1. Introduction
Urbanization, including the development of high-density cities as well as suburban sprawl and the expansion of exurban areas at the urban–rural fringe, is a dominant driver of land change across the globe [
1]. Associated with land use/cover change, urbanization is widely recognized as an important factor in resource scarcity [
2], public health [
3], environmental degradation [
4], energy consumption [
5,
6], climate change [
7,
8], etc. As the most populous country in the world, China has experienced rapid urbanization since its economic reforms started in 1978. For instance, the urbanization level of China increased from 17.9% in 1978 to 60.6% in 2019 [
9,
10]. Uncontrolled urban sprawl caused by rapid urbanization promotes the interaction between urban areas and urban boundaries (suburban or rural areas) and consequently leads to adverse land use conflicts at the three interfaces of the “urban core-suburban-rural” (USR) triad structure [
11,
12]. As such, in addition to mapping urban areas alone, long-term and consistent mapping of the three subcategories of USR areas is essential to track, understand, and simulate the interaction of urban–suburban–rural areas, pathways of urbanization, and their corresponding impact on landscape fragmentation, environmental degradation, and even sharp social contradictions [
11].
Over the past few decades, urban extent and its dynamics—rather than USR—have been commonly mapped by several remote sensing imagery-based approaches [
13]. The widely used satellite-based remote sensing data include NTL [
14,
15], medium spatial resolution imagery (Landsat, MODIS, SPOT, CHRIS/Proba, etc.) [
16,
17,
18,
19,
20], and fine resolution imagery (IKONOS, QuickBird, etc.) [
21,
22]. However, these approaches either focus on extracting one subcategory (generally urban) in the USR structure or roughly categorize USR as one classification of land use, such as an impervious surface or residential area. Hence, such approaches are limited in their ability to depict the growing USR interface conflicts among urban core, suburban, and rural areas [
23].
Satellite-based NTL observations show potential for mapping the USR structure due to their capability to discern the distinctions between human activities [
12,
24,
25,
26,
27,
28]. NTL images derived from various satellite sensors—including the Defense Meteorological Satellite Program-Operational Linescan System (DMSP-OLS), the Visible Infrared Imaging Radiometer Suite (VIIRS) sensor onboard the Suomi National Polar-orbiting Partnership satellite (Suomi-NPP), Luojia 1-01, etc.—have been widely applied in urban dynamics studies due to the significant correlations between NTL intensity and human activity intensity at regional to global scales [
29,
30,
31]. Taking the threshold-based method as an example, it is a significant type of urban mapping and urbanization estimation by determining the optimal NTL thresholds using which one can separate the urban from its surrounding surface types (e.g., suburban and rural) [
32]. Such a method mainly relies on the apparent spatial NTL gradient variation between urban areas and other areas [
33,
34,
35]. Accordingly, the NTL gradient also presents distinctions between urban and suburban and suburban and rural areas [
13,
36], which can be applied for retrieving the USR structure from NTL imagery.
Although satellite-based NTL observations show potential for mapping USR structures, a long-time series of USR dynamics (1992–present) for better unveiling human development and urbanization processes from regional to global scales is still lacking. This is due to the lack of sufficient attention paid by remote sensing researchers to the impact of urban–suburban interactions. For instance, Ma et al. (2015) proposed a spatially quantitative approach for partitioning DMSP/OLS NTL images into five types of urban subregions based on the quadratic relationship between the pixel-level NTL and the corresponding NTL gradient. Zhou et al. (2018) proposed a sequentially quantile-based approach to retrieve urban extent from DMSP NTL imagery by eliminating rural and suburban areas at the global scale. Although the abovementioned approaches refer to NTL gradients among subcategories of residential areas, they still focus on urban extent rather than USR structure. Huang et al. [
23] proposed an improved multiple iterating quantile approach to gradually delineate the scope of three USR subcategories from DMSP NTL imagery (1995–2013). Hence, this is a good NTL-based attempt to retrieve the USR structure. However, it suffers from the limitation of NTL inconsistency since it is based on different sources, such as DMSP (1992–2013) and VIIRS (2012–2020), and is restricted to a short period of 1995–2013 [
37]. Specifically, due to the significant differences in the sensor performance and overpass time of DMSP and VIIRS, the spatiotemporal trends and variations of the NTL time series are inconsistent and incomparable [
38]. This makes the USR approach invalid for VIIRS. It is also the key problem in long-term urban-related studies based on NTL data from DMSP and VIIRS. To overcome this limitation, the common solution is to generate a consistent NTL dataset by integrating DMSP and VIIRS [
39,
40]. Taking a harmonized global nighttime light dataset 1992–2018 [
41] as an example, VIIRS data were simulated as DMSP-like NTL based on the sigmoid function relationship between VIIRS data and DMSP data in 2013. In addition, Zhao et al. (2020) estimated urban dynamics based on the consistent DMSP-like NTL data (1992–2018) conducted by stepwise calibration approach and integration approach [
32]. However, such conversions of VIIRS NTL into DMSP-like NTL are likely to over-adjust the spatial trends and variations in the NTL gradient distribution in the original VIIRS to a defined probability distribution (e.g., logarithmic distribution). It may be negligible in delineating the boundary of an integrated object such as urban extent but has reduced accuracy when mapping the more complicated USR structure involving three subcategories. Therefore, it is necessary to keep the probability distribution of VIIRS unchanged as far as possible to present the ground-true NTL gradient in USR. In addition, considering the relatively small NTL variations in USR subcategories, a general approach to portraying the USR structure accurately from different NTL sources, such as DMSP and VIIRS, without additional information/datasets as references is urgently needed for capturing the USR transition from NTL observations.
