle; second, a gradual increase in the striping over time for band M1, resulting from not accounting for the positional dependence of the SD reflectance; and finally, a small (~0.3%) but universal amount of time invariant striping for all the VisNIR bands, possibly coming from a small amount of positional dependence in the prelaunch measured SD reflectance. Here, we apply appropriate methods to resolve the unwanted striping for the N20 VIIRS VisNIR bands. The de-striping algorithms have been applied in the NASA N20 VIIRS Collection 2.1 L1B products, with the first delivery of the forward L1B F-factor LUTs (version v3.1.1.7) in November 2023.

Authors

Publication Details

Published
2024-08-16
DOI
https://doi.org/10.1117/12.3027686
Citations
2
Primary Topic
Calibration and Measurement Techniques
Type
article
Field-Weighted Citation Impact
2.60
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Earth reflectance image striping mitigation for NOAA-20 VIIRS visible and near-infrared bands

Qiaozhen Mu, Truman Wilson, Ning Lei, Kevin A. Twedt et al.
2 citations
Calibration and Measurement Techniques
2.60
article

Earth reflectance image striping mitigation for NOAA-20 VIIRS visible and near-infrared bands

Qiaozhen Mu, Truman Wilson, Ning Lei, Kevin A. Twedt, Sherry C. Li, Xiaoxiong Xiong, Xu Geng
article en
2 citations

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) is an Earth-observing satellite sensor. VIIRS data are used to generate about 30 products (see Ref. 3) for Earth studies and weather forecasting. Out of the 22 VIIRS spectral bands, 14 are the reflective solar bands (RSBs). Earth views from some of these RSBs onboard the NOAA-20 (N20) satellite show unexpected striping. Our investigation shows that the striping for the N20 VIIRS visible and near-infrared (VisNIR) bands comes from three sources: first, for bands M1 (412 nm) and M2 (445 nm), an obvious dependence on the sides of the half-angle-mirror (HAM), a result of a likely error in the HAM’s reflectivity extrapolated to the telescope solar diffuser (SD) view angle; second, a gradual increase in the striping over time for band M1, resulting from not accounting for the positional dependence of the SD reflectance; and finally, a small (~0.3%) but universal amount of time invariant striping for all the VisNIR bands, possibly coming from a small amount of positional dependence in the prelaunch measured SD reflectance. Here, we apply appropriate methods to resolve the unwanted striping for the N20 VIIRS VisNIR bands. The de-striping algorithms have been applied in the NASA N20 VIIRS Collection 2.1 L1B products, with the first delivery of the forward L1B F-factor LUTs (version v3.1.1.7) in November 2023.

Climate action
Openalex Percentile: Top 9%
Calibration and Measurement Techniques
2.60
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.