Research

19 pages, 14530 KiB

Open AccessArticle

Spatiotemporal Protein Variations Based on VIIRS-Derived Regional Protein Algorithm in the Northern East China Sea

by Myeongseop Kim, Sungjun Kim, Dabin Lee, Hyo-Keun Jang, Sanghoon Park, Yejin Kim, Jaesoon Kim, Seok-Hyun Youn, Huitae Joo, Seunghyun Son and Sang-Heon Lee

Remote Sens. 2024, 16(5), 829; https://doi.org/10.3390/rs16050829 - 28 Feb 2024

Viewed by 509

Abstract

Over the past two decades, the environmental characteristics of the northern East China Sea (NECS) that make it a crucial spawning ground for commercially significant species have faced substantial impacts due to climate change. Protein (PRT) within phytoplankton, serving as a nitrogen-rich food [...] Read more.

Over the past two decades, the environmental characteristics of the northern East China Sea (NECS) that make it a crucial spawning ground for commercially significant species have faced substantial impacts due to climate change. Protein (PRT) within phytoplankton, serving as a nitrogen-rich food for organisms of higher trophic levels, is a sensitive indicator to environmental shifts. This study aims to develop a regional PRT algorithm to characterize spatial and temporal variations in the NECS from 2012 to 2022. Employing switching chlorophyll-a and particulate organic nitrogen algorithms, the developed regional PRT algorithm demonstrates enhanced accuracy. Satellite-estimated PRT concentrations, utilizing data from the Visible Infrared Imaging Radiometer Suite (VIIRS), generally align with the 1:1 line when compared to in situ data. Seasonal patterns and spatial distributions of PRT in both the western and eastern parts of the NECS from 2012 to 2022 were discerned, revealing notable differences in the spatial distribution and major controlling factors between these two areas. In conclusion, the regional PRT algorithm significantly improves estimation precision, advancing our understanding of PRT dynamics in the NECS concerning PRT concentration and environmental changes. This research underscores the importance of tailored algorithms in elucidating the intricate relationships between environmental variables and PRT variations in the NECS. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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38 pages, 11952 KiB

Open AccessEditor’s ChoiceArticle

NOAA MODIS SST Reanalysis Version 1

by Olafur Jonasson, Alexander Ignatov, Boris Petrenko, Victor Pryamitsyn and Yury Kihai

Remote Sens. 2023, 15(23), 5589; https://doi.org/10.3390/rs15235589 - 30 Nov 2023

Viewed by 794

Abstract

The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2000–present) and Aqua (4 July 2002–present) was performed. The dataset was produced using the NOAA Advanced Clear-Sky Processor for [...] Read more.

The first NOAA full-mission reanalysis (RAN1) of the sea surface temperature (SST) from the two Moderate Resolution Imaging Spectroradiometers (MODIS) onboard Terra (24 February 2000–present) and Aqua (4 July 2002–present) was performed. The dataset was produced using the NOAA Advanced Clear-Sky Processor for Ocean (ACSPO) enterprise SST system from Collection 6.1 brightness temperatures (BTs) in three MODIS thermal emissive bands centered at 3.7, 11, and 12 µm with a spatial resolution of 1 km at nadir. In the initial stages of reprocessing, several instabilities in the MODIS SST time series were observed. In particular, Terra SSTs and corresponding BTs showed three ‘steps’: two on 30 October 2000 and 2 July 2001 (due to changes in the MODIS operating mode) and one on 25 April 2020 (due to a change in its nominal blackbody temperature, BBT, from 290 to 285 K). Additionally, spikes up to several tenths of a kelvin were observed during the quarterly warm-up/cool-down (WUCD) exercises, when the Terra MODIS BBT was varied. Systematic gradual drifts of ~0.025 K/decade were also seen in both Aqua and Terra SSTs over their full missions due to drifting BTs. These calibration instabilities were mitigated by debiasing MODIS BTs using the time series of observed minus modeled (‘O-M’) BTs. The RAN1 dataset was evaluated via comparisons with various in situ SSTs. The data meet the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), often by a wide margin, in a clear-sky ocean domain of 19–21%. The long-term SST drift is typically less than 0.01 K/decade for all MODIS SSTs, except for the daytime ‘subskin’ SST, for which the drift is ~0.02 K/decade. The MODIS RAN1 dataset is archived at NOAA CoastWatch and updated monthly in a delayed mode with a latency of two months. Additional archival with NASA JPL PO.DAAC is being discussed. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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34 pages, 10192 KiB

