A Spatiotemporally Constrained Interpolation Method for Missing Pixel Values in the Suomi-NPP VIIRS Monthly Composite Images: Taking Shanghai as an Example
-
Published:2023-05-08
Issue:9
Volume:15
Page:2480
-
ISSN:2072-4292
-
Container-title:Remote Sensing
-
language:en
-
Short-container-title:Remote Sensing
Author:
Liu Qingyun1, Fan Junfu12ORCID, Zuo Jiwei1, Li Ping1, Shen Yunpeng1, Ren Zhoupeng2ORCID, Zhang Yi3
Affiliation:
1. School of Civil and Architectural Engineering, Shandong University of Technology, Zibo 255000, China 2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China 3. School of Geosciences and Info-Physics, Central South University, Changsha 410083, China
Abstract
The Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB) nighttime light data is a powerful remote sensing data source. However, due to stray light pollution, there is a lack of VIIRS data in mid-high latitudes during the summer, resulting in the absence of high-precision spatiotemporal continuous datasets. In this paper, we first select nine-time series interpolation methods to interpolate the missing images. Second, we construct image pixel-level temporal continuity constraints and spatial correlation constraints and remove the pixels that do not meet the constraints, and the eliminated pixels are filled with the focal statistics tool. Finally, the accuracy of the time series interpolation method and the spatiotemporally constrained interpolation method (STCIM) proposed in this paper are evaluated from three aspects: the number of abnormal pixels (NP), the total light brightness value (TDN), and the absolute value of the difference (ADN). The results show that the images simulated by the STCIM are more accurate than the nine selected time series interpolation methods, and the image interpolation accuracy is significantly improved. Relevant research results have improved the quality of the VIIRS dataset, promoted the application research based on the VIIRS DNB long-time series night light remote sensing image, and enriched the night light remote sensing theory and method system.
Funder
National Natural Science Foundation of China State Key Laboratory of Resources and Environmental Information System Shandong Provincial Natural Science Foundation National Key Research and Development Program of China Shandong University of Technology
Subject
General Earth and Planetary Sciences
Reference43 articles.
1. Remote sensing of night-time light;Li;Int. J. Remote Sens.,2017 2. Relation between satellite observed visible-near infrared emissions, population, economic activity and electric power consumption;Elvidge;Int. J. Remote Sens.,1997 3. Ma, T., Xu, T., Huang, L., and Zhou, A. (2018). A human settlement composite index (HSCI) derived from nighttime luminosity associated with imperviousness and vegetation indexes. Remote Sens., 10. 4. Lu, D., Wang, Y.H., Yang, Q.Y., Su, K.C., Zhang, H.Z., and Li, Y.Q. (2021). Modeling Spatiotemporal Population Changes by Integrating DMSP-OLS and NPP-VIIRS Nighttime Light Data in Chongqing, China. Remote Sens., 13. 5. Gu, Y., Shao, Z.F., Huang, X., and Cai, B.W. (2022). GDP Forecasting Model for China’s Provinces Using Nighttime Light Remote Sensing Data. Remote Sens., 14.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献
|
|