VIIRS/DNB夜间灯光月度产品插补方法对比——以北京为例
|
摘要
新一代VIIRS/DNB(Visible Infrared Imaging Radiometer Suite Day/Night Band)夜间灯光数据因其具备更精细的时空分辨率、数据不存在饱和现象、不同年份数据可比性强等优点而迅速代替DMSP/OLS(Defense Meteorological Satellite Program's Operational Linescan System)夜间灯光数据成为新的研究热点。但由于杂散光的污染,VIIRS/DNB夏季数据数值缺失严重,致使数据在空间和时间上不连续,因此,插补缺失数据成为后续应用的前提。鉴于此,论文从插补结果异常值、与参照值对比、计算性能三个方面,系统比较了三次样条插值(样条)、三次Hermite插值(Hermite)、灰色预测模型(GM)、三次指数平滑法(指数)4种插补方法的适用性,以期为插补VIIRS/DNB夜间灯光数据提供方法选择的依据。研究结果表明:(1)异常值比较方面,Hermite法未出现异常值,另外3种算法仅出现少量异常值(0.02%～1.34%);(2)与参照值的对比方面,Hermite法与参考值接近程度最高,GM接近程度最低,样条法和指数法介于两者中间;(3)算法性能比较方面,4种方法都具备计算简单、容易编程的特点,但指数法的算法时长是另外3种方法的10倍以上。因此,综合多方表现,当插补月份前后两侧均有足够长的原始数据时,插补效果好、计算速度快、不会出现过冲现象的Hermite法最适宜,样条法次之;当插补月份仅单侧有足够长的数据时,适宜采用指数法(插补效果好、计算速度较慢)或GM(插补效果偏低、计算速度快)进行插补。
Comparing with nighttime light data acquired by the Defense Meteorological Satellite Program's Operational Linescan System(DMSP/OLS),nighttime light data sensed by the Visible Infrared Imaging Radiometer Suite Day/Night Band(VIIRS/DNB) have a higher spatial resolution and finer temporal resolution.VIIRS/DNB nighttime light data also have a substantial number of improvements in terms of accuracy and inflight calibrations.As a result,VIIRS/DNB nighttime light data become a new research hotspot rapidly.Even so,VIIRS/DNB nighttime light data are vulnerable to stray light and contain a large number of distorted values in mid and high latitudes,especially in summer.Therefore,this study took Beijing as an example and adopted cubic spline interpolation(spline),cubic Hermite interpolation(Hermite),gray model(GM),and triple exponential smoothing(exponent) to interpolate default data of May to July 2015,and then compared the results of these four interpolation algorithms.The result shows that:1) With regard to abnormal values,Hermite does not produce any abnormal value,while the other three algorithms generate few such values(0.02%～1.34%).2)Comparing with the reference data—the Visible Infrared Imaging Radiometer Suite Cloud Mask Stray Light(VCMSL) version,the interpolation result of Hermite is closest to the reference,and the GM result is least close to the reference.3) In terms of computing time,all of these four algorithms are easy to be programmed and calculated,but the exponential smoothing method has to calculate smoothing parameter repeatedly and therefore it will spend much more time than the other three algorithms.In conclusion,a comprehensive assessment shows that when the two time periods before and after the interpolation months both have enough original data,Hermite will be the best choice because of its great interpolation performance,no overshoots,and fast calculation speed.Spline takes the second place.When only one side of the interpolation months has adequate data,GM and exponent methods both can be used.The GM calculation runs fast but the interpolation result is not optimal,and exponent calculation runs slow but the algorithm interpolates well.
