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Remote Sensing 2013, 5(6), 3057-3081; doi:10.3390/rs5063057

Article
Potential of NPP-VIIRS Nighttime Light Imagery for Modeling the Regional Economy of China
Xi Li 1,*, Huimin Xu 2, Xiaoling Chen 1 and Chang Li 3
1
State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China; E-Mail: cxl@lmars.whu.edu.cn
2
School of Economics, Zhongnan University of Economics and Law, Wuhan 430060, China; E-Mail: xuhuimin1985_2008@163.com
3
College of Urban and Environmental Science, Central China Normal University, Wuhan 430079, China; E-Mail: lcshaka@126.com
*
Author to whom correspondence should be addressed; E-Mail: li_rs@163.com; Tel.: +86-27-6877-8141.
Received: 18 April 2013; in revised form: 7 June 2013 / Accepted: 13 June 2013 /
Published: 19 June 2013

Abstract

: Historically, the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) was the unique satellite sensor used to collect the nighttime light, which is an efficient means to map the global economic activities. Since it was launched in October 2011, the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite has become a new satellite used to monitor nighttime light. This study performed the first evaluation on the NPP-VIIRS nighttime light imagery in modeling economy, analyzing 31 provincial regions and 393 county regions in China. For each region, the total nighttime light (TNL) and gross regional product (GRP) around the year of 2010 were derived, and a linear regression model was applied on the data. Through the regression, the TNL from NPP-VIIRS were found to exhibit R2 values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship between the TNL from DMSP-OLS (F16 and F18 satellites) and GRP. Using the regression models, the GRP was predicted from the TNL for each region, and we found that the NPP-VIIRS data is more predictable for the GRP than those of the DMSP-OLS data. This study demonstrates that the recently released NPP-VIIRS nighttime light imagery has a stronger capacity in modeling regional economy than those of the DMSP-OLS data. These findings provide a foundation to model the global and regional economy with the recently availability of the NPP-VIIRS data, especially in the regions where economic census data is difficult to access.
Keywords:
nighttime light; gross regional product; Visible Infrared Imaging Radiometer Suite; linear regression

1. Introduction

Regional and global economic data is important to understanding the developing world, and performing an economic census plays a major role in collecting such data. However, the economic census data is sometimes difficult to acquire at both the regional and global scales. In addition, the economic census data is always coarse in the spatial dimension, because the data in the basic administrative regions is inaccessible in many countries. Therefore, surveying the world economy in spatial dimensions using technical approaches as alternatives to the traditional economic census is an important and challenging task for the academic community [1,2].

Compared to the high cost of performing a traditional economic census, a remote sensing technique provides an efficient approach to survey the economy. A typical example is using remotely sensed optical imagery for mapping the urban land use distribution, which is an important indicator of the economic status of a country [35]. In addition, remote sensing can also be used to investigate agriculture [6,7], fishery [8,9] and forestry [10], which are important components of a country’s economy. Among the various sources of remote sensing data, nighttime light imagery has played a direct and unique role in investigating economic activities, because the artificial nighttime light can reflect the use of public lighting and commercial lighting, which are strongly associated with the state of the economy. A number of studies have indicated that the nighttime light has a very high correlation with the national and regional economic volume [11,12]. Compared to the census approach, the mapping of nighttime light can help to investigate the economics on a large scale with very low cost; thus, the nighttime light imagery has been used to investigate the regional economics in many countries [1317].

Traditionally, the nighttime light data is acquired by the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) sensors, which is owned by the U.S. Air Force and archived by National Oceanic and Atmospheric Administration (NOAA) of the United States. The first DMSP satellite was launched in 1972, and the digital format of the imagery has been recorded since the year of 1992. From 1992 to the present, there were a series of DMSP satellites (e.g., F10–F18) imaging nighttime light. A DMSP-OLS sensor can acquire images every day, but the daily acquired imagery is usually improper for further analysis, because such image signal strength reduces due to sensor noise, atmospheric effects and moonlight variation. Thus individual DMSP-OLS images taken over a year should be combined together to produce a global annual stable light product, namely the average visible, stable lights and cloud-free composite, which has been produced by the Earth Observation Group in the NOAA National Geophysical Data Center. The composites, at spatial a resolution of 30 arc second, have pixel values ranging between 0 and 63. All the DMSP-OLS composites are available at the website of NOAA ( http://www.ngdc.noaa.gov/dmsp/downloadV4composites.html). Although some nighttime light imagery at higher spatial resolution have been evaluated in environmental and economic fields [18,19], the only practical nighttime light data on economic evaluation was DMSP-OLS data, due to its large coverage area and low price.

A new generation of nighttime light imagery emerged in 2012, the Visible Infrared Imaging Radiometer Suite (VIIRS) nighttime light imagery, acquired by the Suomi National Polar-orbiting Partnership (NPP) Satellite. Similar with the DMSP-OLS nighttime light data, the NPP-VIIRS was initially designed to monitor the atmosphere and environment, and its nighttime light imagery is a byproduct of the data under the cloud-free condition. A more detailed introduction on NPP-VIIRS is provided in the website of National Aeronautics and Space Administration ( http://npp.gsfc.nasa.gov/index.html). On January 3rd, 2013, National Oceanic and Atmosphere Administration (NOAA) released the first global nighttime light imagery derived from the NPP-VIIRS data at its website ( http://www.ngdc.noaa.gov/dmsp/data/viirs_fire/viirs_html/viirs_ntl.html). The imagery was generated by using the VIIRS day/night band data acquired on nights with zero moonlight. The observed individual NPP-VIIRS images in 18–26 April 2012 and 11–23 October 2012 were used, and a cloud mask was introduced using the VIIRS M15 thermal band to produce the global nighttime light imagery.

Although DMSP-OLS nighttime light imagery has shown strong capacity in evaluating economic distribution over both global and regional scales, its weakness is obvious—no on-board radiometric calibration and limited radiometric detection capacity, which results in the over-saturation problem in urban centers [2023]. All these weaknesses may reduce the correlation between the detected nighttime light and the economic activities. Fortunately, the arrival of NPP-VIIRS, with its on-board radiometric calibration and wider radiometric detection range, can provide a more accurate nighttime light source for economic modeling. Therefore, the purpose of this study is to investigate the potential of NPP-VIIRS nighttime light imagery in modeling a regional economy, with a comparative analysis between the DMSP-OLS data and the NPP-VIIRS data.

2. Study Area and Data

To evaluate the potential of NPP-VIIRS nighttime light imagery in modeling a regional economy, we used China’s provincial and county level administrative regions for analysis. In China, there are three major levels of administrative areas: province, prefecture and county. In this study, all the provincial regions, except Hong Kong, Macau and Taiwan, were selected, resulting in 31 provincial regions for analysis. At the county level, there are over 2000 counties in China, of which the administrative boundary is difficult to acquire one by one. Counties in five provinces, Anhui, Fujian, Guangdong, Jiangxi and Zhejiang were used, and as a result, there are 393 county level regions for analysis. More specifically, in this study, the county level regions include three types: municipality in a prefecture-level city, county and county level city. In fact, the municipality has a higher administrative rank than a county, but the economic data and boundaries of its districts are difficult to collect from public sources; thus, each municipality is treated as a statistical region along with the county level region. Figure 1 and Figure 2 illustrate the 31 provinces and 393 county level regions as maps, respectively, and Table 1 lists the number of county level regions in the five provinces.