Considering the long-standing neglect of USR structure and interactions among each subcategory by remote sensing researchers and the current limitations of applying an urban mapping approach in USR structure retrieval from NTL data, we propose a general approach to delineate the long-term extent of USR subcategories using both DMSP and VIIRS NTL (1992–2020). We then analyze the USR dynamics of 19 cities in China with different development levels. Including a slight adjustment of VIIRS to DMSP to generate consistent NTL data, our approach is developed by improving the existing methods using the DMSP stable NTL [
13,
23] with the Mann–Kendall method [
42,
43]. The remainder of this article details the study area and dataset (
Section 2), the major parts of the USR mapping approach (
Section 3), the results and evaluation (
Section 4), the discussion of the approach and results (
Section 5), and conclusions (
Section 6).
2. Study Area and Dataset
In our case study, 19 cities in China (Beijing, Shanghai, Tianjin, Guangzhou, Wuhan, Chengdu, Xi’an, Nanjing, Zhengzhou, Changchun, Urumqi, Qingdao, Taiyuan, Datong, Liaocheng, Bengbu, Yingkou, Shangqiu, Sanya) with different levels of urbanization and socioeconomic development are chosen. China has been experiencing rapid urbanization since the 1980s with an uneven process of urbanization in such a large area. Hence, it can serve as an ideal experimental setting to evaluate the performance of our USR mapping approach.
Table 1 shows the basic information of the 19 cities, including the specific population and the economic location about the cities. For each city, we use the identical range of its administrative division as the area where the USR extraction was applied.
The two primary datasets used for USR extraction in this study were DMSP/OLS stable NTL images (source:
http://www.ngdc.noaa.gov/dmsp (accessed on 16 October 2021)) and NPP-VIIRS annual VNL V2 NTL images (source:
https://eogdata.mines.edu/products/vnl/ (accessed on 8 November 2021)) [
44]. The annual cloud-free-composited stable NTL datasets spanning the years 1992–2013, obtained by DMSP, were generated from five individual sensors—including F10 (1992–1994), F12 (1995–1996), F14 (1997–2003), F16 (2004–2009), and F18 (2010–2013). The annual composited VIIRS NTL datasets spanning 2012 to 2020 produced from monthly cloud-free average radiance grids were utilized to build the consistent time series of NTL imagery for USR extraction from 1992 to 2020. Two auxiliary datasets—namely, the water mask based on the MODIS product MOD44W (source:
http://earthexplorer.usgs.gov/ (accessed on 2 November 2021)) and the gas flare mask [
45]—were applied to remove water and gas flare pixels. Six time frames of land use maps of China—including 1995, 2000, 2005, 2010, 2015, and 2020—produced by the Chinese Academy of Sciences, were derived from Landsat images with a 100 m spatial resolution based on visual interpretation, were used for the validation of urban cores in this study [
46]. The time series of MODIS land cover product MCD12Q1 from 2001 to 2019 annually (source:
https://search.earthdata.nasa.gov/ (accessed on 5 November 2021)) were downloaded for the validation of urban core and suburban areas in this study [
47]. The area of extracted USR extents and the validating land use maps were calculated based on the same coordinate system: Krasovsky 1940 Albers. All datasets were preprocessed to 30 arc second spatial resolution to match the NTL sequence imagery. Detailed information on the datasets is shown in
Table 2.