Comparing with nighttime light data acquired by the Defense Meteorological Satellite Program's Operational Linescan System(DMSP/OLS),nighttime light data sensed by the Visible Infrared Imaging Radiometer Suite Day/Night Band(VIIRS/DNB) have a higher spatial resolution and finer temporal resolution.VIIRS/DNB nighttime light data also have a substantial number of improvements in terms of accuracy and inflight calibrations.As a result,VIIRS/DNB nighttime light data become a new research hotspot rapidly.Even so,VIIRS/DNB nighttime light data are vulnerable to stray light and contain a large number of distorted values in mid and high latitudes,especially in summer.Therefore,this study took Beijing as an example and adopted cubic spline interpolation(spline),cubic Hermite interpolation(Hermite),gray model(GM),and triple exponential smoothing(exponent) to interpolate default data of May to July 2015,and then compared the results of these four interpolation algorithms.The result shows that:1) With regard to abnormal values,Hermite does not produce any abnormal value,while the other three algorithms generate few such values(0.02%～1.34%).2)Comparing with the reference data—the Visible Infrared Imaging Radiometer Suite Cloud Mask Stray Light(VCMSL) version,the interpolation result of Hermite is closest to the reference,and the GM result is least close to the reference.3) In terms of computing time,all of these four algorithms are easy to be programmed and calculated,but the exponential smoothing method has to calculate smoothing parameter repeatedly and therefore it will spend much more time than the other three algorithms.In conclusion,a comprehensive assessment shows that when the two time periods before and after the interpolation months both have enough original data,Hermite will be the best choice because of its great interpolation performance,no overshoots,and fast calculation speed.Spline takes the second place.When only one side of the interpolation months has adequate data,GM and exponent methods both can be used.The GM calculation runs fast but the interpolation result is not optimal,and exponent calculation runs slow but the algorithm interpolates well.
引文
陈青燕,梁丹,徐文兵,等.2016.一种线目标豪斯多夫相似距离度量指标[J].测绘科学,41(8):14-18.[Chen Q Y,Liang D,Xu W B,et al.2016.Study of the linear object similarity measure based on Hausdorff distance.Science of Surveying and Mapping,41(8):14-18.]
戴豪民,许爱强.2015.基于张力格林样条的EMD均值曲线插值方法[J].计算机工程与设计,36(2):401-405.[Dai HM,Xu A X.2015.Interpolation approach of mean curve for EMD based on Green's spline in tension.Computer Engineering and Design,36(2):401-405.]
邓聚龙.1982.灰色控制系统[J].华中工学院学报,(3):9-18[Deng J L.1982.The gray control system.Journal of Huazhong University of Science and Technology,(3):9-18.]
邓聚龙.1986.本征性灰色系统的主要方法[J].系统工程理论与实践,(1):60-65.[Deng J L.1986.The main methods of intrinsic gray system.Systems Engineering-Theory&Practice,(1):60-65.]
段功豪,牛瑞卿,赵艳南,等.2016.基于动态指数平滑模型的降雨诱发型滑坡预测[J].武汉大学学报(信息科学版),41(7):958-962.[Duan G H,Niu R Q,Zhao Y N,et al2016.Rainfall-induced landslide prediction based on dynamic exponential smoothing model.Geomatics and Information Science of Wuhan University,41(7):958-962.]
甘祥前,任超,刘林波.2018.基于小波去噪的非等间距多点灰色预测模型[J].测绘地理信息,43(3):41-44.[Gan XQ,Ren C,Liu L B.2018.Non-equidistant multi-point gray prediction model based on wavelet danoising.Journal of Geomatics,43(3):41-44.]
顾朝林,张悦,翟炜,等.2016.城市与区域定量研究进展[J]地理科学进展,35(12):1433-1446.[Gu C L,Zhang YZhai W,et al.2016.Progress in urban and regional quantitative research.Progress in Geography,35(12):1433-1446.]
李峰,孙广通,王秋玲,等.2018.NPP-VIIRS夜间灯光数据的县级电力消费空间化研究[J].测绘与空间地理信息,41(4):8-11.[Li F,Sun G T,Wang Q L,et al.2018.Spatialization research of electricity consumptions at county leve using NPP-VIIRS nighttime lights.Geomatics&Spatia Information Technology,41(4):8-11.]
