scholarly journals Cross-Sensor Nighttime Lights Image Calibration for DMSP/OLS and SNPP/VIIRS with Residual U-Net

2021 ◽  
Vol 13 (24) ◽  
pp. 5026
Author(s):  
Dmitry Nechaev ◽  
Mikhail Zhizhin ◽  
Alexey Poyda ◽  
Tilottama Ghosh ◽  
Feng-Chi Hsu ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Dynamic Range ◽  
Satellite System ◽  
Sensor Calibration ◽  
Environmental Light ◽  
Nighttime Lights ◽  
Military Conflicts ◽  
Highly Correlated ◽  
Polar Satellite

Remote sensing of nighttime lights (NTL) is widely used in socio-economic studies of economic growth, urbanization, stability of power grid, environmental light pollution, pandemics and military conflicts. Currently, NTL data are collected with two sensors: (1) Operational Line-scan System (OLS) onboard the satellites from the Defense Meteorology Satellite Program (DMSP) and (2) Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi NPP (SNPP) and NOAA-20 satellites from the Joint Polar Satellite System (JPSS). However, the nighttime images acquired by these two sensors are incompatible in spatial resolution and dynamic range. To address this problem, we propose a method for the cross-sensor calibration with residual U-net convolutional neural network (CNN). The CNN produces DMSP-like NTL composites from the VIIRS annual NTL composites. The pixel radiances predicted from VIIRS are highly correlated with NTL observed with OLS (0.96 < R2 < 0.99). The method can be used to extend long-term series of annual NTL after the end of DMSP mission or to cross-calibrate same year NTL from different satellites to study diurnal variations.

scholarly journals A New 32-Day Average-Difference Method for Calculating Inter-Sensor Calibration Radiometric Biases between SNPP and NOAA-20 Instruments within ICVS Framework

2021 ◽  
Vol 13 (16) ◽  
pp. 3079
Author(s):  
Banghua Yan ◽  
Mitch Goldberg ◽  
Xin Jin ◽  
Ding Liang ◽  
Jingfeng Huang ◽  
...  
Keyword(s):  
Infrared Imaging ◽  
Satellite System ◽  
Advanced Technology ◽  
New Method ◽  
Double Difference ◽  
Sensor Calibration ◽  
Long Term Trends ◽  
Cross Track ◽  
Radiative Transfer Modeling

Two existing double-difference (DD) methods, using either a 3rdSensor or Radiative Transfer Modeling (RTM) as a transfer, are applicable primarily for limited regions and channels, and, thus critical in capturing inter-sensor calibration radiometric bias features. A supplementary method is also desirable for estimating inter-sensor calibration biases at the window and lower sounding channels where the DD methods have non-negligible errors. In this study, using the Suomi National Polar-orbiting Partnership (SNPP) and Joint Polar Satellite System (JPSS)-1 (alias NOAA-20) as an example, we present a new inter-sensor bias statistical method by calculating 32-day averaged differences (32D-AD) of radiometric measurements between the same instrument onboard two satellites. In the new method, a quality control (QC) scheme using one-sigma (for radiance difference), or two-sigma (for radiance) thresholds are established to remove outliers that are significantly affected by diurnal biases within the 32-day temporal coverage. The performance of the method is assessed by applying it to estimate inter-sensor calibration radiometric biases for four instruments onboard SNPP and NOAA-20, i.e., Advanced Technology Microwave Sounder (ATMS), Cross-track Infrared Sounder (CrIS), Nadir Profiler (NP) within the Ozone Mapping and Profiler Suite (OMPS), and Visible Infrared Imaging Radiometer Suite (VIIRS). Our analyses indicate that the globally-averaged inter-sensor differences using the 32D-AD method agree with those using the existing DD methods for available channels, with margins partially due to remaining diurnal errors. In addition, the new method shows its capability in assessing zonal mean features of inter-sensor calibration biases at upper sounding channels. It also detects the solar intrusion anomaly occurring on NOAA-20 OMPS NP at wavelengths below 300 nm over the Northern Hemisphere. Currently, the new method is being operationally adopted to monitor the long-term trends of (globally-averaged) inter-sensor calibration radiometric biases at all channels for the above sensors in the Integrated Calibration/Validation System (ICVS). It is valuable in demonstrating the quality consistencies of the SDR data at the four instruments between SNPP and NOAA-20 in long-term statistics. The methodology is also applicable for other POES cross-sensor calibration bias assessments with minor changes.