Open AccessArticle

VIIRS Edition 1 Cloud Properties for CERES, Part 2: Evaluation with CALIPSO

by Christopher R. Yost, Patrick Minnis, Sunny Sun-Mack, William L. Smith, Jr. and Qing Z. Trepte

Remote Sens. 2023, 15(5), 1349; https://doi.org/10.3390/rs15051349 - 28 Feb 2023

Cited by 1 | Viewed by 1755

Abstract

The decades-long Clouds and Earth’s Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the [...] Read more.

The decades-long Clouds and Earth’s Radiant Energy System (CERES) Project includes both cloud and radiation measurements from instruments on the Aqua, Terra, and Suomi National Polar-orbiting Partnership (SNPP) satellites. To build a reliable long-term climate data record, it is important to determine the accuracies of the parameters retrieved from the sensors on each satellite. Cloud amount, phase, and top height derived from radiances taken by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the SNPP are evaluated relative to the same quantities determined from measurements by the Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) on the Cloud Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft. The accuracies of the VIIRS cloud fractions are found to be as good as or better than those for the CERES amounts determined from Aqua MODerate-resolution Imaging Spectroradiometer (MODIS) data and for cloud fractions estimated by two other operational algorithms. Sensitivities of cloud fraction bias to CALIOP resolution, matching time window, and viewing zenith angle are examined. VIIRS cloud phase biases are slightly greater than their CERES MODIS counterparts. A majority of cloud phase errors are due to multilayer clouds during the daytime and supercooled liquid water clouds at night. CERES VIIRS cloud-top height biases are similar to those from CERES MODIS, except for ice clouds, which are smaller than those from CERES MODIS. CERES VIIRS cloud phase and top height uncertainties overall are very similar to or better than several operational algorithms, but fail to match the accuracies of experimental machine learning techniques. The greatest errors occur for multilayered clouds and clouds with phase misclassification. Cloud top heights can be improved by relaxing tropopause constraints, improving lapse-rate to model temperature profiles, and accounting for multilayer clouds. Other suggestions for improving the retrievals are also discussed. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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33 pages, 16332 KiB

Open AccessArticle

VIIRS Edition 1 Cloud Properties for CERES, Part 1: Algorithm Adjustments and Results

by Patrick Minnis, Sunny Sun-Mack, William L. Smith, Jr., Qing Z. Trepte, Gang Hong, Yan Chen, Christopher R. Yost, Fu-Lung Chang, Rita A. Smith, Patrick W. Heck and Ping Yang

Remote Sens. 2023, 15(3), 578; https://doi.org/10.3390/rs15030578 - 18 Jan 2023

Cited by 1 | Viewed by 2002

Abstract

Cloud properties are essential for the Clouds and the Earth’s Radiant Energy System (CERES) Project, enabling accurate interpretation of measured broadband radiances, providing a means to understand global cloud-radiation interactions, and constituting an important climate record. Producing consistent cloud retrievals across multiple platforms [...] Read more.

Cloud properties are essential for the Clouds and the Earth’s Radiant Energy System (CERES) Project, enabling accurate interpretation of measured broadband radiances, providing a means to understand global cloud-radiation interactions, and constituting an important climate record. Producing consistent cloud retrievals across multiple platforms is critical for generating a multidecadal cloud and radiation record. Techniques used by CERES for retrievals from measurements by the MODerate-Resolution Imaging Spectroradiometer (MODIS) on Terra and Aqua platforms are adapted for the application to radiances from the Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership to continue the CERES record beyond the MODIS era. The algorithm adjustments account for spectral and channel differences, use revised reflectance models, and set new thresholds for detecting thin cirrus clouds at night. Cloud amounts from VIIRS are less than their MODIS counterparts by 0.016 during the day and 0.026 at night, but trend consistently over the 2012–2020 period. The VIIRS mean liquid water cloud fraction differs by ~0.01 from the MODIS amount. The average cloud heights from VIIRS differ from the MODIS heights by less than 0.2 km, except the VIIRS daytime ice cloud heights, which are 0.4 km higher. The mean VIIRS nonpolar optical depths are 17% (1%) larger (smaller) than those from MODIS for liquid (ice) clouds. The VIIRS cloud hydrometeor sizes are generally smaller than their MODIS counterparts. Discrepancies between the MODIS and VIIRS properties stem from spectral and spatial resolution differences, new tests at night, calibration inconsistencies, and new reflectance models. Many of those differences will be addressed in future editions. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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23 pages, 14269 KiB

