GDP Forecasting Model for China's Provinces Using Nighttime Light Remote Sensing Data

被引：19

作者：

Gu, Yan ^{[1
]}

Shao, Zhenfeng ^{[1
]}

Huang, Xiao ^{[2
]}

Cai, Bowen ^{[3
]}

机构：

[1] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, Wuhan 430072, Peoples R China

[2] Univ Arkansas, Dept Geosci, Fayetteville, AR 72701 USA

[3] Wuhan Univ, Sch Remote Sensing & Informat Engn, Wuhan 430072, Peoples R China

来源：

REMOTE SENSING | 2022年 / 14卷 / 15期

基金：

中国国家自然科学基金;

关键词：

nighttime light remote sensing; gross domestic product (GDP); ARIMA model; DMSP-OLS; IMAGES; MODIS;

D O I：

10.3390/rs14153671

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

In order to promote the economic development of China's provinces and provide references for the provinces to make effective economic decisions, it is urgent to investigate the trend of province-level economic development. In this study, DMSP/OLS data and NPP/VIIRS data were used to predict economic development. Based on the GDP data of China's provinces from 1992 to 2016 and the nighttime light remote sensing (NTL) data of corresponding years, we forecast GDP via the linear model (LR model), ARIMA model, ARIMAX model, and SARIMA model. Models were verified against the GDP records from 2017 to 2019. The experimental results showed that the involvement of NTL as exogenous variables led to improved GDP prediction.

引用

页数：17

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