scholarly journals Constructing a New Inter-Calibration Method for DMSP-OLS and NPP-VIIRS Nighttime Light

2020 ◽  
Vol 12 (6) ◽  
pp. 937 ◽  
Author(s):  
Jinji Ma ◽  
Jinyu Guo ◽  
Safura Ahmad ◽  
Zhengqiang Li ◽  
Jin Hong
Keyword(s):  
Infrared Imaging ◽  
Dynamic Range ◽  
Calibration Method ◽  
Logistic Function ◽  
Coefficient Of Determination ◽  
Nighttime Light ◽  
Development Assessment ◽  
China Region

The anthropogenic nighttime light (NTL) data that are acquired by satellites can characterize the intensity of human activities on the ground. It has been widely used in urban development assessment, socioeconomic estimate, and other applications. However, currently, the two main sensors, Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) and Suomi National Polar-orbiting Partnership Satellite’s Visible Infrared Imaging Radiometer Suite (NPP-VIIRS), provide inconsistent data. Hence, the application of NTL for long-term analysis is hampered. This study constructed a new inter-calibration method for DMSP-OLS and NPP-VIIRS nighttime light to solve this problem. First, NTL data were processed to obtain vicarious site across China. By comparing different candidate models, it is discovered the Biphasic Dose Response (BiDoseResp) model, which is a weighted combination of sigmoid functions, can best perform the regression between DMSP-OLS and logarithmically transformed NPP-VIIRS. The coefficient of determination of BiDoseResp model reaches 0.967. It’s residual sum of squares is 6.136 × 10 5 , which is less than 6.199 × 10 5 of Logistic function. After obtaining the BiDoseResp-calibrated VIIRS (BDRVIIRS), we smoothed it by a filter with optimal parameters to maximize the consistency. The result shows that the consistency of NTL data is greatly enhanced after calibration. In 2013, the correlation coefficient between DMSP-OLS and original NPP-VIIRS data in the China region is only 0.621, while that reaches to 0.949 after calibration. Finally, a consistent NTL dataset of China from 1992 to 2018 was produced. When compared with the existing methods, our method is applicable to the full dynamic range of DMSP-OLS. Besides, it is more suitable for country or larger scale areas. It is expected that this method can greatly facilitate the development of research that is based on the historical NTL archive.

scholarly journals An Overall Assessment of JPSS-2 VIIRS Radiometric Performance Based on Pre-launch Testing

2018 ◽  
Author(s):  
Hassan Oudrari ◽  
Jeffrey McIntire ◽  
Xiaoxiong Xiong ◽  
James Butler ◽  
Qiang Ji ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Infrared Imaging ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Scattered Light ◽  
Light Response ◽  
Satellite System ◽  
Sensor Data ◽  
Polarization Sensitivity ◽  
Spectral Performance

The Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the second Joint Polar Satellite System (JPSS) completed its sensor level testing in February 2018. The JPSS-2 (J2) mission is scheduled to launch in 2022, and will be very similar to its two predecessor missions, the Suomi National Polar-orbiting Partnership (SNPP) mission, launched on 28 October 2011, and JPSS-1 (renamed NOAA-20) launched on 18 November 2017. VIIRS instrument has 22 spectral bands covering the spectrum between 0.4 and 12.6 mircron: 14 reflective solar bands (RSB), 7 thermal emissive bands (TEB), and one day-night band (DNB). It is a cross-track scanning radiometer capable of providing global measurements through observations at two spatial resolutions, 375 m and 750 m at nadir for the imaging bands and moderate bands, respectively. This paper will provide an overview of J2 VIIRS characterization methodologies and calibration performance during the pre-launch testing phases performed by the National Aeronautics and Space Administration (NASA) VIIRS Characterization Support Team (VCST) to evaluate the at-launch baseline radiometric performance and generate the parameters needed to populate the sensor data record (SDR) Look-Up-Tables (LUTs). Key sensor performance metrics include the signal to noise ratio (SNR), radiance dynamic range, reflective and emissive bands calibration performance, polarization sensitivity, spectral performance, response versus scan-angle (RVS), and scattered light response. A set of performance metrics generated during the pre-launch testing program will be compared to both the SNPP and JPSS-1 VIIRS sensors.