In this study, the DMSP-OLS data and NPP-VIIRS data were used. Because the NPP-VIIRS data is only available for the year 2012, we chose the DMSP-OLS data with an acquisition year close to 2012; the closest available DMSP-OLS data are the annual stable nighttime light composites in 2009 and 2010, which were acquired by the F16 and F18 satellites, respectively. The NPP-VIIRS imagery is a preliminary product, which contains lights from cities, towns, transportation corridors, gas flares, and biomass burning and background noise, and in some places has features associated with the reflectance of light off bright surfaces, such as snow covered mountains or bright playa lake beds. In addition, the confounding factors that are irrelevant to economic activities must be removed. We propose a simple and approximate process for removing the confounding factors, which uses a hypothesis that the lit areas in 2010 and 2012 are the same, and is based on the following process: generate a mask with all positive value pixels from the DMSP-OLS imagery in 2010 and multiply the NPP-VIIRS imagery by the mask to derive a denoised nighttime light imagery. The hypothesis is approximately correct because there should be a small number of pixels where the lit value increased from zero to positive. Nevertheless, the process is efficient and the data quality has been improved.

All the nighttime light imagery was reprojected to a Lambert azimuthal equal area projection with a spatial resolution at 500 m. The two types of nighttime light data are described in Table 2. The nighttime light imagery of China in 2009, 2010 and 2012 are shown in Figures 3, 4 and 5, respectively. In addition, the nighttime light imagery in Guangzhou municipality was shown as a local case in Figure 6.

The gross regional production (GRP) data for each county level region and provincial region is derived from China Statistical Yearbook for Regional Economy and Urban Statistical Yearbook of China [24,25]. The Chinese currency unit of the GRP is Renminbi (RMB), also called Chinese Yuan.

3. Methodology and Analysis

3.1. Linear Regression Model

To evaluate the capacity of nighttime light imagery in modeling the economy, we prepare the GRP data and nighttime light data for each administrative region and perform a regression analysis to quantify the relationship between the two types of data. The total nighttime light (TNL), defined as the sum of all pixel values in a region of an image [22], is used as the nighttime light data to characterize an administrative region. Because the GRP data in 2012 is not available, the TNL data in 2012 was associated with the 2010 GRP data. A linear regression model without an intercept was used to describe the relationship between GRP and TNL as

g = wt
where g denotes the GRP, t denotes the TNL and w represents the slope. At the provincial level, the GRP data and the TNL data were analyzed for the 31 provincial regions using the regression analysis. At the county level, the GRP data and the TNL data were analyzed for the 393 county level regions. To describe the regression analysis more clearly, the analysis data is listed in Table 3.

3.2. Regression Results

Through the linear regression, the TNL-GRP relationship was analyzed, as shown in Figure 7. At the provincial level, the R2 of the DMSP-OLS data with GRP are 0.6923 in 2009 (DMSP-F16 satellite) and 0.7056 in 2010 (DMSP-F18 satellite), whereas that of the NPP-VIIRS data with GRP is 0.8699 in 2010. The results show that the VIIRS data can better reflect the GRP than the DMSP-OLS data in provincial regions.

At the county level, we found that the TNL-GRP relationship from the VIIRS data is stronger than those of the DMSP-OLS data. The R2 values are 0.7678 and 0.6748 for the data from 2009 (DMSP-F16 satellite) and 2010 (DMSP-F16 satellite), respectively, which are lower than 0.8544, the R2 value from the NPP-VIIRS data. Thus, NPP-VIIRS data is more strongly correlated with GRP than those of DMSP-OLS data from both the DMSP-F16 and DMSP-F18 satellites at the county level.

From the regression analysis, it is easy to determine that the NPP-VIIRS imagery has higher capacity in modeling the GRP. Here, we use the regression model to predict the GRP from nighttime light to evaluate the capacity of TNL in predicting GRP. For each region, a predicted GRP is calculated from the slope values and TNL using Equation (2). Because the numbers of provincial regions and county regions are 31 and 393, respectively, of which the latter is too large to be shown in a table; thus, we only list the real GRP and predicted GRP of the provincial regions in Table 4 and list the county data in the Appendix. Next, a relative error was used to evaluate the capacity of TNL in predicting GRP as

e = g g g
where g denotes the real GRP and g′ denotes the predicted GRP. The relative error values of all the provincial regions are listed in Table 4.

From Table 4, we found that the predictability of different nighttime light sensors varies in different provincial regions. For example, the relative error of the predicted GRP in 2009 derived from DMSP-OLS data in 2009 in Liaoning Province and Heilongjiang Province are 5.3% and 168.4%, respectively, which are very different. In comparison, the counterparts of the predicted GRP in 2010 derived from the NPP-VIIRS data in 2012 are −1.7% and 40.4%, respectively, which are much closer.

To evaluate the predictability of different nighttime light data for the GRP comprehensively, we classified the absolute relative error into three levels: 0–25% as high accuracy, 25–50% as moderate accuracy and >50% as wrong. Next, we calculated the percent of each level for the different estimation approaches. For example, when estimating the GRP from the DMSP-OLS data in 2009 in the provincial regions, there are nine provincial regions with high accuracy among the 31 regions, and the fraction of provincial regions of high accuracy is 29.03%. Based on this criterion, the predictability for each type of nighttime light imagery was quantified with the three indices listed in Table 5.

In the provincial regions, the NPP-VIIRS data exhibits a high capacity in predicting the GRP, with a high accuracy percent of 54.84%, which is much higher than those of the DMSP-OLS data in 2009 and 2010, as indicated in the table. In addition, the fraction of the data providing wrong predictions from the NPP-VIIRS data (29.03%) is much lower than those of the DMSP-OLS data in 2009 (41.94%) and 2010 (38.71%) as Table 5 indicates. Therefore, the NPP-VIIRS data is more reliable for predicting the GRP data in China’s provincial regions.

In the county regions, the fraction of high accuracy predictions from the NPP-VIIRS data is 34.10%, which is significantly higher than those of the DMSP data in 2009 and 2010, with values of 18.32% and 10.69%, respectively. The wrong percent index also demonstrates that the NPP-VIIRS data in more capable in accurately predicting the GRP than those of the DMSP data in 2009 and 2010, as indicated in Table 5.

Thus, we can conclude that the NPP-VIIRS data is more reliable for predicting the economic data than those of the DMSP-OLS data, and the NPP-VIIRS data is more robust in different spatial scales than those of the DMSP-OLS data. In addition, it is interesting to find a general underestimation in more developed regions (e.g., Beijing, Shanghai) and overestimation in less developed areas (e.g., Ningxia, Xinjinag, and Tibet). To explore this pattern in the NPP-VIIRS data, we performed a correlation analysis between the GRP Per Capita in 2010 and the relative error for the 31 provincial level regions, and found that the correlation coefficient is equal to −0.3849 under a significance level of 0.05, which indicates that the relative error has significant negative correlation with the GRP Per Capita.