Open AccessArticle

A Framework for Satellite-Based 3D Cloud Data: An Overview of the VIIRS Cloud Base Height Retrieval and User Engagement for Aviation Applications

by Yoo-Jeong Noh, John M. Haynes, Steven D. Miller, Curtis J. Seaman, Andrew K. Heidinger, Jeffrey Weinrich, Mark S. Kulie, Mattie Niznik and Brandon J. Daub

Remote Sens. 2022, 14(21), 5524; https://doi.org/10.3390/rs14215524 - 02 Nov 2022

Cited by 7 | Viewed by 2677

Abstract

Satellites have provided decades of valuable cloud observations, but the data from conventional passive radiometers are biased toward information from at or near cloud top. Tied with the Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Calibration/Validation research, we [...] Read more.

Satellites have provided decades of valuable cloud observations, but the data from conventional passive radiometers are biased toward information from at or near cloud top. Tied with the Joint Polar Satellite System (JPSS) Visible Infrared Imaging Radiometer Suite (VIIRS) Cloud Calibration/Validation research, we developed a statistical Cloud Base Height (CBH) algorithm using the National Aeronautics and Space Administration (NASA) A-Train satellite data. This retrieval, which is currently part of the National Oceanic and Atmospheric Administration (NOAA) Enterprise Cloud Algorithms, provides key information needed to display clouds in a manner that goes beyond the typical top-down plan view. The goal of this study is to provide users with high-quality three-dimensional (3D) cloud structure information which can maximize the benefits and performance of JPSS cloud products. In support of the JPSS Proving Ground Aviation Initiative, we introduced Cloud Vertical Cross-sections (CVCs) along flight routes over Alaska where satellite data are extremely helpful in filling significant observational gaps. Valuable feedback and insights from interactions with aviation users allowed us to explore a new approach to provide satellite-based 3D cloud data. The CVC is obtained from multiple cloud retrieval products with supplementary data such as temperatures, Pilot Reports (PIREPs), and terrain information. We continue to improve the product demonstrations based on user feedback, extending the domain to the contiguous United States with the addition of the Geostationary Operational Environmental Satellite (GOES)-16 Advanced Baseline Imager (ABI). Concurrently, we have refined the underlying science algorithms for improved nighttime and multilayered cloud retrievals by utilizing Day/Night Band (DNB) data and exploring machine learning approaches. The products are evaluated using multiple satellite data sources and surface measurements. This paper presents our accomplishments and continuing efforts in both scientific and user-engagement improvements since the beginning of the VIIRS era. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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15 pages, 14691 KiB

Open AccessArticle

Ten Years of VIIRS EDR Imagery Validation and User Interactions

by Donald Hillger, William E. Line, Curtis Seaman, Steven D. Miller, Steve Finley and Thomas J. Kopp

Remote Sens. 2022, 14(17), 4167; https://doi.org/10.3390/rs14174167 - 25 Aug 2022

Cited by 4 | Viewed by 1559

Abstract

Over ten years of Visible Infrared Imaging Radiometer Suite (VIIRS) Environmental Data Records (EDR) Imagery Team activities have included primarily imagery validation, but also product generation and display and user interactions. VIIRS imagery validation starts with pre-launch preparations leading up to producing first-light [...] Read more.