李翔,陈振杰,吴洁璇,等.2017.基于夜间灯光数据和空间回归模型的城市常住人口格网化方法研究[J].地球信息科学学报,19(10):1298-1305.[Li X,Chen Z J,Wu J X,e al.2017.Gridding methods of city permanent population based on night light data and spatial regression models Journal of Geo-information Science,19(10):1298-1305.]
李欣欣,何飞,王利.2018.大连金普新区城镇空间拓展特征研究[J].地理空间信息,(1):27-31.[Li X X,He F,Wang L.2018.Study on urban spatial expansion characteristics of Jinpu new district in Dalian.Geospatial Information(1):27-31.]
刘建文,周玉科.2018.站点尺度的青藏高原时序NDVI重构方法比较与应用[J].地理科学进展,37(3):427-437.[Liu J W,Zhou Y K.2018.Comparison and application of ND-VI time-series reconstruction methods at site scale on the Tibetan Plateau.Progress in Geography,37(3):427-437.]
刘敬强,瓦哈甫·哈力克,哈斯穆·阿比孜,等.2013.新疆特色林果业种植对气候变化的响应[J].地理学报,68(5):708-720.[Liu J Q,Wahap H L K,Hasm A B Z,et al.2013.Responses of characteristic horticulture plant to climate change of Xinjiang.Acta Geographica Sinica,68(5):708-720.]
刘思峰,杨英杰.2015.灰色系统研究进展(2004-2014)[J].南京航空航天大学学报,47(1):1-18.[Liu S F,Yang Y J.2015.Advances in grey system research(2004-2014).Journal of Nanjing University of Aeronautics&Astronautics,47(1):1-18.]
彭建,李慧蕾,刘焱序,等.2018.雄安新区生态安全格局识别与优化策略[J].地理学报,73(4):701-710.[Peng J,Li H L,Liu Y X,et al.2018.Identification and optimization of ecological security pattern in Xiong'an new area.Acta Geographica Sinica,73(4):701-710.]
沈海迪,万振凯.2017.基于指数平滑法的动态预测机制[J].计算机技术与发展,27(7):6-9.[Shen H D,Wan Z K.2017.A dynamic prediction mechanism based on exponential smoothing method.Computer Technology and Development,27(7):6-9.]
宋中民,邓聚龙.2001.反向累加生成及灰色GOM(1,1)模型[J].系统工程,(1):66-69.[Song Z M,Deng J L.2001.The accumulated generating operation in opposite direction and its use in gray model GOM(1,1).Systems Engineering,(1):66-69.]
严兰兰,韩旭里,饶智勇.2017.具有指定多项式重构精度和连续阶的插值曲线构造方法[J].计算机辅助设计与图形学学报,29(4):707-719.[Yan L L,Han X L,Rao Z Y.Construction method of interpolation curve with given polynomial reconstruction precision and continuity order.Journal of Computer-Aided Design&Computer Graphics,29(4):707-719.]
张嘉望,郭军献,李福松.2015.基于最优平滑系数三次指数平滑法的转速预测[J].探测与控制学报,37(5):43-46.[Zhang J W,Guo J X,Li F S.2015.Rotation speed prediction based on cubic exponential smoothing method.Journal of Detection&Control,37(5):43-46.]
张晓瑞,方创琳,王振波,等.2013.基于RBF神经网络的城市建成区面积预测研究:兼与BP神经网络和线性回归对比分析[J].长江流域资源与环境,22(6):691-697.[Zhang X R,Fang C L,Wang Z B,et al.2013.Prediction of urban built-up area based on RBF neural network.Resources and Environment in the Yangtze Basin,22(6):691-697.]
赵杰,花向红,刘闯,等.2015.ARIMA-BP组合模型在高铁沉降预报中的应用研究[J].测绘地理信息,40(4):54-56.[Zhao J,Hua X H,Liu C,et al.2015.Application of ARI-MA-BP model in the settlement prediction of high speed railway.Journal of Geomatics,40(4):54-56.]
