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Ye, Y.; Deng, J.; Huang, L.; Zheng, Q.; Wang, K.; Tong, C.; Hong, Y. |
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Title |
Modeling and Prediction of NPP-VIIRS Nighttime Light Imagery Based on Spatiotemporal Statistical Method |
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2020 |
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IEEE Transactions on Geoscience and Remote Sensing |
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in press |
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Remote Sensing |
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The cloud-free monthly composite of the global nighttime light (NTL) data derived from the Suomi National Polar orbiting Partnership with the Visible Infrared Imaging Radiometer Suite (VIIRS) day/night band (DNB) has gained popularity for detecting anthropogenic and socioeconomic activities. However, the monthly VIIRS DNB composite suffers from a data missing problem induced by continuous cloud cover. The full potential of the VIIRS DNB time series is consequently hindered by low-quality and missing observations. This article proposes a spatiotemporal statistical method (STSM) to predict the VIIRS DNB imagery in severe absence of valid observations' situation. The polynomial with the harmonic model was applied to describe the long-term trends and seasonal cycles in time series. A spatial marginal semivariogram was established to quantify the data dependence in space; we then used spatial interpolation to correct the predicted results from temporal curve fitting. The final predicted values were validated with the actual values based on cross-validation. The results suggest that the STSM is suitable for predicting with a high coefficient of determination (R² = 0.922) and a relatively low root-mean-square error (RMSE = 3.40 nW/cm²/sr). We extended the proposed method to forecast future imagery for a five-month period, the performance of which was more stable, with the highest R²/RMSE (0.158 ± 0.010), compared with two other methods. Therefore, the STSM is effective and stable for modeling and predicting the VIIRS DNB monthly composite and will help address the data missing issue. |
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UP @ altintas1 @ |
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3267 |
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Polivka, T.N.; Wang, J.; Ellison, L.T.; Hyer, E.J.; Ichoku, C.M. |
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Improving Nocturnal Fire Detection With the VIIRS Day-Night Band |
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Journal Article |
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2016 |
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IEEE Transactions on Geoscience and Remote Sensing |
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IEEE Trans. Geosci. Remote Sensing |
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54 |
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9 |
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5503-5519 |
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Remote Sensing |
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Building on existing techniques for satellite remote sensing of fires, this paper takes advantage of the day-night band (DNB) aboard the Visible Infrared Imaging Radiometer Suite (VIIRS) to develop the Firelight Detection Algorithm (FILDA), which characterizes fire pixels based on both visible-light and infrared (IR) signatures at night. By adjusting fire pixel selection criteria to include visible-light signatures, FILDA allows for significantly improved detection of pixels with smaller and/or cooler subpixel hotspots than the operational Interface Data Processing System (IDPS) algorithm. VIIRS scenes with near-coincident Advanced Spaceborne Thermal Emission and Reflection (ASTER) overpasses are examined after applying the operational VIIRS fire product algorithm and including a modified “candidate fire pixel selection†approach from FILDA that lowers the 4-μm brightness temperature (BT) threshold but includes a minimum DNB radiance. FILDA is shown to be effective in detecting gas flares and characterizing fire lines during large forest fires (such as the Rim Fire in California and High Park fire in Colorado). Compared with the operational VIIRS fire algorithm for the study period, FILDA shows a large increase (up to 90%) in the number of detected fire pixels that can be verified with the finer resolution ASTER data (90 m). Part (30%) of this increase is likely due to a combined use of DNB and lower 4-μm BT thresholds for fire detection in FILDA. Although further studies are needed, quantitative use of the DNB to improve fire detection could lead to reduced response times to wildfires and better estimate of fire characteristics (smoldering and flaming) at night. |
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0196-2892 |
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LoNNe @ kyba @ |
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1781 |
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Letu, H.; Hara, M.; Tana, G.; Nishio, F. |
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A Saturated Light Correction Method for DMSP/OLS Nighttime Satellite Imagery |
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2012 |
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IEEE Transactions on Geoscience and Remote Sensing |
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IEEE Trans. Geosci. Remote Sensing |
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50 |
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2 |
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389-396 |
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DMSP-OLS; remote sensing; light at night; radiometry; calibration |