Over ten years of Visible Infrared Imaging Radiometer Suite (VIIRS) Environmental Data Records (EDR) Imagery Team activities have included primarily imagery validation, but also product generation and display and user interactions. VIIRS imagery validation starts with pre-launch preparations leading up to producing first-light imagery shortly after the launch of each Joint Polar Satellite System (JPSS) satellite. Imagery quality is scrutinized for typical imagery visualization problems, as well as the overall ability to utilize VIIRS imagery for analysis and forecasting purposes. Then, long-term monitoring of imagery continues through the lifetime of each VIIRS instrument. The VIIRS EDR Imagery Team has undertaken four major ground system code changes. The first of these code changes was needed in 2013 when Near Constant Contrast (NCC) Imagery at night was not routinely being generated from the Day-Night Band (DNB) due to incorrect sensitivity limits. The second applied Terrain Correction to the VIIRS EDR Imagery in 2020. The third, in 2021, was needed to fix an imagery banding anomaly in the NCC, which was masked for years by the natural variability of most NCC Imagery. The fourth was the increase from 6 M-band EDRs to all 16 M-band EDRs in 2021, allowing for the display of true-color and other multi-band imagery products from VIIRS Imagery EDRs. Here, we summarize the efforts of the VIIRS EDR Imagery Team which have resulted in a valuable suite of quality-controlled imagery products for the user community. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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21 pages, 8608 KiB

Open AccessArticle

Evaluation of 10-Year NOAA/NASA Suomi NPP and NOAA-20 VIIRS Reflective Solar Band (RSB) Sensor Data Records (SDR) over Deep Convective Clouds

by Wenhui Wang, Changyong Cao, Xi Shao, Slawomir Blonski, Taeyoung Choi, Sirish Uprety, Bin Zhang and Yan Bai

Remote Sens. 2022, 14(15), 3566; https://doi.org/10.3390/rs14153566 - 25 Jul 2022

Cited by 4 | Viewed by 1591

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key instrument onboard the Suomi NPP (S-NPP) and the NOAA-20 satellites that provides state-of-the-art Earth observations for ocean, land, aerosol, and cloud applications. VIIRS Reflective Solar Band (RSB) Sensor Data Records (SDR, or Level [...] Read more.

The Visible Infrared Imaging Radiometer Suite (VIIRS) is a key instrument onboard the Suomi NPP (S-NPP) and the NOAA-20 satellites that provides state-of-the-art Earth observations for ocean, land, aerosol, and cloud applications. VIIRS Reflective Solar Band (RSB) Sensor Data Records (SDR, or Level 1b products) are calibrated and produced independently by The National Oceanic and Atmospheric Administration (NOAA) and the National Aeronautics and Space Administration (NASA) VIIRS science teams. Multiple versions of S-NPP and NOAA-20 VIIRS SDRs are available to date. This study evaluates the long-term calibration stability, biases, and inter-channel consistency of S-NPP and NOAA-20 VIIRS SDRs generated by NOAA and NASA over Deep Convective Clouds (DCC) to support downstream applications, especially climate data record studies. Five VIIRS RSB SDRs were analyzed in this study: (1) NOAA version 2 S-NPP VIIRS reprocessed SDRs (NOAA-NPP-V2, 2012–2020), (2) NASA Collection 1 S-NPP VIIRS SDRs (NASA-NPP-C1, 2012–2021), (3) NASA Collection 2 S-NPP VIIRS SDRs (NASA-NPP-C2, 2012–2021), (4) NOAA constant F-factor calibrated NOAA-20 VIIRS SDRs (NOAA-N20-ConstF, 2018–2021), and (5) NASA Collection 2 NOAA-20 VIIRS SDRs (NASA-N20-C2, 2018–2021). The DCC time series analysis results indicate that the calibrations of the three S-NPP VIIRS RSB SDRs are generally stable, with trends within ±0.1%/year for all RSBs, except for M3–M4 (all three S-NPP SDRs) and I3 (NASA-NPP-C1 only). The calibration of NASA-NPP-C2 SDRs is more uniform at individual detector levels. NOAA-NPP-V2 and NASA-NPP-C1 SDRs exhibit non-negligible time-dependent detector level degradation in M1–M4 (up to 1.5% in 2020–2021), causing striping in the SDR imagery. The biases between NOAA and NASA S-NPP VIIRS RSB SDRs are from 0.1% to 2.4%. The calibrations of the two NOAA-20 VIIRS RSB SDRs are also generally stable, with trends within ±0.16%/year. Small downward trends were observed in the visible and near-infrared (VIS/NIR) bands, and small upward trends were observed in the shortwave infrared (SWIR) bands for both NOAA and NASA NOAA-20 SDRs. The biases between NOAA and NASA NOAA-20 VIIRS RSB SDRs are nearly constant over time and within ±0.2% for VIS/NIR bands and ±0.7% for SWIR bands. There exists large inter-satellite biases between S-NPP and NOAA-20 VIIRS SDRs, especially in the VIS/NIR bands (up to 4.5% for NOAA SDRs and up to 7% for NASA SDRs). In addition, the DCC reflectance of S-NPP VIIRS RSB spectral bands is more consistent with that of the SCanning Imaging Absorption SpectroMeter for Atmospheric CHartographY (SCIAMACHY) than that of NOAA-20. Bands M4 and M9 seem out of family in all five S-NPP and NOAA-20 RSB SDRs evaluated. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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26 pages, 7602 KiB