赵志伟,杨景明,呼子宇,等.2016.基于一次指数平滑法的自适应差分进化算法[J].控制与决策,31(5):790-796[Zhao Z W,Yang J M,Hu Z Y,et al.2016.Self-adaptive differential evolution algorithm based on exponentia smoothing.Control and Decision,31(5):790-796.]
Bennett M M,Smith L C.2017.Advances in using multitemporal night-time lights satellite imagery to detect,estimate and monitor socioeconomic dynamics[J].Remote Sensing of Environment,192:176-197.
Chen X,Nordhaus W.2015.A test of the new VIIRS lights data set:Population and economic output in Africa[J].Remote Sensing,7(4):4937-4947.
Elvidge C D,Baugh K E,Zhizhin M,et al.2013.Why VIIRSdata are superior to DMSP for mapping nighttime lights[J].Proceedings of the Asia-Pacific Advanced Network,3562-69.
Fritsch F N,Carlson R E.1980.Monotone piecewise cubic interpolation[J].SIAM Journal on Numerical Analysis,17(2):238-246.
Han X.2018.Shape-preserving piecewise rational interpolation with higher order continuity[J].Applied Mathematics&Computation,337:1-13.
Hyndman R J,Athanasopoulos G.2014.Forecasting:Principles and practice[M].Australia:OTexts.
Lee T E,Miller S D,Turk F J,et al.2006.The NPOESS VI-IRS day/night visible sensor[J].Bulletin of the American Meteorological Society,87(2):191-199.
Levin N.2017.The impact of seasonal changes on observed nighttime brightness from 2014 to 2015 monthly VIIRSDNB composites[J].Remote Sensing of Environment193:150-164.
Li X,Li D,Xu H,et al.2017.Intercalibration between DMSPOLS and VIIRS night-time light images to evaluate city light dynamics of Syria's major human settlement during Syrian Civil War[J].International Journal of Remote Sensing,38(21):5934-5951.
Li X,Xu H,Chen X,et al.2013.Potential of NPP-VIIRSnighttime light imagery for modeling the regional economy of China[J].Remote Sensing,5(6):3057-3081.
Li X,Zhang R,Huang C,et al.2015.Detecting 2014 Northern Iraq insurgency using night-time light imagery[J].International Journal of Remote Sensing,36(13):3446-3458.
Liao L B,Weiss S,Mills S,et al.2013.Suomi NPP VIIRS daynight band on-orbit performance[J].Journal of Geophysical Research:Atmospheres,118(22):12705-12718.
Ma T,Zhou C,Pei T,et al.2014.Responses of Suomi-NPPVIIRS-derived nighttime lights to socioeconomic activity in China's cities[J].Remote Sensing Letters,5(2):165-174.
Miller S D,Straka W,Mills S P,et al.2013.Illuminating the capabilities of the Suomi national polar-orbiting partnership(NPP)visible infrared imaging radiometer suite(VI-IRS)day/night band[J].Remote Sensing,5(12):6717-6766.
Mills S,Weiss S,Liang C.2013.VIIRS day/night band(DNB)stray light characterization and correction[C]//Proc.SPIE8866,Earth Observing Systems XVIII.doi:10.1117/12.2023107.
Qiu S,Shao X,Cao C Y,et al.2017.Assessment of straylight correction performance for the VIIRS day/night band using Dome-C and Greenland under lunar illumination[J].International Journal of Remote Sensing,38(21):5880-5898.
Román M O,Wang Z,Sun Q,et al.2018.NASA's Black Marble nighttime lights product suite[J].Remote Sensing of Environment,210:113-143.
Shi K,Yu B,Huang Y,et al.2014.Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales:A comparison with DMSP-OLS data[J].Remote Sensing,6(2):1705-1724.
Zhang Q,Wang P,Chen H,et al.2017.A novel method for urban area extraction from VIIRS DNB and MODIS NDVIdata:A case study of Chinese cities[J].International Journal of Remote Sensing,38(21):6094-6109.