scholarly journals S-NPP VIIRS Day Night Band On-Board Solar Diffuser Calibration Validation Using the Scheduled Lunar Collections

2021 ◽  
Vol 13 (6) ◽  
pp. 1093
Author(s):  
Taeyoung Choi ◽  
Changyong Cao
Keyword(s):  
Infrared Imaging ◽  
Dynamic Range ◽  
Spectral Response ◽  
High Gain ◽  
Radiometric Calibration ◽  
Calibration Source ◽  
Global Space ◽  
Long Term Trends ◽  
Calibration Coefficients

Similar to the Reflective Solar Band (RSB) calibration, Suomi-National Polar-orbiting Partnership (S-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) on-board calibration of Day Night Band (DNB) is based on the Solar Diffuser (SD) observations in the Low Gain State (LGS). DNB has a broad spectral response covering a wavelength range roughly from 500 nm to 900 nm with a large dynamic range from three different gain states called High Gain State (HGS), Mid Gain State (MGS), and LGS. The calibration of MGS and HGS is also dependent on the LGS gain estimation with the gain ratios for each gain state. Over the lifetime of S-NPP VIIRS operations, the LGS gains have been derived from the on-board SD observations since its launch in October 2011. In this study, the lifetime LGS gains are validated by the lunar calibration coefficients (defined as F-factors) using a lunar irradiance model called Global Space-based Inter-Calibration System (GSICS) Implementation of RObotic Lunar Observatory (ROLO) (GIRO). Using the moon as an independent on-orbit calibration source, the S-NPP VIIRS DNB on-board SD based radiometric calibration is validated by the lunar F-factors within two percent of the lunar F-factors in terms of the standard deviation in the long-term trends over nine years of the S-NPP VIIRS operation.

scholarly journals A Case for a New Satellite Mission for Remote Sensing of Night Lights

2021 ◽  
Vol 13 (12) ◽  
pp. 2294
Author(s):  
John C. Barentine ◽  
Ken Walczak ◽  
Geza Gyuk ◽  
Cynthia Tarr ◽  
Travis Longcore
Keyword(s):  
Remote Sensing ◽  
Infrared Imaging ◽  
Dynamic Range ◽  
Light Pollution ◽  
Satellite Mission ◽  
Diverse Range ◽  
Nighttime Lights ◽  
Short Time Span ◽  
Terrestrial Environments ◽  
Night Lights

The physiology and behavior of most life at or near the Earth’s surface has evolved over billions of years to be attuned with our planet’s natural light–dark cycle of day and night. However, over a relatively short time span, humans have disrupted this natural cycle of illumination with the introduction and now widespread proliferation of artificial light at night (ALAN). Growing research in a broad range of fields, such as ecology, the environment, human health, public safety, economy, and society, increasingly shows that ALAN is taking a profound toll on our world. Much of our current understanding of light pollution comes from datasets generated by remote sensing, primarily from two missions, the Operational Linescan System (OLS) instrument of the now-declassified Defense Meteorological Satellite Program (DMSP) of the U.S. Department of Defense and its follow-on platform, the Day-Night Band (DNB) of the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on board the Suomi National Polar-Orbiting Partnership satellite. Although they have both proved invaluable for ALAN research, sensing of nighttime lights was not the primary design objective for either the DMSP-OLS or VIIRS-DNB instruments; thus, they have some critical limitations. Being broadband sensors, both the DMSP-OLS and VIIRS-DNB instruments suffer from a lack of spectral information. Additionally, their spatial resolutions are too low for many ALAN research applications, though the VIIRS-DNB instrument is much improved over the DMSP-OLS in this regard, as well as in terms of dynamic range and quantization. Further, the very late local time of VIIRS-DNB observations potentially misses the true picture of ALAN. We reviewed both current literature and guiding advice from ALAN experts, aggregated from a diverse range of disciplines and Science Goals, to derive recommendations for a mission to expand knowledge of ALAN in areas that are not adequately addressed with currently existing orbital missions. We propose a stand-alone mission focused on understanding light pollution and its effects on our planet. Here we review the science cases and the subsequent mission recommendations for NITESat (Nighttime Imaging of Terrestrial Environments Satellite), a dedicated ALAN observing mission.