3.3. Regression Analysis in the County Level Regions by Discarding Two Outliers

There are two obvious outliers in the regression analysis of the county regions in the above section (Figure 7), Guangzhou and Shenzhen, which are two major economic centers in Southern China. A possible reason for these outliers is that more GRP can be generated in these regions per unit NTL. Here, we discarded the two outliers and performed the regression analysis, which left 391 county regions for the regression analysis. The results of the regression analysis and its predictability analysis are shown in Figure 8 and Table 6, respectively.

From Figure 8, the R2 of the DMSP-OLS data in 2009, that in 2010 and the NPP-VIIRS data are 0.8513, 0.8011 and 0.9431, respectively, all of which are all significantly improved by discarding the two outliers, as demonstrated by comparison to the corresponding original R2 values of 0.7678, 0.6748 and 0.8544 (Figure 7). In particular, the NPP-VIIRS data are perfectly correlated with the GRP data, with an R2 of 0.9431. In addition, removing the outliers also greatly improved the predictability of all the nighttime light data, as indicated in Table 6—the high accuracy percentage increases and the wrongly predicted percentage decreases, as listed in Tables 5 and 6. These findings demonstrate that ability of the DMSP-OLS and NPP-VIIRS data to model the GRP can be greatly improved in county regions by discarding only very a few outliers. Furthermore, the NPP-VIIRS data is still more predictable in reflecting the GRP than those of the DMSP-OLS data under this condition.

3.4. Potential Factors behind the Results

From the experiments, the NPP-VIIRS nighttime light imagery exhibits a stronger ability than the DMSP-OLS nighttime light imagery to model and predict the gross regional product (GRP) in China. Due to absence of the GRP data in 2012, we made use of the 2012 NPP-VIIRS nighttime light imagery to model the GRP in 2010. Although there is a two-year gap between the NPP-VIIRS data and the GRP data, the NPP-VIIRS data exhibited good performance in modeling the GRP data. Naturally, we can infer that when using the NPP-VIIRS data to model the GRP data in the same year, the relationship should be stronger. Moreover, the NPP-VIIRS imagery we used is a primary product made by NOAA, who did not remove the background noise and some temporary lit sources, e.g., forest fires. To deal with this problem, we used a mask derived from the DMSP-OLS data in 2010 to only include the stable artificial lit sources to make the NPP-VIIRS data more efficient. Because the DMSP-OLS composites are generated from many individual images from one year with a series of advanced processing steps, such as denoising, while the NPP-VIIRS is only generated from several images without denoising, the two types of data cannot be directly compared. Nevertheless, the NPP-VIRRS data provided the best results, and there is significant room for improvement in the NPP-VIIRS data quality and NOAA is working to improve the data.

There are three potential factors that should make NPP-VIIRS imagery more efficient than the DMSP-OLS imagery in modeling the economy. First, the DMSP-OLS imagery has a serious problem of over-saturation in city centers because of the limitation of the radiometric detection ability [26]. To support this point of view, we calculated the ratio of the over-saturation area to the lit area for each provincial region from the DMSP-OLS data in 2010, and three regions, Beijing, Shanghai and Tianjin, were found to be in top three, with the ratio values of 13.82%, 13.41% and 4.93%, respectively. Because these three regions are highly over-saturated lit regions, the regression model significantly underestimated the GRP of the regions of Beijing, Shanghai and Tianjin, with relative errors of −54.6%, −67.9% and −35.6%, respectively, while the relative errors from the NPP-VIIRS estimation for Beijing, Shanghai and Tianjin were −40.6%, −17.9% and −5.0%, respectively (Table 4). Therefore, the GRP estimation can be improved significantly in the over-saturated regions when using the NPP-VIIRS data instead of the DMSP-OLS data. Second, while DMSP-OLS performs measurements around dusk or dawn, NPP-VIIRS performs measurements around midnight. Because human activities are often affected by time, the timing differences could have a big impact on sensors’ capability to capture economic activities. Third, the onboard calibration on NPP-VIIRS, which is absent on DMSP-OLS, can also have a huge impact on data quality. The mechanism of the above three factors are primarily inferred, and more rigorous investigations should be taken to clarify them.

4. Discussion

Although there were several remotely sensed nighttime light sources of data [11,18,19], the only global source for economic modeling was the DMSP-OLS data, which is useful in mapping the spatial distribution of the economy. Nevertheless, the DMSP-OLS has severe limits in spatial resolution and radiometric detection range [22], which hinder accurate economic modeling. The optimal manner to observe the global nighttime light is by using the Nightsat Mission concept [27], which aims to develop a specific remote sensing satellite to comprehensively record the light at fine spatial, temporal, spectral and radiometric resolution. However, due to the high cost and technical requirements, the Nightsat Mission is only in the blueprint stage. Under this background, the NPP-VIIRS nighttime light imagery, a byproduct of the recently launched Suomi NPP satellite, is currently making great progress in acting as a bridge to the Nightsat mission from the DMSP satellites.

Historically, modeling China’s regional economy using nighttime light imagery mainly takes prefecture and provincial level regions as material for analysis [13,17], and it is the first time to make the analysis in China’s county level regions which is more valuable than those of the provincial and prefectural regions since the these regions are basic statistical units in China and their statistical data are more likely to have error. Like the previous work of modeling regional economy [13,14], the nighttime light data cannot model the regional economy as the unique source because the regression model should be set up with samples from economic data in the same country or region. Therefore, prior knowledge is needed when using the nighttime light in modeling regional economy.

Since the product of the global NPP-VIIRS nighttime light imagery was released by NOAA in 2013, there has been no published work related to the NPP-VIIRS nighttime light imagery, and this study is the first evaluation of NPP-VIIRS nighttime light imagery which proves the its stronger capacity in modeling regional economy than the DMSP-OLS nighttime light imagery.

5. Conclusion

The Visible Infrared Imaging Radiometer Suite (VIIRS) sensor on the Suomi National Polar-orbiting Partnership (NPP) Satellite is a new generation sensor to record nighttime light, while this task was only assigned to the Defense Meteorological Satellite Program’s Operational Linescan System (DMSP-OLS) before 2011. As economic modeling is the a work that nighttime light imagery can efficiently deal with, this study made a first evaluation of the NPP-VIIRS nighttime light imagery by modeling China’s regional economy in 31 provincial units and 393 county units.

Through regression analysis, the NPP-VIIRS data was found to exhibit a stronger capacity in modeling the regional economy compared with the nighttime light data acquired from the DMSP/F16 and DMSP/F18 satellites. Quantitatively, the total nighttime light (TNL) from NPP-VIIRS were found to exhibit R2 values of 0.8699 and 0.8544 with the provincial GRP and county GRP, respectively, which are significantly stronger than the relationship (R2 at 0.6923 and 0.7678 for F16 satellite and R2 at 0.7056 and 0.6748 for F18 satellite) between the TNL from DMSP-OLS and GRP. And it is interesting find the TNL-GRP relationship can be significantly improved by discarding only two outliers in the regression analysis of the county level regions, making the R2 values from 0.7678, 0.6748 and 0.8544 to 0.8513, 0.8011 and 0.9431 for DMSP-OLS/F16, DMSP-OLS/F18 and NPP-VIIRS, respectively. With this improvement, TNL from the NPP-VIIRS imagery can be used to predict the GRP in county level regions with 42.97% of them in high accuracy, while the rates of the DMSP-OLS/ F16 and DMSP-OLS/ F18 are 36.06% and 19.18%, respectively. All these results demonstrate that the NPP-VIIRS data is more powerful than the DMSP-OLS data in modeling regional economy.