3. Methods
In this paper, we define the three subcategories of USR based on the variations of NTL intensity as the NTL intensity is positively correlated with human activity intensity. To achieve rapid and semiautomatic extraction of USR, an improved method was developed for iteratively determining the specific values of each subcategory of USR, which was proven to be effective for both DMSP/OLS and VIIRS-NPP data. First, to eliminate the discontinuity between two sets of NTL data from different sensors, the VIIRS-NPP data were enhanced by a maximum–minimum (0–63) piecewise linear stretch to match the value ranges of the DMSP/OLS data. Second, a multiple iteration approach based on the combination of the NTL gradient and the Mann–Kendall-inspired algorithm was used to progressively extract the annual USR extent in the study area from 1992 to 2020. Finally, a temporal consistency check was applied to the USR extent obtained from the second step for deriving more consistent and reasonable USR dynamics.
Figure 1 shows the general flow chart for retrieving the extent of the three USR subcategories. More details of each step can be found in the following.
3.1. Data Preparation
The most basic definition of USR is a place where people live. That is, uninhabited places such as water and swaps should be removed first. To eliminate the pixels with nighttime light values greater than 0 caused by water, we first used the MODIS product MOD44W with a spatial resolution of 250 m to preprocess the original DMSP and VIIRS data. Compared with DMSP data with oversaturation, VIIRS data capture weaker nighttime lights and render them in a larger range of magnitude, which makes the pixel value of the most densely populated areas of human activity in the actual urban land far higher than that of the other urban land. Therefore, we performed a maximum–minimum (0–63) piecewise linear stretch on the initial VIIRS data to compress the exorbitant lighting values of some pixels in the actual urban land (
Figure 2) to match the value ranges of DMSP/OLS data without changing the real USR distribution of VIIRS data. The piecewise linear stretch is expressed in the following equation:
where
and
represent the 2nd percentile and 98th percentile of the input VIIRS data, respectively.
means the value of each pixel in the input VIIRS data and
means the output value of the input pixel. The operator “
int” is the antonomasia of the rounding function.
3.2. USR Extraction Based on Gradient Mutation Detection
We developed an innovative algorithm based on the gradient mutation detection of NTL images and the regulation of USR distribution to progressively determine the value of the thresholds between urban core, suburban, and rural areas. When the water area has already been removed, the remaining pixels with values larger than zero will still include areas where no people live because of the blooming effect of NTL images, especially in the DMSP data. To remove such unpopulated areas, we eliminated the pixels with values smaller than the 20th percentile of the DMSP data in the original DMSP data and the pixels with values smaller than the 5th percentile of the VIIRS data in the original VIIRS data because there are fewer blooming effects in the VIIRS data. Then, we assumed that all the remaining pixels belong to residential areas. This was then considered to be the whole USR extent for the next gradient mutation detection.
To fully recognize the gradient variation in the USR region extracted from NTL images, the whole USR extent was first used to construct a quantile curve that revealed the variation in NTL intensity with percentiles from 0 to 100 in the NTL image and along with the corresponding reference line directly connecting the two endpoints of the quantile curve [
13]. Gradient mutation detection hypothesizes that the values of the pixels around the boundary lines between the urban core and suburban, and suburban and rural areas change rapidly and this leads to a sudden drop in the corresponding position of the quantile curve. For one potential extent of USR in the administrative region of a city, there can be several turning points corresponding to one sudden drop in the quantile curve. However, only the digital number (DN) values of two of them are needed, which will represent the threshold values of boundary lines segregating urban core and suburban or suburban and rural areas. The key to gradient mutation detection is determining the threshold values most efficiently.
To shorten the search time, we first established a list of candidate turning points based on the quantile curve to remove the redundant points. The two optimal threshold values of turning points can be considered as the mutation point in the quantile curve. In contrast to the perceptual determination with the maximum gap between the quantile curves and reference lines [
13,
23,
32], the mutation point is redefined in this paper from the standpoint of derivatives. Based on the reference line, the quantile curve can be approximately regarded as a combination of a convex function and a concave function, the proportions of which in the whole quantile curve manifest the residential type of the input NTL image. This then determines where the mutation point lies (
Figure 3). It is more significant to map USR than urban extent only. In this paper, we used a heuristic algorithm originating from the Mann–Kendall method, which is widely used for trend testing in hydrology [
48,
49], to determine the mutation point. The Mann–Kendall-inspired algorithm is expressed in the following equation:
and
represent the DN values of two neighboring points in the quantile curve. The number of the points in one quantile curve is
. The operator “
” is the antonomasia of calculating average value; The operator “
” is the antonomasia of calculating variance. The forward sequence
was computed by the formulas listed above with the input DN values ranking according to the percentile from 0 to 100, while the inverse sequence
was computed in the same way using the inverse DN values ranking according to the percentile from 100 to 0 as input. The mutation point of the original data was obtained by finding the intersection point of the forward and inverse sequences on the same abscissa.
