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Remote Sens. 2017, 9(8), 786;

Transferability of Economy Estimation Based on DMSP/OLS Night-Time Light

College of Engineering, Peking University, Beijing 100871, China
College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
Author to whom correspondence should be addressed.
Received: 29 May 2017 / Revised: 14 July 2017 / Accepted: 28 July 2017 / Published: 31 July 2017
(This article belongs to the Special Issue Recent Advances in Remote Sensing with Nighttime Lights)
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Despite the fact that economic data are of great significance in the assessment of human socioeconomic development, the application of this data has been hindered partly due to the unreliable and inefficient economic censuses conducted in developing countries. The night-time light (NTL) imagery from the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP/OLS) provides one of the most important ways to evaluate an economy with low cost and high efficiency. However, little research has addressed the transferability of the estimation across years. Based on the entire DN series from 0 to 63 of NTL data and GDP data in 31 provinces of mainland China from 2000 to 2012, this paper aims to study the transferability of economy estimation across years, with four linear and non-linear data mining methods, including the Multiple Linear Regression (MLR), Local Weighted Regression (LWR), Partial Least Squares Regression (PLSR), and Support Vector Machine Regression (SVMR). We firstly built up the GDP estimation model based on the NTL data in each year with each method respectively, then applied each model to the other 12 years for the evaluation of the time series transferability. Results revealed that the performances of models differ greatly across years and methods: PLSR (mean of ) and SVMR (mean of ) are superior to MLR (mean of ) and LWR (mean of ) for model calibration; only PLSR (mean of , mean of ) holds a strong transferability among different years; the frequency of three DN sections of (0–1), (4–16), and (57–63) are especially important for economy estimation. Such results are expected to provide a more comprehensive understanding of the NTL, which can be used for economy estimation across years. View Full-Text
Keywords: transferability; data mining; night-time light; DMSP/OLS; economy estimation transferability; data mining; night-time light; DMSP/OLS; economy estimation

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

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Qi, K.; Hu, Y.; Cheng, C.; Chen, B. Transferability of Economy Estimation Based on DMSP/OLS Night-Time Light. Remote Sens. 2017, 9, 786.

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