The good performance of the NPP-VIIRS data revealed by our analysis provides a quantitative foundation to use such imagery as data source for taking the economic census in the countries with low-quality statistical systems and countries where economic data is blocked to the outside. Consequently, such economic census data can help international community to provide humanitarian aid to the regions under economic crisis and humanitarian disasters.

Since NPP-VIIRS imagery is a recently emerging data source, all the released data is one scene of images acquired in 2012, which is insufficient to make a comprehensive evaluation. As the National Oceanic and Atmospheric Administration (NOAA) is working to produce more and higher quality NPP-VIIRS nighttime light imagery, future study can be taken on multi-temporal analysis of the imagery in many fields such as land cover mapping, change detection, energy consumption evaluation and fishing boats detection. Besides, more advanced technique should be developed to analyze this kind of imagery because of its higher spatial resolution than the traditional DMSP-OLS nighttime light imagery.

We would like to thank the anonymous reviewers who gave very helpful comments to improve the quality of this paper. This research was supported by the National Natural Science Foundation of China under grant nos. 41023001, 41101413, 41001260, 41101407 and 41071261, National Technology Support Project under grant nos. 2011BAB01B01 and 2012BAH28B04, the PhD Program Foundation of Ministry of Education of China under grant no. 20110141120073 and the 863 Program under grant nos. 2012AA12A304 and 2012AA12A306.

  • Conflict of InterestThe authors declare no conflict of interest.

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Appendix

Table Table A1. The gross regional product (GRP) and total nighttime light (TNL) in the 393 county level regions. DMSP 2009 represents the DMSP-OLS data in 2009, DMSP 2010 represents the DMSP-OLS data in 2010 and NPP 2012 represents the NPP-VIIRS data in 2012.

Click here to display table

Table A1. The gross regional product (GRP) and total nighttime light (TNL) in the 393 county level regions. DMSP 2009 represents the DMSP-OLS data in 2009, DMSP 2010 represents the DMSP-OLS data in 2010 and NPP 2012 represents the NPP-VIIRS data in 2012.
County NameProvinceGRP (Billion RMB)TNL

