PROGRESS IN GEOGRAPHY ›› 2019, Vol. 38 ›› Issue (1): 126-138.doi: 10.18306/dlkxjz.2019.01.011

• Reviews • Previous Articles     Next Articles

Interpolation methods comparison of VIIRS/DNB nighttime light monthly composites: A case study of Beijing

Mulin CHEN1,2(), Hongyan CAI2,*()   

  1. 1. The College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China
    2. State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing 100101, China
  • Received:2018-04-03 Revised:2018-09-16 Online:2019-01-28 Published:2019-01-22
  • Contact: Hongyan CAI;
  • Supported by:
    Strategic Priority Research Program of Chinese Academy of Sciences, No. XDA20010203;Key Project of the Chinese Academy of Sciences, No. ZDRW-ZS-2017-4;Fundamental Research Funds for the Central Universities, No. 2018CXZZ003.


Comparing with nighttime light data acquired by the Defense Meteorological Satellite Program's Operational Linescan System (DMSP/OLS), nighttime light data sensed by the Visible Infrared Imaging Radiometer Suite Day/Night Band (VIIRS/DNB) have a higher spatial resolution and finer temporal resolution. VIIRS/DNB nighttime light data also have a substantial number of improvements in terms of accuracy and in-flight calibrations. As a result, VIIRS/DNB nighttime light data become a new research hotspot rapidly. Even so, VIIRS/DNB nighttime light data are vulnerable to stray light and contain a large number of distorted values in mid and high latitudes, especially in summer. Therefore, this study took Beijing as an example and adopted cubic spline interpolation (spline), cubic Hermite interpolation (Hermite), gray model (GM), and triple exponential smoothing (exponent) to interpolate default data of May to July 2015, and then compared the results of these four interpolation algorithms. The result shows that: 1) With regard to abnormal values, Hermite does not produce any abnormal value, while the other three algorithms generate few such values (0.02%~1.34%). 2) Comparing with the reference data—the Visible Infrared Imaging Radiometer Suite Cloud Mask Stray Light (VCMSL) version, the interpolation result of Hermite is closest to the reference, and the GM result is least close to the reference. 3) In terms of computing time, all of these four algorithms are easy to be programmed and calculated, but the exponential smoothing method has to calculate smoothing parameter repeatedly and therefore it will spend much more time than the other three algorithms. In conclusion, a comprehensive assessment shows that when the two time periods before and after the interpolation months both have enough original data, Hermite will be the best choice because of its great interpolation performance, no overshoots, and fast calculation speed. Spline takes the second place. When only one side of the interpolation months has adequate data, GM and exponent methods both can be used. The GM calculation runs fast but the interpolation result is not optimal, and exponent calculation runs slow but the algorithm interpolates well.

Key words: VIIRS/DNB nighttime light data, cubic spline interpolation, cubic Hermite interpolation, gray model, triple exponential smoothing method