Figure 3 shows the three types of constructed quantile curves (column I), the specific USR structures corresponding to each quantile curve (column II), and the mutation points calculated by the Mann–Kendall-inspired algorithm (column III). When the quantile curve is regarded as a concave function (
Figure 3(aⅠ)), it refers to the USR structure with blooming area (
Figure 3(aⅡ)) and the point with the first derivative is zero on the quantile curve which is detected as the mutation point (
Figure 3(aⅢ)); when the quantile curve is regarded as a combination of a convex function and a concave function (
Figure 3(bⅠ)), it refers to the USR triad structure (
Figure 3(bⅡ)) and the point with the second derivative is zero on the quantile curve is detected as the mutation point (
Figure 3(bⅢ)); when the quantile curve is regarded as a convex function (
Figure 3(cⅠ)), it refers to the urban core–suburban (US) structure (
Figure 3(cⅡ)) and the point with the first derivative is zero on the quantile curve is detected as the mutation point (
Figure 3(cⅢ)).
Combining all the steps mentioned above, the overall process of USR extraction can be divided into four major steps: (1) construct quantile curves based on the NTL image to be divided; (2) calculate all the candidate turning points in the quantile curve; (3) detect the mutation point using the heuristic algorithm originating from the Mann–Kendall method; and (4) determine the turning point with the closest DN value to that of the mutation point. According to the hypothesis of the three subcategories of the USR structure of cities varying greatly in population density and human activity intensity, this heuristic approach can retrieve urban core, suburban, and rural areas with two iterations. The first iteration separates the rural areas, and the second identifies the suburban areas, while the rest of the NTL image is recognized as the urban core. However, the blooming effect of the DMSP/OLS NTL data, which narrows the gap in the NTL intensity between the urban core and suburban areas in the cities with a low level of economic development in a specific year, makes it hard to distinguish the USR structure properly within two iterations when the inherent USR structure of cities was replaced by “urban core-suburban-blooming-rural” (USBR) NTL distribution in corresponding DMSP/OLS NTL images. To solve this problem, we used the percentile of the intersection point of the quantile curve and the reference line of the total USR range to determine whether the third iteration is needed to extract the real urban core (
Figure 4). The USR range with a percentile of the intersection point greater than 70 is regarded as the rural-dominated type with fewer urban pixels, which is more likely to display the USBR NTL distribution. Thus, the second iteration separated the blooming area instead of the suburban area and the third iteration is needed.
3.3. Temporal Consistency Check
The time series DMSP NTL images acquired by different sensors lack time continuity due to the influence of many external factors, such as satellite orbit drift, along with the fact that the satellite sensors are not corrected by satellite radiometric correction. This will affect the identification of the DMSP NTL distribution and the number of iterations for USR extraction. Therefore, we first applied temporal filtering with a window size of 7 to revise the number of iterations for USR extraction for each city based on DMSP NTL imagery. Then, a temporal consistency check and logical reasoning modification based on the irreversibility of the urbanization process were employed for each pixel in both the DMSP and VIIRS USR ranges to obtain a more reliable USR-extent sequence with less temporal inconsistency from 1992 to 2020. A detailed description of the method can be found in Li et al. (2015).
5. Discussion
5.1. Less Adjustment of VIIRS NTL in Harmonized NTL Time Series Datasets
Long-term NTL time series data fill the gap of accessible data for retrieving time series of USR subcategories in a semiautomatic and more rapid way, among which the DMSP NTL imagery provides the data from 1992 to 2013, while the VIIRS NTL imagery makes the data spanning the years 2012–2020 available. However, there are some disadvantages to mapping USR extents using DMSP NTL data owing to the existing blooming and oversaturation effect [
51], which exaggerates the extracted extent of suburban and rural areas compared with that from VIIRS NTL data. Using VIIRS NTL data of higher spatial resolution for USR extraction can improve the accuracies of the results to some extent (e.g., the average KC of urban areas extracted in Shangqiu from VIIRS NTL data reached 0.64, while that from DMSP NTL data was only 0.42 as shown in
Table 4). In this study, we retrieved the USR based on their NTL gradient, which was directly displayed in VIIRS NTL datasets. Taking the processed VIIRS dataset proposed by Zhao et al. (2020) as an example, although over adjustments of VIIRS show sigmoid consistent temporal trends as DMSP NTL, it will lose the VIIRS details of USR with little area and introduce new uncertainty such as overestimation [
39].