20092010DMSP 2009DMSP 2010NPP 2012
Shitai CountyAnhui1.091.266003958195
Yi CountyAnhui1.371.5827266946214
Jingde CountyAnhui1.672.0413435168279
Qimen CountyAnhui2.773.21258811458327
Jixi CountyAnhui2.803.3448289565778
Xiuning CountyAnhui3.524.14728317780863
Qingyang CountyAnhui3.544.359201247721390
Yuexi CountyAnhui3.524.55320010000418
Jing CountyAnhui3.844.684529143371422
Taihu CountyAnhui4.445.62310513742765
Wangjiang CountyAnhui4.465.729361227841296
Langxi CountyAnhui4.455.7411598286413061
Jinzhai CountyAnhui5.126.025420154001178
Hanshan CountyAnhui5.356.4415371333842638
Quanjiao CountyAnhui5.406.5019509399624224
Mingguang MunicipalityAnhui5.766.7914242299272431
Laian CountyAnhui5.786.9920144438733554
Dongzhi CountyAnhui5.657.029070237601113
Jieshou MunicipalityAnhui5.967.2613582224602765
Tongling CountyAnhui5.757.4626701569536367
Qianshan CountyAnhui5.607.796320210541194
Dangshan CountyAnhui6.537.8920508340402325
She CountyAnhui6.767.9215457305821318
Guzhen CountyAnhui6.638.1710176147891629
Funan CountyAnhui7.048.3017354395133362
Huoshan CountyAnhui6.688.3510776204341078
Fengyang CountyAnhui7.268.5225633500114068
Linquan CountyAnhui7.478.5920005433593308
Dingyuan CountyAnhui7.238.6515208438813366
He CountyAnhui7.278.7229517675104583
Si CountyAnhui7.368.7810130205441293
Wuhe CountyAnhui7.468.939257171531314
Susong CountyAnhui7.099.087380223381002
Lingbi CountyAnhui7.819.178646191982112
Lixin CountyAnhui8.139.4915142341962597
Shucheng CountyAnhui8.009.5011946267302211
Guangde CountyAnhui8.249.9317685464054182
Nanling CountyAnhui8.1710.1412162313222905
Wuhu CountyAnhui8.0510.3020098395354121
Lujiang CountyAnhui8.8710.3917414477533881
Shou CountyAnhui9.3410.6911204266812058
Fanchang CountyAnhui8.7710.8319200427945279
Taihe CountyAnhui9.4111.1325007482533836
Huaining CountyAnhui8.5111.2417650463472930
Suixi CountyAnhui9.1711.2853721931259143
Mengcheng CountyAnhui9.5811.439971235343422
Xiao CountyAnhui9.6511.6532039560874444
Zongyang CountyAnhui9.3012.0511601380711706
Yingshang CountyAnhui10.5712.5636005654785193
Guoyang CountyAnhui10.5812.8020960395243556
Ningguo MunicipalityAnhui10.6813.0112723264742716
Tongcheng MunicipalityAnhui10.2813.4113763331992403
Huaiyuan CountyAnhui11.1913.5620597415013508
Huangshan MunicipalityAnhui12.2814.0826402465732918
Huoqiu CountyAnhui11.4314.2920824501573371
Tianchang MunicipalityAnhui11.7114.7033932632254316
Xuancheng MunicipalityAnhui13.1214.7528843642696570
Chaozhou MunicipalityAnhui14.0615.4926473609165511
Chizhou MunicipalityAnhui12.6715.9329289680755201
Changfeng CountyAnhui13.2216.38445479278011225
Lu'an MunicipalityAnhui12.6716.5045404926797632
Fengtai CountyAnhui14.7117.2031259563194719
Chuzhou MunicipalityAnhui13.6517.42390956714811717
Bozhou MunicipalityAnhui16.0217.5642206780698010
Dangtu CountyAnhui14.9318.94385198496710239
Wuwei CountyAnhui17.8521.9226172794705742
Feidong CountyAnhui18.6822.00522649573913586
Fuyang MunicipalityAnhui18.8924.29475268547810732
Suuzhou MunicipalityAnhui21.5224.65510428702911054
Feixi CountyAnhui21.4627.485851911541421955
Anqing MunicipalityAnhui19.2328.3520434377115004
Bengbu MunicipalityAnhui26.7131.52366505549912621
Huaibei MunicipalityAnhui28.1934.8827697347274770
Tongling MunicipalityAnhui28.6139.218667145962893
Huainan MunicipalityAnhui36.2543.16554529701911547
Maanshan MunicipalityAnhui52.0762.16329075107514471
Wuhu MunicipalityAnhui65.2379.47552729698920943
Hefei MunicipalityAnhui159.15192.04756969679237327
Songxi CountyFujian2.052.3925764437324
Zhenghe CountyFujian2.062.4025707101701
Zherong CountyFujian2.282.7122773022303
Zhouning CountyFujian2.292.7239626793792
Mingxi CountyFujian2.663.1828456474553
Guangze CountyFujian2.923.4330306043593
Pingnan CountyFujian2.883.4430714897356
Shouning CountyFujian3.003.6332914200282
Qingliu CountyFujian3.514.25471812014905
Jianning CountyFujian3.514.2837337580488
Huaan CountyFujian3.584.339503140471502
Taining CountyFujian3.954.54521612732812
Shunchang CountyFujian4.495.1647637251712
Jiangle CountyFujian4.415.366845149081199
Ninghua CountyFujian4.815.884591126801138
Pucheng CountyFujian5.496.4753959260614
Wuyishan MunicipalityFujian5.596.5814713241153306
Yongtai CountyFujian6.167.308506164391424
Yunxiao CountyFujian5.837.3312778168223097
Wuping CountyFujian6.167.4210250170161277
Dongshan CountyFujian5.907.8417326259662005
Jianyang MunicipalityFujian6.647.8613000229403814
Changtai CountyFujian6.187.9019932353525983
Liancheng CountyFujian6.247.9515152224822051
Gutian CountyFujian6.928.1911368109981430
Minqing CountyFujian7.388.4310946151651406
Datian CountyFujian6.608.5719737274252586
Pinghe CountyFujian7.258.7715062292752691
Changting CountyFujian7.278.8311065212151820
Pingtan CountyFujian7.368.976260131572527
Xiapu CountyFujian7.999.3911008162801873
Zhaoan CountyFujian8.099.7217645292032365
Youxi CountyFujian8.3110.0017224204431743
Zhangping MunicipalityFujian8.3010.1614054242402142
Dehua CountyFujian8.8610.1713105199163126
Jian'ou MunicipalityFujian8.7210.3213460201291912
Luoyuan CountyFujian7.8910.3917438257833568
Sha CountyFujian8.5810.4321883295773097
Shaowu MunicipalityFujian8.9310.629286168811775
Nanjing CountyFujian8.9510.7220884374032940
Yongding CountyFujian9.4611.1023248409013602
Shanghang CountyFujian10.8312.6724797414544025
Ningde MunicipalityFujian10.8412.6817337263485279
Fuding MunicipalityFujian10.7613.3820452393105972
Xianyou CountyFujian11.9413.9827430458245675
Zhangpu CountyFujian12.5615.0038744633266961
Yongchun CountyFujian14.8817.1024363377504914
Nanping MunicipalityFujian15.2717.6426329299033188
Fuan MunicipalityFujian14.2817.7222684318584381
Yongan MunicipalityFujian14.9918.2822729392693614
Lianjiang CountyFujian15.7818.8525709400905978
Sanming MunicipalityFujian18.6922.7523057332854147
Minhou CountyFujian19.6923.85454808167012996
Changle MunicipalityFujian25.4730.31400176411411062
Anxi CountyFujian24.9030.6046147773897589
Zhangzhou MunicipalityFujian29.9834.9018013266735252
Longhai MunicipalityFujian29.4836.558874212854018143
Shishi MunicipalityFujian32.5337.02305413385310607
Huian CountyFujian34.4539.94581388180810128
Longyan MunicipalityFujian34.2340.9745677719678818
Fuqing MunicipalityFujian41.1447.746143910734312364
Nan'an MunicipalityFujian41.3448.239795215129523615
Putian MunicipalityFujian57.2071.0510008314390324093
Quanzhou MunicipalityFujian70.3682.59680318734621784
Jinjiang MunicipalityFujian79.8990.8911607913684128985
Fuuzhou MunicipalityFujian132.80154.367943610696334064
Xiamen MunicipalityFujian173.72206.0117192120838466025
Nan'ao CountyGuangdong0.800.9520533438271
LianshanZhuang&Yao A.C.Guangdong1.381.7138085376563
LiannanYao A.C.Guangdong1.481.8938516484622
Xinfeng CountyGuangdong2.623.0658909579666
Luhe CountyGuangdong2.643.218331161571097
Shixing CountyGuangdong2.863.42715110970970
Pingyuan CountyGuangdong2.913.529918166701446
RuyuanYao A.C.Guangdong3.343.878071117141120
Heping CountyGuangdong3.503.978794131441287
Wengyuan CountyGuangdong3.584.0510671147771133
Jiaoling CountyGuangdong3.614.3011194176061122
Yun'an CountyGuangdong3.794.4214272227491483
Dapu CountyGuangdong3.904.4612271283931785
Fengshun CountyGuangdong4.955.8712113280002004
Yu'nan CountyGuangdong5.185.8822662342452386
Dongyuan CountyGuangdong5.115.9523459376283410
Nanxiong MunicipalityGuangdong4.746.03640011116737
Renhua CountyGuangdong5.186.29747711576878
Zijin CountyGuangdong5.476.3220356320272133
Yangshan CountyGuangdong5.046.3711789198371667
Lechang MunicipalityGuangdong5.596.4810661174251815
Deqing CountyGuangdong5.076.5915473223721698
Lianping CountyGuangdong5.946.8011521161861095
Longmen CountyGuangdong5.746.8821708383432708
Wuhua CountyGuangdong5.947.0519332380032229
Fengkai CountyGuangdong5.647.0618450222121581
Guangning CountyGuangdong6.017.1018770261641819
Xuwen CountyGuangdong6.547.7814399264991632
Yangxi CountyGuangdong7.218.7418865326973273
Yunfu MunicipalityGuangdong7.608.8629787418354433
Longchuan CountyGuangdong7.718.8914022207001768
Luoding MunicipalityGuangdong7.909.0616638242752663
Enping MunicipalityGuangdong8.159.3331348463563483
Xingning MunicipalityGuangdong8.259.9026741395382657
Lianzhou MunicipalityGuangdong8.3710.7011688177651971
Wuchuan MunicipalityGuangdong9.0010.8027646436254237
Fogang CountyGuangdong9.3711.4428759418523499
Chaozhou MunicipalityGuangdong10.7811.497637010063611172
Jiexi CountyGuangdong9.7211.8319141334172047
Xinxing CountyGuangdong9.9911.8721010344593001
Huaiji CountyGuangdong9.5811.9118304279302381
Huilai CountyGuangdong10.3412.8223854405733769
Leizhou MunicipalityGuangdong10.6412.8320978482812743
Mei CountyGuangdong10.7912.8530562479503979
Yangdong CountyGuangdong10.5412.9936190561856005
Shanwei MunicipalityGuangdong11.7313.0120541299572172
Meizhou MunicipalityGuangdong11.5813.3416853207122879
Raoping CountyGuangdong11.4113.3528849457903959
Lufeng MunicipalityGuangdong11.5313.7237179587025403
Suixi CountyGuangdong11.6513.9143086766675513
Haifeng CountyGuangdong13.2415.2939775601435569
Heyuan MunicipalityGuangdong12.8315.5934851457345851
Sihui MunicipalityGuangdong17.9016.0648263637267002
Heshan MunicipalityGuangdong14.3616.1748717714796508
Yangchun MunicipalityGuangdong14.6217.8438393560376186
Lianjiang MunicipalityGuangdong15.4318.2831526650267094
Conghua MunicipalityGuangdong15.6018.7355067780327090
Qingxin CountyGuangdong15.1920.0640751681036482
Kaiping MunicipalityGuangdong16.8120.1142475660465932