Open AccessEditor’s ChoiceArticle

JPSS VIIRS SST Reanalysis Version 3

by Olafur Jonasson, Alexander Ignatov, Victor Pryamitsyn, Boris Petrenko and Yury Kihai

Remote Sens. 2022, 14(14), 3476; https://doi.org/10.3390/rs14143476 - 20 Jul 2022

Cited by 5 | Viewed by 2925

Abstract

The 3rd full-mission reanalysis (RAN3) of global sea surface temperature (SST) with a 750 m resolution at nadir is available from VIIRS instruments flown onboard two JPSS satellites: NPP (February 2012–present) and N20 (January 2018–present). Two SSTs, ‘subskin’ (sensitive to skin SST) and [...] Read more.

The 3rd full-mission reanalysis (RAN3) of global sea surface temperature (SST) with a 750 m resolution at nadir is available from VIIRS instruments flown onboard two JPSS satellites: NPP (February 2012–present) and N20 (January 2018–present). Two SSTs, ‘subskin’ (sensitive to skin SST) and ‘depth’ (proxy for in situ SST at depth of 20 cm), were produced from brightness temperatures (BTs) in the VIIRS bands centered at 8.6, 11 and 12 µm during the daytime and an additional 3.7 µm band at night, using the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. The RAN3 dataset is fully archived at NASA JPL PO.DAAC and NOAA CoastWatch, and routinely supplemented in near real time (NRT) with a latency of a few hours. Delayed mode (DM) processing with a 2 months latency follows NRT, resulting in a more uniform science quality SST record. This paper documents and evaluates the performance of the VIIRS RAN3 dataset. Comparisons with in situ SSTs from drifters and tropical moorings (D+TM) as well as Argo floats (AFs) (both available from the NOAA iQuam system) show good agreement, generally within the NOAA specifications for accuracy (±0.2 K) and precision (0.6 K), in a clear-sky domain covering 18–20% of the global ocean. The nighttime SSTs compare with in situ data more closely, as expected due to the reduced diurnal thermocline. The daytime SSTs are also generally within NOAA specs but show some differences between the (D+TM) and AF validations as well as residual drift on the order of −0.1 K/decade. BT comparisons between two VIIRSs and MODIS-Aqua show good consistency in the 3.7 and 12 µm bands. The 11 µm band, while consistent between NPP and N20, shows residual drift with respect to MODIS-Aqua. Similar analyses of the 8.6 µm band are inconclusive, as the performance of the MODIS band 29 centered at 8.6 µm is degraded and unstable in time and cannot be used for comparisons. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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23 pages, 10073 KiB

Open AccessArticle

S-NPP VIIRS Lunar Calibrations over 10 Years in Reflective Solar Bands (RSB)

by Taeyoung Choi, Changyong Cao, Xi Shao and Wenhui Wang

Remote Sens. 2022, 14(14), 3367; https://doi.org/10.3390/rs14143367 - 13 Jul 2022

Cited by 6 | Viewed by 1736

Abstract

Since 28 October 2011, the VIIRS Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (S-NPP) has operated over 10 years and successfully generated scientific global images for the Earth’s environment and climate studies. Besides thermal and day night bands, VIIRS [...] Read more.