Figure 12 shows the comparison of the VIIRS variation after logarithmic transformation conducted by Zhao et al. and the adjusted VIIRS in this study. Compared to the histogram of the original VIIRS data (
Figure 12a), the VIIRS variation after linear stretching in this paper basically follows the integral distribution pattern of the raw VIIRS imagery, while the VIIRS variation after logarithmic transformation shows a significant inconsistency with the raw VIIRS imagery in the middle- and low-DN areas (the red rectangle in
Figure 12c). The logarithmic transformation indeed suppresses the sharp radiance jump between urban core areas and suburban and rural areas; however, the VIIRS radiance in suburban and rural areas tends to be homogenized. Compared to the histogram of the original DMSP data in the same year (
Figure 12d), the VIIRS variation after linear stretching displays the same distribution in the high-DN areas (the green ellipses in
Figure 12b,d), which might help to detect the urban core areas with high DN values. Hence, we propose that less adjustment of VIIRS to maintain its spatial and radiometric advantage is significant to USR retrieval for long-term consistent NTL datasets.
5.2. Uncertainty in the USR Extraction
The key feature of our approach for USR extraction lies in the combination of visualizing the gradient variation of NTL imagery using the quantile curve and mutation detection based on a heuristic algorithm originating from the Mann–Kendall method, which implied a hidden precondition that the result of the mutation detection must exactly match the corresponding DN values of the turning points obtained from the quantile curve. However, a spot of unmatching DN value pairs with gaps ranging from 1 to 4 was found during the experiments, and the solution to this was to seek out the turning point with the most proximate DN value to that of the mutation point; that is, the final DN value of the threshold was determined by the constructed quantile curve. On the other hand, determining the final DN value of the threshold based on a comparison of the DN values of the mutation point and the selected turning point, the results changed slightly. This is the optional fine-tuning for the USR extraction. The concrete practice of fine-tuning is to choose the DN value of the mutation point as the final threshold when the gap between the DN values of two points is greater than 1; otherwise, the DN value of the selected turning point is chosen as the final threshold. The results show that the fine-tuning technique can marginally ameliorate both overestimation (e.g., Changchun and Sanya in 2010) and underestimation (e.g., Wuhan in 2005) of the extracted urban area from DMSP datasets by acquiring a greater or smaller threshold.
The Mann–Kendall-based mutation point algorithm in this study is precise with mathematical implications. It consists of searching for the point at which the first or the second derivative is zero in the quantile curve according to different situations (
Figure 3).
Figure 13 shows the comparisons of extracted USR extent in Beijing with the quantile approach improved by Huang et al. to retrieve the extent of USR subcategories from NTL images, which just calculates the point on the quantile curve located farthest from the reference line [
23].
Figure 13a–c are the threshold values of blooming, suburban, and urban areas using the Mann–Kendall-based mutation point algorithm, respectively, and
Figure 13g displays the corresponding USR results. Meanwhile,
Figure 13d–f are the threshold values of blooming, suburban, and urban areas based on the quantile approach, respectively, and
Figure 13h displays the corresponding USR results. The quantile approach separating the USR subcategories based on the point on the quantile curve located farthest from the reference line can capture the point with the first derivative being zero when the patterns of NTL variations are the same as (d) or (f). However, it failed to identify the accurate mutation point regarding situations such as (e), as the retrieved suburban area was nearly the same as the retrieved urban area (
Figure 13h), while the Mann–Kendall-based algorithm proposed in this paper succeeded in calculating the point with the second derivative of zero in the same situation (
Figure 13b) and thus delineated the correct extent of the suburban area (
Figure 13g).