Xinyi MunicipalityGuangdong17.4520.5911930159581010
Yingde MunicipalityGuangdong15.9620.8947561760569607
Dianbai CountyGuangdong17.8721.2748352684779222
Gaoyao MunicipalityGuangdong17.0421.52677759878210623
Huazhou MunicipalityGuangdong19.1422.7722671400603466
Jieyang MunicipalityGuangdong18.6023.0131729360786065
Taishan MunicipalityGuangdong19.3023.10634381075658929
Jiedong CountyGuangdong18.4323.1646547703538449
Huidong CountyGuangdong21.6625.0563030890778628
Yangjiang MunicipalityGuangdong20.3625.4229563398188396
Gaozhou MunicipalityGuangdong24.1627.8428684403252962
Boluo CountyGuangdong25.2729.4811871816081714087
Puning MunicipalityGuangdong24.2830.12572209823210248
Chaoan CountyGuangdong27.0531.4017962196973277
Shaoguan MunicipalityGuangdong29.3234.06724178638910796
Qingyuan MunicipalityGuangdong31.3735.818649810486115808
Zhaoqing MunicipalityGuangdong32.5338.45419115568310670
Maoming MunicipalityGuangdong44.5156.7332469487997973
Zengcheng MunicipalityGuangdong57.2568.1612953517608520933
Zhanjiang MunicipalityGuangdong62.3976.917179710080622033
Jiangmen MunicipalityGuangdong76.8488.3815842021084324661
Huizhou MunicipalityGuangdong88.83113.0522629827580539995
Shaotou MunicipalityGuangdong102.79119.9520487125824335836
Zhuhai MunicipalityGuangdong103.87120.8615938620246630467
ZhongShan MunicipalityGuangdong156.64185.0730890334202363758
Dongguan MunicipalityGuangdong376.39424.65528739543842120807
Foshan MunicipalityGuangdong482.09565.15588483640247128858
Shenzhen MunicipalityGuangdong820.13958.15384745400431121564
Guangzhou MunicipalityGuangdong840.97987.94577226630177147401
Zixi CountyJiangxi1.411.6515464028331
Tonggu CountyJiangxi1.582.0018586542404
Guangchang CountyJiangxi1.602.0234855975345
Jing'an CountyJiangxi1.872.2335397518591
Shicheng CountyJiangxi2.052.3046099233595
Yihuang CountyJiangxi2.242.7128756113150
Quannan CountyJiangxi2.372.7139197235335
Shangyou CountyJiangxi2.482.7736225413306
Lianhua CountyJiangxi2.342.8118824151195
Le'an CountyJiangxi2.482.8920484746189
Xiajiang CountyJiangxi2.362.9017647142551
Anyuan CountyJiangxi2.642.9638618076390
Xunwu CountyJiangxi2.593.07479014035841
Jinggangshan MunicipalityJiangxi2.553.1148998839906
Dingnan CountyJiangxi2.753.1154559823781
Lichuan CountyJiangxi2.573.1234027475285
Wan'an CountyJiangxi2.633.2920094855174
Xingzi CountyJiangxi2.273.34690616653828
Jinxi CountyJiangxi3.083.62554710762893
Dean CountyJiangxi2.743.73573414662887
Pengze CountyJiangxi3.113.77447914116753
Chongyi CountyJiangxi3.193.8224393957289
Huichang CountyJiangxi3.604.255079137971088
Hengfeng CountyJiangxi3.664.4940728008671
Duchang CountyJiangxi3.604.50457713222731
Yiyang CountyJiangxi3.604.51816414768901
Yujiang CountyJiangxi3.294.52980018754974
Yongxin CountyJiangxi3.794.6226696500243
Jiujiang CountyJiangxi3.764.6411242305272096
Wuyuan CountyJiangxi4.154.777105196741591
Wuning CountyJiangxi4.235.09277115158630
Wannian CountyJiangxi4.245.10703213266671
Yifeng CountyJiangxi4.065.10464216919995
Yanshan CountyJiangxi4.395.1810549191051234
Anyi CountyJiangxi4.505.28692814280612
Chongren CountyJiangxi4.545.3821967510371
Nancheng CountyJiangxi4.425.41753212846692
Xin'gan CountyJiangxi4.355.4245279037294
Fuliang CountyJiangxi4.605.4313099269351547
Nanfeng CountyJiangxi4.535.43704311131587
Suichuan CountyJiangxi4.505.4532257566501
Dayu CountyJiangxi5.255.69630510177623
Wanzai CountyJiangxi4.705.7637418227423
Jishui CountyJiangxi4.886.0041229062374
Longnan CountyJiangxi5.136.019617178281138
Xiushui CountyJiangxi5.056.13348513112930
Fengxin CountyJiangxi5.166.28885517928945
Ruichang MunicipalityJiangxi5.216.297693171801568
Yongxiu CountyJiangxi5.436.3311132310081551
Hukou CountyJiangxi4.826.436897162831497
Ruijin MunicipalityJiangxi5.406.5114468238572332
Yongfeng CountyJiangxi5.256.5121437675578
Yugan CountyJiangxi5.566.571034217045888
Anfu CountyJiangxi5.346.6036058939415
Yushan CountyJiangxi5.276.7610191223081489
Dongxiang CountyJiangxi5.697.036708163451193
Shanggao CountyJiangxi5.947.117244205621299
Luxi CountyJiangxi5.967.3126497626523
Ji'an CountyJiangxi5.927.406644168701352
Gan CountyJiangxi6.247.468908156201438
Xingguo CountyJiangxi6.487.55825812458681
Taihe CountyJiangxi6.297.64593212746871
Ningdu CountyJiangxi6.437.651117212139727
Poyang CountyJiangxi6.748.0314947325001681
Xinfeng CountyJiangxi7.068.40951416072947
Nankang MunicipalityJiangxi7.418.4921806359092598
Shangrao CountyJiangxi6.698.7222328443784282
Yudu CountyJiangxi7.438.957342138671142
Dexing MunicipalityJiangxi7.719.0716312332232297
Yingtan MunicipalityJiangxi6.569.1710005139412163
Shangli CountyJiangxi8.029.896254174151013
Fenyi CountyJiangxi8.0210.105246157591135
Ji'an MunicipalityJiangxi9.0610.8319590330204055
Gao'an MunicipalityJiangxi9.2711.2512617362532484
Shangrao MunicipalityJiangxi9.5511.547490103542344
Yichun MunicipalityJiangxi9.8911.6214335384633289
Leping MunicipalityJiangxi12.1914.3414164264111758
Guangfeng CountyJiangxi12.8015.1011024158901149
Zhangshu MunicipalityJiangxi11.8015.219563224482022
Jinxian CountyJiangxi14.3216.5214227253561474
Xinjian CountyJiangxi14.7517.5240679710239311
Fuzhou MunicipalityJiangxi17.1320.3526907439874226
Guixi MunicipalityJiangxi15.4820.5818772289583099
Ganzhou MunicipalityJiangxi18.3520.9130439401557244
Fengcheng MunicipalityJiangxi20.0524.0930459563654652
Jingdezhen MunicipalityJiangxi19.6126.3818471280613411
Nanchang CountyJiangxi25.5330.60577178725710148
Pingxiang MunicipalityJiangxi26.1032.0316906322022747
Jiujiang MunicipalityJiangxi39.0347.0827771471496168
Xinyu MunicipalityJiangxi40.3953.0232904645396765
Nanchang MunicipalityJiangxi124.65150.10527707245315290
JingningShe A.C.Zhejiang2.292.6841805656435
Qingyuan CountyZhejiang2.633.1733984735326
Yunhe CountyZhejiang2.803.3558098944990
Dongtou CountyZhejiang3.003.4422784129613
Taishun CountyZhejiang3.413.9860769417781
Wencheng CountyZhejiang3.524.058324146851684
Songyang CountyZhejiang3.904.7510180155561662
Pan'an CountyZhejiang4.074.826247124791050
Suichang CountyZhejiang4.875.777558147421349
Shengsi CountyZhejiang5.345.89354447911115
Longquan MunicipalityZhejiang5.166.2610406130741288
Kaihua CountyZhejiang5.917.0210055175181740
Changshan CountyZhejiang6.207.5512293234182209
Xianju CountyZhejiang8.4010.1015757303302580
Sanmen CountyZhejiang8.8910.6620153329373036
Jinyun CountyZhejiang9.1111.2217004269172420
Qingtian CountyZhejiang9.4211.4318743330253177
Chun'an CountyZhejiang10.0711.7513738272332126
Longyou CountyZhejiang9.7711.8315369326572728
Tiantai CountyZhejiang10.0711.8619741334653406
Daishan CountyZhejiang10.2412.819050174401797
Wuyi CountyZhejiang10.8012.9619530355903640
Pujiang CountyZhejiang11.0813.1619795372883410
Jiangshan MunicipalityZhejiang14.1117.0220167387612876
Lishui MunicipalityZhejiang14.3917.5230283483787021
Lanxi MunicipalityZhejiang14.7418.0324391446454390
Jiande MunicipalityZhejiang16.2218.9625266499043865
Anji CountyZhejiang15.9019.0132444658356482
Tonglu CountyZhejiang16.7319.7927372497876646
Pingyang CountyZhejiang17.3920.2626927498096336
Yongjia CountyZhejiang17.6220.5133419556455501
Xinchang CountyZhejiang18.7321.5120065330234450
Fenghua MunicipalityZhejiang19.3922.5132874630076300
Haiyan CountyZhejiang21.0223.8330902576296106
Deqing CountyZhejiang20.3324.0238101758387020
Cangnan CountyZhejiang21.7225.4835920611888842
Xiangshan CountyZhejiang23.6127.1729780553277088
Shengzhou MunicipalityZhejiang23.1727.3434342601694537
Jiashan CountyZhejiang22.6427.6136047689916407
Ninghai CountyZhejiang23.6227.8738738676346931
Changxing CountyZhejiang24.0428.39516459512710249
Dongyang MunicipalityZhejiang24.4028.69514628542913501
Lin'an MunicipalityZhejiang23.5028.7727757536835031
Yuhuan CountyZhejiang24.4830.8227406415676498
Yongkang MunicipalityZhejiang26.0931.0537850628496883
Quzhou MunicipalityZhejiang26.4832.6238721696036272
Linhai MunicipalityZhejiang27.7032.80673541012339949
Pinghu MunicipalityZhejiang28.3734.05426157944010226
Jinhua MunicipalityZhejiang34.0540.30549549590812331
Tongxiang MunicipalityZhejiang34.0340.93503299624610185
Fuyang MunicipalityZhejiang35.2541.575095110068912054
Shangyu MunicipalityZhejiang36.8243.63571649622011564
Haining MunicipalityZhejiang37.5345.585776910311210130
Zhoushan MunicipalityZhejiang37.9545.65508919863412725
Ruian MunicipalityZhejiang38.2845.72598289035615623
Shaoxing MunicipalityZhejiang40.7646.6710638151664355
Leqing MunicipalityZhejiang42.3549.58570769261211524
Yuyao MunicipalityZhejiang48.9256.798466414899723042
Jiaxing MunicipalityZhejiang48.2257.828615014641216040
Wenling MunicipalityZhejiang50.2058.156959710436915206
Huzhou MunicipalityZhejiang50.6159.648692415548618378
Yiwu MunicipalityZhejiang52.3861.998373112051928499
Zhuji MunicipalityZhejiang52.7762.157338612561011760
Cixi MunicipalityZhejiang62.4975.749770316587031900
Shaoxing CountyZhejiang65.5877.619336815275525479
Taaizhou MunicipalityZhejiang72.9385.289465213408924225
Wenzhou MunicipalityZhejiang105.43119.637806510394930137
Ningbo MunicipalityZhejiang254.90306.2222429035990993111
Hangzhou MunicipalityZhejiang406.99474.08361647552131131346
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Figure 1. The 31 provinces in China’s mainland for analysis in this study. Note: this map is not a full map of China and only shows the regions used in this study, so that Hong Kong, Macao, all the islands and the ocean are not illustrated in this map.