Since 28 October 2011, the VIIRS Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (S-NPP) has operated over 10 years and successfully generated scientific global images for the Earth’s environment and climate studies. Besides thermal and day night bands, VIIRS has 14 reflective solar bands (RSBs) that cover a spectral range of 0.41 µm to 2.25 µm. The primary and daily source of calibration for the RSBs is the Solar Diffuser (SD) as an onboard calibrator, and its degradations are tracked by the Solar Diffuser Stability Monitor (SDSM). Alternatively, monthly scheduled lunar calibration has provided long-term on-orbit trends that validate the corresponding SD-based calibration results. In this paper, on-orbit lunar calibration and comparison results are focused on, in conjunction with the SD calibrations that are performed by the National Oceanic and Atmospheric Administration (NOAA) VIIRS team. In addition, a recent study showed that there is increasing striping in the VIIRS images in the RSBs caused by the non-uniform SD degradation. The estimation of the SD non-uniformity and a mitigation method is proposed along with the striping reductions. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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23 pages, 14191 KiB

Open AccessFeature PaperArticle

AVHRR GAC Sea Surface Temperature Reanalysis Version 2

by Boris Petrenko, Victor Pryamitsyn, Alexander Ignatov, Olafur Jonasson and Yury Kihai

Remote Sens. 2022, 14(13), 3165; https://doi.org/10.3390/rs14133165 - 01 Jul 2022

Cited by 3 | Viewed by 1562

Abstract

The 40+ years-long sea surface temperature (SST) dataset from 4 km Global Area Coverage (GAC) data of the Advanced Very High-Resolution Radiometers (AVHRR/2s and/3s) flown onboard ten NOAA satellites (N07/09/11/12/14/15/16/17/18/19) has been created under the NOAA AVHRR GAC SST Reanalysis 2 (RAN2) Project. [...] Read more.

The 40+ years-long sea surface temperature (SST) dataset from 4 km Global Area Coverage (GAC) data of the Advanced Very High-Resolution Radiometers (AVHRR/2s and/3s) flown onboard ten NOAA satellites (N07/09/11/12/14/15/16/17/18/19) has been created under the NOAA AVHRR GAC SST Reanalysis 2 (RAN2) Project. The data were reprocessed with the NOAA Advanced Clear Sky Processor for Ocean (ACSPO) enterprise SST system. Two SST products are reported in the full ~3000 km AVHRR swath: ‘subskin’ (highly sensitive to true skin SST, but debiased with respect to in situ SST) and ‘depth’ (a closer proxy for in situ data, but with reduced sensitivity). The reprocessing methodology aims at close consistency of satellite SSTs with in situ SSTs, in an optimal retrieval domain. Long-term orbital and calibration trends were compensated by daily recalculation of regression coefficients using matchups with drifters and tropical moored buoys (supplemented by ships for N07/09), collected within limited time windows centered at the processed day. The nighttime Sun impingements on the sensor black body were mitigated by correcting the L1b calibration coefficients. The Earth view pixels contaminated with a stray light were excluded. Massive cold SST outliers caused by volcanic aerosols following three major eruptions were filtered out by a modified, more conservative ACSPO clear-sky mask. The RAN2 SSTs are available in three formats: swath L2P (144 10-min granules per 24 h interval) and two 0.02° gridded (uncollated L3U, also 144 granules/24 h; and collated L3C, two global maps per 24 h, one for day and one for the night). This paper evaluates the RAN2 SST dataset, with a focus on the L3C product and compares it with two other available AVHRR GAC L3C SST datasets, NOAA Pathfinder v5.3 and ESA Climate Change Initiative v2.1. Among the three datasets, the RAN2 covers the global ocean more completely and shows reduced regional and temporal biases, improved stability and consistency between different satellites, and in situ SSTs. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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21 pages, 3848 KiB

Open AccessArticle

Ten Years of VIIRS Land Surface Temperature Product Validation

by Yuling Liu, Peng Yu, Heshun Wang, Jingjing Peng and Yunyue Yu

Remote Sens. 2022, 14(12), 2863; https://doi.org/10.3390/rs14122863 - 15 Jun 2022

Cited by 2 | Viewed by 1815

Abstract

The Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature (LST) has been operationally produced for a decade since the Suomi National Polar-orbiting Partnership (SNPP) launched in October 2011. A comprehensive evaluation of its accuracy and precision will be helpful for product users [...] Read more.