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Several studies have clarified that electric power consumption can be estimated from the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) stable light imagery. As digital numbers (DNs) of stable light images are often saturated in the center of city areas, we developed a saturated light correction method for the DMSP/OLS stable light image using the nighttime radiance calibration image of the DMSP/OLS. The comparison between the nonsaturated part of the stable light image for 1999 and the radiance calibration image for 1996-1997 in major areas of Japan showed a strong linear correlation (R2 = 92.73) between the DNs of both images. Saturated DNs of the stable light image could therefore be corrected based on the correlation equation between the two images. To evaluate the new saturated light correction method, a regression analysis is performed between statistic data of electric power consumption from lighting and the cumulative DNs of the stable light image before and after correcting for the saturation effects by the new method, in comparison to the conventional method, which is, the cubic regression equation method. The results show a stronger improvement in the determination coefficient with the new saturated light correction method (R2 = 0.91, P = 1.7 ·10-6 <; 0.05) than with the conventional method (R2 = 0.81, P = 2.6 ·10-6 <; 0.05) from the initial correlation with the uncorrected data (R2 = 0.70, P = 4.5 · 10-6 <; 0.05). The new method proves therefore to be very efficient for saturated light correction. |
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0196-2892 |
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IDA @ john @ |
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204 |
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Zhao, N., Zhang, W., Liu, Y., Samson, E. L., Chen, Y., & Cao, G. |
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Improving Nighttime Light Imagery With Location-Based Social Media Data |
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2018 |
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IEEE Transactions on Geoscience and Remote Sensing |
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57 |
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4 |
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Remote Sensing |
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Location-based social media have been extensively utilized in the concept of “social sensing” to exploit dynamic information about human activities, yet joint uses of social sensing and remote sensing images are underdeveloped at present. In this paper, the close relationship between the number of Twitter users and brightness of nighttime lights (NTL) over the contiguous United States is calculated and geotagged tweets are then used to upsample a stable light image for 2013. An associated outcome of the upsampling process is the solution of two major problems existing in the NTL image, pixel saturation, and blooming effects. Compared with the original stable light image, digital number (DN) values of the upsampled stable light image have larger correlation coefficients with gridded population (0.47 versus 0.09) and DN values of the new generation NTL image product (0.56 versus 0.52), i.e., the Visible Infrared Imaging Radiometer Suite day/night band image composite. In addition, total personal incomes of states are disaggregated to each pixel in proportion to the DN value of the pixel in the NTL images and then aggregate by counties. Personal incomes distributed by the upsampled NTL image are closer to the official demographic data than those distributed by the original stable light image. All of these results explore the potential of geotagged tweets to improve the quality of NTL images for more accurately estimating or mapping socioeconomic factors. |
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2353 |
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Schwarting, T., McIntire, J., Oudrari, H., & Xiong, X |
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JPSS-1/NOAA-20 VIIRS Day-Night Band Prelaunch Radiometric Calibration and Performance |
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2019 |
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IEEE Transactions on Geoscience and Remote Sensing |
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1-13 |
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Instrumentation |
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The Visible Infrared Imaging Radiometer Suite (VIIRS) on board the first Joint Polar-Orbiting Satellite System series 1 (JPSS-1) has a panchromatic, three gain stage, day-night band (DNB) capable of imaging the Earth under illumination conditions ranging from reflected moonlight to daytime scenes. The DNB has four charged-coupled devices (CCDs) with 32 different modes of time-delay integration and subpixel aggregation to achieve high SNR in low light conditions while maintaining roughly constant spatial resolution across scan. During the prelaunch testing phase, these 32 different aggregation modes are separately calibrated over a large dynamic range (covering seven orders of magnitude) through a series of radiometric tests designed to generate initial calibration coefficients for the sensor data record (SDR) operational algorithm, assess radiometric performance, and determine compliance with the sensor design requirements. Early in the environmental testing at the Raytheon El Segundo facility, nonlinear behavior was discovered in some DNB edge of scan aggregation modes at low signal levels. In response to this nonlinearity, the test program was altered to characterize the radiometric performance both in the baseline configuration and with a modified aggregation scheme that eliminates the modes used at the end of scan, replacing them with an unaffected adjacent mode and trading off spatial resolution for improved linearity. Presented in this paper is the radiometric performance under both sensor configurations including dynamic range, sensitivity, radiometric uncertainty, and nonlinearity along with a discussion of the potential impact to DNB on-orbit calibration and SDR performance. |
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IDA @ intern @ |
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2541 |
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