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Figure 1. The 31 provinces in China’s mainland for analysis in this study. Note: this map is not a full map of China and only shows the regions used in this study, so that Hong Kong, Macao, all the islands and the ocean are not illustrated in this map.
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Figure 2. The 393 county level regions in the five provinces for analysis in this study.

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Figure 2. The 393 county level regions in the five provinces for analysis in this study.
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Figure 3. DMSP-OLS (F16 satellite) nighttime light imagery of China’s land in 2009.

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Figure 3. DMSP-OLS (F16 satellite) nighttime light imagery of China’s land in 2009.
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Figure 4. DMSP-OLS (F18 satellite) nighttime light imagery of China’s land in 2010.

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Figure 4. DMSP-OLS (F18 satellite) nighttime light imagery of China’s land in 2010.
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Figure 5. NPP-VIIRS nighttime light imagery of China’s land in 2012. Note: the VIIRS imagery has a wide radiometric detection limit, so that there is a number of pixels with values greater than 63, but pixels of this type only occupy approximately 0.05% of all the pixels with positive values, so the pixel values larger than 63 is set to the brightest color in the map to keep it consistent with the DMSP-OLS data for illustration.

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Figure 5. NPP-VIIRS nighttime light imagery of China’s land in 2012. Note: the VIIRS imagery has a wide radiometric detection limit, so that there is a number of pixels with values greater than 63, but pixels of this type only occupy approximately 0.05% of all the pixels with positive values, so the pixel values larger than 63 is set to the brightest color in the map to keep it consistent with the DMSP-OLS data for illustration.
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Figure 6. The nighttime light in Guangzhou municipality: (a) DMSP-OLS (F16 satellite) data in 2009, (b) DMSP-OLS (F18 satellite) data in 2010, and (c) NPP-VIIRS data in 2012.