The Visible Infrared Imaging Radiometer Suite (VIIRS) Land Surface Temperature (LST) has been operationally produced for a decade since the Suomi National Polar-orbiting Partnership (SNPP) launched in October 2011. A comprehensive evaluation of its accuracy and precision will be helpful for product users in climate studies and atmospheric models. In this study, the VIIRS LST is validated with ground observations from multiple high-quality radiation networks, including six stations from the Surface Radiation budget (SURFRAD) network, two stations from the Baseline Surface Radiation Network (BSRN), and 13 stations from the Atmospheric Radiation Measurement (ARM) network, to evaluate its performance over various land-cover types. The VNP21A1 LST was validated against the same ground observations as a reference. The results yield a close agreement between the SNPP VIIRS LST and ground LSTs with a bias of −0.4 K and a RMSE of 1.96 K over six SURFRAD sites; a bias of −0.2 K and a RMSE of 1.93 K over two BSRN sites; and a bias of −0.1 K and a RMSE of 1.7 K over the 13 ARM sites. The time series of the LST errors over individual sites indicate seasonal cycles. The data anomaly over the BSRN site in Cabauw and the SURFRAD site in Desert Rock is revealed and discussed in this study. In addition, a method using Landsat-8 data is applied to quantify the heterogeneity level of each ground station and the results provide promising insights. The validation results demonstrate the maturity of the JPSS VIIRS LST products and their readiness for various application studies. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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23 pages, 41559 KiB

Open AccessArticle

Assessment of VIIRS on the Identification of Harmful Algal Bloom Types in the Coasts of the East China Sea

by Changpeng Li, Bangyi Tao, Yalin Liu, Shugang Zhang, Zhao Zhang, Qingjun Song, Zhibing Jiang, Shuangyan He, Haiqing Huang and Zhihua Mao

Remote Sens. 2022, 14(9), 2089; https://doi.org/10.3390/rs14092089 - 27 Apr 2022

Cited by 3 | Viewed by 1890

Abstract

Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics [...] Read more.

Visible Infrared Imaging Radiometer Suite (VIIRS) data were systematically evaluated and used to detect harmful algal bloom (HAB) and classify algal bloom types in coasts of the East China Sea covered by optically complex and sediment-rich waters. First, the accuracy and spectral characteristics of VIIRS retrieved normalized water-leaving radiance or the equivalent remote sensing reflectance from September 2019 to October 2020 that were validated by the long-term observation data acquired from an offshore platform and underway measurements from a cruise in the Changjiang Estuary and adjacent East China Sea. These data were evaluated by comparing them with data from the Moderate-Resolution Imaging Spectroradiometer. The bands of 486, 551, and 671 nm provided much higher quality than those of 410 and 443 nm and were more suitable for HAB detection. Secondly, the performance of four HAB detection algorithms were compared. The Ratio of Algal Bloom (RAB) algorithm is probably more suitable for HAB detection in the study area. Importantly, although RAB was also verified to be applicable for the detection of different kinds of HAB (Prorocentrum donghaiense, diatoms, Ceratium furca, and Akashiwo sanguinea), the capability of VIIRS in the classification of those algal species was limited by the lack of the critical band near 531 nm. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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18 pages, 5772 KiB

Open AccessArticle

Retrieval of Daily Mean VIIRS SST Products in China Seas

by Qianmei Li, Qingyou He and Chuqun Chen

Remote Sens. 2021, 13(24), 5158; https://doi.org/10.3390/rs13245158 - 19 Dec 2021

Cited by 1 | Viewed by 2054

Abstract

Sea surface temperature (SST) is one of the most important factors in regulating air-sea heat flux and, thus, climate change. Most of current global daily SST products are derived from one or two transient measurements of polar-orbiting satellites, which are not the same [...] Read more.