scholarly journals A Deep Learning Trained Clear-Sky Mask Algorithm for VIIRS Radiometric Bias Assessment

2019 ◽  
Vol 12 (1) ◽  
pp. 78 ◽  
Author(s):  
Xingming Liang ◽  
Quanhua Liu ◽  
Banghua Yan ◽  
Ninghai Sun
Keyword(s):  
Infrared Imaging ◽  
Satellite System ◽  
Radiative Transfer Model ◽  
Sensor Calibration ◽  
Transfer Model ◽  
Clear Sky ◽  
Brightness Temperatures ◽  
Spectral Bands ◽  
Csm Model ◽  
Cloud Mask

Clear-sky mask (CSM) is a crucial influence on the calculating accuracy of the sensor radiometric biases for spectral bands of visible, infrared, and microwave regions. In this study, a fully connected deep neural network (FCDN) was proposed to generate CSM for the Visible Infrared Imaging Radiometer Suite (VIIRS) onboard Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 satellites. The model, well-trained by S-NPP data, was used to generate both S-NPP and NOAA-20 CSMs for the independent data, and the results were validated against the biases between the sensor observations and Community Radiative Transfer Model (CRTM) calculations (O-M). The preliminary result shows that the FCDN-CSM model works well for identifying clear-sky pixels. Both O-M mean biases and standard deviations were comparable with the Advance Clear-Sky Processor over Ocean (ACSPO) and were significantly better than a prototype cloud mask (PCM) and the case without a clear-sky check. In addition, by replacing CRTM brightness temperatures (BTs) with the atmosphere air temperature and water vapor contents as input features, the FCDN-CSM exhibits its potential to generate fast and accurate VIIRS CSM onboard follow-up Joint Polar Satellite System (JPSS) satellites for sensor calibration and validation before the physics-based CSM is available.

scholarly journals The VIIRS Sea-Ice Albedo Product Generation and Preliminary Validation

2018 ◽  
Vol 10 (11) ◽  
pp. 1826 ◽  
Author(s):  
Jingjing Peng ◽  
Yunyue Yu ◽  
Peng Yu ◽  
Shunlin Liang
Keyword(s):  
Sea Ice ◽  
Infrared Imaging ◽  
Climate Model ◽  
Global Climate ◽  
Weather Prediction ◽  
Greenland Ice Sheet ◽  
Estimation Algorithm ◽  
Satellite System ◽  
Physical Models

Ice albedo feedback amplifies climate change signals and thus affects the global climate. Global long-term records on sea-ice albedo are important to characterize the regional or global energy budget. As the successor of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (Visible Infrared Imaging Radiometer Suite) started its observation from October 2011 on S-NPP (Suomi National Polar-orbiting Partnership). It has improved upon the capabilities of the operational Advanced Very High Resolution Radiometer (AVHRR) and provides observation continuity with MODIS. We used a direct estimation algorithm to produce a VIIRS sea-ice albedo (VSIA) product, which will be operational in the National Oceanic and Atmospheric Administration’s (NOAA) S-NPP Data Exploration (NDE) version of the VIIRS albedo product. The algorithm is developed from the angular bin regression method to simulate the sea-ice surface bidirectional reflectance distribution function (BRDF) from physical models, which can represent different sea-ice types and vary mixing fractions among snow, ice, and seawater. We compared the VSIA with six years of ground measurements at 30 automatic weather stations from the Programme for Monitoring of the Greenland Ice Sheet (PROMICE) and the Greenland Climate Network (GC-NET) as a proxy for sea-ice albedo. The results show that the VSIA product highly agreed with the station measurements with low bias (about 0.03) and low root mean square error (RMSE) (about 0.07) considering the Joint Polar Satellite System (JPSS) requirement is 0.05 and 0.08 at 4 km scale, respectively. We also evaluated the VSIA using two datasets of field measured sea-ice albedo from previous field campaigns. The comparisons suggest that VSIA generally matches the magnitude of the ground measurements, with a bias of 0.09 between the instantaneous albedos in the central Arctic and a bias of 0.077 between the daily mean albedos near Alaska. The discrepancy is mainly due to the scale difference at both spatial and temporal dimensions and the limited sample size. The VSIA data will serve for weather prediction applications and climate model calibrations. Combined with the historical observations from MODIS, current S-NPP VIIRS, and NOAA-20 VIIRS observations, VSIA will dramatically contribute to providing high-accuracy routine sea-ice albedo products and irreplaceable records for monitoring the long-term sea-ice albedo for climate research.