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Figure 6. The nighttime light in Guangzhou municipality: (a) DMSP-OLS (F16 satellite) data in 2009, (b) DMSP-OLS (F18 satellite) data in 2010, and (c) NPP-VIIRS data in 2012.
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Figure 7. The scatter diagram of the regression variables in provincial regions and in county regions: (a) DMSP-OLS (satellite DMSP-F16) data in 2009 versus GRP data in 2009 of the provincial regions, (b) DMSP-OLS (satellite DMSP-F18) data in 2010 versus GRP data in 2010 of the provincial regions, (c) NPP-VIIRS data in 2012 versus GRP data in 2010 of the provincial regions, (d) DMSP-OLS (satellite DMSP-F16) data in 2009 versus GRP data in 2009 of the county regions, (e) DMSP-OLS (satellite DMSP-F18) data in 2010 versus GRP data in 2010 of the county regions and (f) NPP-VIIRS data in 2012 versus GRP data in 2010 of county regions.

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Figure 7. The scatter diagram of the regression variables in provincial regions and in county regions: (a) DMSP-OLS (satellite DMSP-F16) data in 2009 versus GRP data in 2009 of the provincial regions, (b) DMSP-OLS (satellite DMSP-F18) data in 2010 versus GRP data in 2010 of the provincial regions, (c) NPP-VIIRS data in 2012 versus GRP data in 2010 of the provincial regions, (d) DMSP-OLS (satellite DMSP-F16) data in 2009 versus GRP data in 2009 of the county regions, (e) DMSP-OLS (satellite DMSP-F18) data in 2010 versus GRP data in 2010 of the county regions and (f) NPP-VIIRS data in 2012 versus GRP data in 2010 of county regions.
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Figure 8. The scatter diagram of the regression analysis in the county regions after discarding Guangzhou and Shenzhen as outliers: (a) DMSP-OLS data in 2009 versus the GRP data in 2009 of the county regions, (b) DMSP-OLS data in 2010 versus the GRP data 2010 of the county regions, and (c) NPP-VIIRS data in 2012 versus the GRP data in 2010 of the county regions.

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Figure 8. The scatter diagram of the regression analysis in the county regions after discarding Guangzhou and Shenzhen as outliers: (a) DMSP-OLS data in 2009 versus the GRP data in 2009 of the county regions, (b) DMSP-OLS data in 2010 versus the GRP data 2010 of the county regions, and (c) NPP-VIIRS data in 2012 versus the GRP data in 2010 of the county regions.
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Table Table 1. The numbers of the county level regions in the five provinces.

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Table 1. The numbers of the county level regions in the five provinces.
Provincial RegionNumber of Regions

MunicipalityCounty and County Level CityAll
Anhui176178
Fujian95867
Guangdong196988
Jiangxi118091
Zhejiang115869
All five provinces67326393
Table Table 2. Spatial resolution of the nighttime light imagery in this study

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Table 2. Spatial resolution of the nighttime light imagery in this study
YearSatellite/SensorSpatial Resolution

Original ImageryArchived CompositesResampled Imagery in this Study
2009DMSP(F16)/OLS2700 m30 arc second500 m
2010DMSP(F18)/OLS2700 m30 arc second500 m
2012NPP/VIIRS742 m15 arc second500 m
Table Table 3. The total nighttime light (TNL) and gross regional product (GRP) for the regression analysis of data from different satellite sensors and acquisition year.

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Table 3. The total nighttime light (TNL) and gross regional product (GRP) for the regression analysis of data from different satellite sensors and acquisition year.
RegionIndependent Variable (TNL)Dependent Variable (GRP)

SensorYearYear
31 provincial level regionsDMSP-OLS (F16)20092009
DMSP-OLS (F18)20102010
NPP-VIIRS20122010

393 county level regionsDMSP-OLS (F16)20092009
DMSP-OLS (F18)20102010
NPP-VIIRS20122010
Table Table 4. Real GRP, predicted GRP and relative error in the provincial regions. RG represents real GRP, PG represents predicted GRP and RE represents the relative error.

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Table 4. Real GRP, predicted GRP and relative error in the provincial regions. RG represents real GRP, PG represents predicted GRP and RE represents the relative error.
RegionGRP in 2009 and the Prediction from DMSP-OLS DataGRP in 2010 and the Prediction from DMSP-OLS DataGRP In 2010 And The Prediction From NPP-VIIRS Data



RGPGRE (%)RGPGRE (%)RGPGRE (%)
Anhui1,006921−8.41,2361,51922.91,2361,52223.1
Beijing1,215606.9−50.11,411640−54.61,411839−40.6
Chongqing653353.5−45.9793456−42.5793627−21.0
Fujian1,224947.9−22.51,4741,164−211,4741,65112.0
Gansu339517.752.841264656.841263754.5
Guangdong3,9482,961.5−25.04,6013,119−32.24,6014,233−8.0
Guangxi776904.616.69571,139199579943.8
Guizhou391378.5−3.346052514.146057124.1
Hainan165265.360.420637280.620637883.5
Hebei1,7242,558.648.42,0392,92843.62,0392,0781.9
Heilongjiang8592,304.9168.41,0372,463137.51,0371,45640.4
Henan1,9481,995.82.52,3092,4817.42,3092,032−12.0
Hubei1,296636.7−50.91,5971,137−28.81,597984−38.4
Hunan1,306560.4−57.11,604898−441,604879−45.2
Jiangsu3,4462,752.7−20.14,1433432−17.24,1434,2091.6
Jiangxi766498.5−34.9945791−16.3945627−33.6
Jilin7281,143.357.18671,37558.686797913.0
Liaoning1,5211,602.45.31,8461,843−0.21,8461,815−1.7
Neimeng9741,262.729.61,1671,50929.31,1671,34815.5
Ningxia13526797.316930077.5169438159.2
Qinghai108167.655.013521861.513524077.9
Shandong3,3903,359.8−0.93,9173,773−3.73,9173,077−21.5
Shanghai1,505504.8−66.51,717551−67.91,7171,410−17.9
Shanxi7361,379.687.59201,70184.99201,50463.5
Shaanxi8171,127.238.01,0121,45143.41,0121,57255.3
Sichuan1,4151,032.1−27.11,7191,293−24.81,7191,698−1.2
Tianjin752557.2−25.9922594−35.6922876−5.0
Tibet4458.432.3518464.751107109.0
Xinjiang428935.4118.75441,4091595441,656204.4
Yunnan617949.153.87221,23470.97221,22469.5
Zhejiang2,2991,604.6−30.22,7722,218−202,7722,9667.0
Table Table 5. Different levels of accuracies of the predicted GRP.

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Table 5. Different levels of accuracies of the predicted GRP.
Region and DataPercent of Relative Error of the Predicted GRP (%)

High AccuracyModerate AccuracyWrong
Provincial regionsDMSP-OLS 200929.0329.0341.94
DMSP-OLS 201035.4825.8138.71
NPP-VIIRS 201254.8416.1329.03

County regionsDMSP-OLS 200918.3222.9058.78
DMSP-OLS 201010.699.9279.39
NPP-VIIRS 201234.1030.0335.87
Table Table 6. Different levels of accuracies of the predicted GRP by discarding the two outliers.

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Table 6. Different levels of accuracies of the predicted GRP by discarding the two outliers.
CountiesPercent of Different Relative Error of Predicted GRP (%)

High AccuracyModerate AccuracyWrong
DMSP 200936.0622.7641.18
DMSP 201019.1818.4162.40
VIIRS 201242.9729.4127.62
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