5.3. Limitation
Compared with retrieving the urban core and suburban area from NTL datasets, there are challenges in mapping rural settlements based on NTL due to the discrete spatial distribution and the backward economy of rural areas. First, the basis of the close relation between NTL and human settlements lies in the NTL imagery seizing artificial lights at night, which are regarded as one of the most important signs of human activities. However, there are fewer detectable lights in rural settlements where people live than in metropolitan areas with higher economic development because of the traditional routine of “work at sunrise and rest at sunset” and the possible power shortage occurring in rural areas. Second, the scattered rural settlements are too small to be detected by the NTL sensors due to the coarse spatial resolution of NTL imagery (DMSP/OLS: 30 arc seconds, NPP-VIIRS: 15 arc seconds), leading to a resulting omission of part of the rural areas. Meanwhile, the rural areas for validation in 30 m or 100 m land use maps also exist as scattered areas, which further makes it difficult to compare with the extracted scope of rural areas from both DMSP and VIIRS NTL datasets when using the single-threshold method. In addition, some of the cities in China without the USR triad structure (e.g., Shenzhen did not have rural areas) need another specific strategy for urban core–suburban (US) extraction.
As for the data itself, the existing blooming effect and saturation problem of DMSP data might reduce the accuracy of the proposed method and lead to minor inconsistency with VIIRS data. For example, the blooming effect of DMSP NTL lightened the surroundings of aggregated rural settlements and thus obtained an overestimated scope of the whole area containing all rural settlements. As a result, the rural areas extracted from DMSP/OLS were apparently larger than that from NPP-VIIRS. Moreover, the linear stretch rounding the DN values of VIIRS as integers to match the data range of DMSP might probably miss the subtle changes of NTL intensity in low-DN regions. Therefore, the strategy of linear stretch for integrating DMSP and VIIRS NTL data can be further improved.
6. Conclusions
In this study, we developed an innovative approach based on the combination of NTL gradient variation and mutation detection using a heuristic algorithm inspired by the Mann–Kendall method. The goal is to map the extent of three USR subcategories from long-term NTL time series data spanning 1992–2020, which is set to meet the need for long time series of USR distributions. First, a maximum–minimum (0–63) piecewise linear stretch was applied for the VIIRS NTL data to match the value ranges of DMSP NTL data. Then, the USR extraction was processed through multiple iterations of the construction of the quantile curve and the detection of mutation points based on the quantile curve. Finally, a temporal consistency check was used to post-process the initial USR area to obtain a more consistent and reliable USR sequence from 1992 to 2020. Nineteen Chinese cities with different levels of urbanization and population sizes were chosen as the study area to verify the robustness and flexibility of the approach. The visual and quantitative evaluations of spatiotemporal consistency compared with the validation data indicate that the USR retrieval results showed good agreement with the land use map derived from Landsat images and the time series product from MODIS, with the average OA of overall urban extents higher than 0.95 and the average KC reaching 0.6.
According to the extracted USR distributions, the selected cities have experienced rapid and profound urbanization during the past decades. The rate, direction, and transition type of urban expansion vary among different cities with diverse levels of socioeconomic development. The growth rates of urban expansion in all the study cities range from 1.3 to the highest value of up to 36.5, and the average growth rate of the urban areas is 4.17. The directions of urban expansion can be generally divided into two types: radiating outward from the center (type A) and radiating in a single direction (type B). The urban expansion of the supercities and the megacities tends to be the former, while that of some cities with smaller population sizes tends to be the latter (
Table 5). Specifically, Beijing, Tianjin, Wuhan, Chengdu, Xi’an, Nanjing, Liaocheng, and Shangqiu presented the urban expansion of type A, and the proportion of type A in all nineteen cities was 42%. On the other hand, Shanghai, Guangzhou, Zhengzhou, Changchun, Urumqi, Qingdao, Taiyuan, Datong, Bengbu, Yingkou, and Sanya presented the urban expansion of type B, and the proportion of type B in all 19 cities was 58%.
This study provides a new technique for mapping USR time series sequences using the integration of DMSP (1992–2013) and VIIRS (2014–2020) NTL data without altering the numerical distribution characteristics and spatial information features of the original VIIRS NTL. This makes it worthy of being promoted to the nationwide and even global scale. Notably, the blooming effect of DMSP NTL data led to an overestimation of the range of rural areas, which then generated an inconsistency between rural areas extracted from DMSP and VIIRS NTL since there was no blooming effect in the latter. Preprocessing of the low DN value region in DMSP NTL data referring to the corresponding area in VIIRS NTL data might help to improve the task of retrieving rural areas using the proposed approach. Moreover, the atmospheric factors might influence the extraction results of USR in relation to long-term time series data as the satellite measurements could be affected by aerosol concentration, humidity, and even lunar cycles [
52,
53,
54,
55,
56]. Future implementation of the proposed method can also take the main atmospheric parameters into account.