Sea surface temperature (SST) is one of the most important factors in regulating air-sea heat flux and, thus, climate change. Most of current global daily SST products are derived from one or two transient measurements of polar-orbiting satellites, which are not the same to daily mean SST values. In this study, high-temporal-resolution SST measurements (32–40 snapshots per day) from a geostationary satellite, FengYun-4A (FY–4A), are used to analyze the diurnal variation of SST in China seas. The results present a sinusoidal pattern of the diurnal variability in SST, with the maximum value at 13:00–15:00 CST and the minimum at 06:00–08:00 CST. Based on the diurnal variation of SST, a retrieval method for daily mean SST products from polar-orbiting satellites is established and applied to 7716 visible infrared imaging radiometer (VIIRS) data in China seas. The results suggest that it is feasible and practical for the retrieval of daily mean SST with an average RMSE of 0.133 °C. This retrieval method can also be utilized to other polar-orbiting satellites and obtain more daily mean satellite SST products, which will contribute to more accurate estimation and prediction between atmosphere and ocean in the future. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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21 pages, 7554 KiB

Open AccessReview

A Review of the Far-Reaching Usage of Low-Light Nighttime Data

by Cynthia L. Combs and Steven D. Miller

Remote Sens. 2023, 15(3), 623; https://doi.org/10.3390/rs15030623 - 20 Jan 2023

Cited by 2 | Viewed by 1563

Abstract

To assess the current and future utility of low-light satellite data, this paper reviewed 1630 papers, presentations, theses, and dissertations using day/night band (DNB) data from the visible infrared imaging radiometer suite (VIIRS) imager and its precursor, the Defense Meteorological Satellite Program’s Operational [...] Read more.

To assess the current and future utility of low-light satellite data, this paper reviewed 1630 papers, presentations, theses, and dissertations using day/night band (DNB) data from the visible infrared imaging radiometer suite (VIIRS) imager and its precursor, the Defense Meteorological Satellite Program’s Operational Linescan system (DMSP-OLS) series from the 1970s through to the year 2021. By the way of a categorical system, we take inventory of the myriad applications of these data to a wide variety of disciplines, ranging from social to natural science, oceans to atmosphere, and biology to civil engineering. Papers from social science fields dominate this spectrum, pointing to the unique aspect of low-light observations in their ability to observe aspects of human civilization at night. We also look at the stratification between applications using natural vs. artificial light, the use of moonlight, and the context of the key earth climate system elements. In light of these findings, a discussion is provided for the future of low-light measurements. Since the start of the VIIRS series, there has been a rapid increase in interest in the use of these data for numerous fields, pointing towards a nascent field centered on the nocturnal earth system science that is enabled by these novel and newly quantifiable measurements. This study is of significant importance in evaluating current uses of low-light data and possible architecture solutions for next-generation satellites. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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13 pages, 5960 KiB

Open AccessTechnical Note

VIIRS after 10 Years—A Perspective on Benefits to Forecasters and End-Users

by Matthew A. Rogers, Steven D. Miller, Curtis J. Seaman, Jorel Torres, Donald Hillger, Ed Szoke and William E. Line

Remote Sens. 2023, 15(4), 976; https://doi.org/10.3390/rs15040976 - 10 Feb 2023

Cited by 1 | Viewed by 1354

Abstract

In the ten years of VIIRS observations, a wide range of applications, both operational and research-based, have been developed, observed, and utilized at the Cooperative Institute for Research in the Atmosphere (CIRA). Training efforts to improve operational forecast use and achieve a greater [...] Read more.

In the ten years of VIIRS observations, a wide range of applications, both operational and research-based, have been developed, observed, and utilized at the Cooperative Institute for Research in the Atmosphere (CIRA). Training efforts to improve operational forecast use and achieve a greater understanding of the unique capabilities of the VIIRS have also been developed to better utilize the new observations made possible. Several unique forecast events, made observable using these novel capabilities of the VIIRS, are detailed. A summary of third-party end-user case studies where VIIRS observations are used for analysis of significant socioeconomic gain, part of a recent CIRA workshop, are also highlighted. Thoughts on the future utility of the VIIRS and VIIRS-like platforms are offered. Full article

(This article belongs to the Special Issue VIIRS 2011–2021: Ten Years of Success in Earth Observations)

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