scholarly journals An Overall Assessment of JPSS-2 VIIRS Radiometric Performance Based on Pre-Launch Testing

2018 ◽  
Vol 10 (12) ◽  
pp. 1921 ◽  
Author(s):  
Hassan Oudrari ◽  
Jeff McIntire ◽  
Xiaoxiong Xiong ◽  
James Butler ◽  
Qiang Ji ◽  
...  
Keyword(s):  
Performance Metrics ◽  
Infrared Imaging ◽  
Dynamic Range ◽  
Signal To Noise Ratio ◽  
Scattered Light ◽  
Light Response ◽  
Satellite System ◽  
Sensor Data ◽  
Polarization Sensitivity ◽  
Spectral Performance

The Visible Infrared Imaging Radiometer Suite (VIIRS) on-board the second Joint Polar Satellite System (JPSS) completed its sensor level testing in February 2018. The JPSS-2 (J2) mission is scheduled to launch in 2022 and will be very similar to its two predecessor missions, the Suomi National Polar-orbiting Partnership (SNPP) mission, launched on 28 October 2011 and JPSS-1 (renamed NOAA-20) launched on 18 November 2017. VIIRS instrument has 22 spectral bands covering the spectrum between 0.4 and 12.6 μm: 14 reflective solar bands (RSB), 7 thermal emissive bands (TEB) and one day-night band (DNB). It is a cross-track scanning radiometer capable of providing global measurements through observations at two spatial resolutions, 375 m and 750 m at nadir for the imaging bands and moderate bands, respectively. This paper will provide an overview of J2 VIIRS characterization methodologies and calibration performance during the pre-launch testing phases performed by the National Aeronautics and Space Administration (NASA) VIIRS Characterization Support Team (VCST) to evaluate the at-launch baseline radiometric performance and generate the parameters needed to populate the sensor data record (SDR) Look-Up-Tables (LUTs). Our analysis results confirmed the good performance of J2 VIIRS, in general as good as previous VIIRS instruments and all non-compliances are expected to have low impact on data quality. Key sensor performance metrics include the signal to noise ratio (SNR), radiance dynamic range, reflective and emissive bands calibration performance, polarization sensitivity, spectral performance, response versus scan-angle (RVS) and scattered light response. A set of performance metrics generated during the pre-launch testing program will be compared to both the SNPP and JPSS-1 VIIRS sensors.

scholarly journals Pre-launch Radiometric Characterization of JPSS-2 VIIRS Thermal Emissive Bands

2019 ◽  
Vol 11 (6) ◽  
pp. 732
Author(s):  
Jeff McIntire ◽  
David Moyer ◽  
Hassan Oudrari ◽  
Xiaoxiong Xiong
Keyword(s):  
Performance Metrics ◽  
Infrared Imaging ◽  
Dynamic Range ◽  
Satellite System ◽  
Quality Data ◽  
Climate Data ◽  
Environmental Testing ◽  
Improved Performance ◽  
Estimation Of Uncertainties ◽  
Thermal Band

The Joint Polar Satellite System 2 (JPSS-2) Visible Infrared Imaging Radiometer Suite (VIIRS) is the third in its series of sensors designed to produce high quality data products for environmental and climate data records once launched. To meet this goal, the VIIRS instrument must be calibrated and characterized prior to launch. A comprehensive test program was conducted at the Raytheon Space and Airborne Systems facility in 2016–2017, including extensive environmental testing. The pre-launch thermal band radiometric performance and stability is the focus of this work including: the evaluation of a number of sensor performance metrics, comparison to the design requirements, and the estimation of uncertainties. Comparisons of the thermal band performance to the earlier Suomi National Polar-orbiting Partnership (SNPP) and JPSS-1 VIIRS instruments as well as the design specifications have shown that JPSS-2 VIIRS exhibits similar performance to its predecessors. The differences of note (decreased blackbody uniformity, reduced dynamic range for bands M15 and M16, and improved performance with respect to striping) are small and not expected to have a significant impact on the science products.

Sign in / Sign up

Export Citation Format

Share Document