Zhao N,Hsu F C,Cao G,et al.2017.Improving accuracy of economic estimations with VIIRS DNB image products[J].International Journal of Remote Sensing,38(21):5899-5918.
戴豪民,许爱强.2015.基于张力格林样条的EMD均值曲线插值方法[J].计算机工程与设计,36(2):401-405.[Dai HM,Xu A X.2015.Interpolation approach of mean curve for EMD based on Green's spline in tension.Computer Engineering and Design,36(2):401-405.]
邓聚龙.1982.灰色控制系统[J].华中工学院学报,(3):9-18[Deng J L.1982.The gray control system.Journal of Huazhong University of Science and Technology,(3):9-18.]
邓聚龙.1986.本征性灰色系统的主要方法[J].系统工程理论与实践,(1):60-65.[Deng J L.1986.The main methods of intrinsic gray system.Systems Engineering-Theory&Practice,(1):60-65.]
段功豪,牛瑞卿,赵艳南,等.2016.基于动态指数平滑模型的降雨诱发型滑坡预测[J].武汉大学学报(信息科学版),41(7):958-962.[Duan G H,Niu R Q,Zhao Y N,et al2016.Rainfall-induced landslide prediction based on dynamic exponential smoothing model.Geomatics and Information Science of Wuhan University,41(7):958-962.]
甘祥前,任超,刘林波.2018.基于小波去噪的非等间距多点灰色预测模型[J].测绘地理信息,43(3):41-44.[Gan XQ,Ren C,Liu L B.2018.Non-equidistant multi-point gray prediction model based on wavelet danoising.Journal of Geomatics,43(3):41-44.]
顾朝林,张悦,翟炜,等.2016.城市与区域定量研究进展[J]地理科学进展,35(12):1433-1446.[Gu C L,Zhang YZhai W,et al.2016.Progress in urban and regional quantitative research.Progress in Geography,35(12):1433-1446.]
李峰,孙广通,王秋玲,等.2018.NPP-VIIRS夜间灯光数据的县级电力消费空间化研究[J].测绘与空间地理信息,41(4):8-11.[Li F,Sun G T,Wang Q L,et al.2018.Spatialization research of electricity consumptions at county leve using NPP-VIIRS nighttime lights.Geomatics&Spatia Information Technology,41(4):8-11.]
李翔,陈振杰,吴洁璇,等.2017.基于夜间灯光数据和空间回归模型的城市常住人口格网化方法研究[J].地球信息科学学报,19(10):1298-1305.[Li X,Chen Z J,Wu J X,e al.2017.Gridding methods of city permanent population based on night light data and spatial regression models Journal of Geo-information Science,19(10):1298-1305.]
李欣欣,何飞,王利.2018.大连金普新区城镇空间拓展特征研究[J].地理空间信息,(1):27-31.[Li X X,He F,Wang L.2018.Study on urban spatial expansion characteristics of Jinpu new district in Dalian.Geospatial Information(1):27-31.]
刘建文,周玉科.2018.站点尺度的青藏高原时序NDVI重构方法比较与应用[J].地理科学进展,37(3):427-437.[Liu J W,Zhou Y K.2018.Comparison and application of ND-VI time-series reconstruction methods at site scale on the Tibetan Plateau.Progress in Geography,37(3):427-437.]
刘敬强,瓦哈甫·哈力克,哈斯穆·阿比孜,等.2013.新疆特色林果业种植对气候变化的响应[J].地理学报,68(5):708-720.[Liu J Q,Wahap H L K,Hasm A B Z,et al.2013.Responses of characteristic horticulture plant to climate change of Xinjiang.Acta Geographica Sinica,68(5):708-720.]
刘思峰,杨英杰.2015.灰色系统研究进展(2004-2014)[J].南京航空航天大学学报,47(1):1-18.[Liu S F,Yang Y J.2015.Advances in grey system research(2004-2014).Journal of Nanjing University of Aeronautics&Astronautics,47(1):1-18.]
彭建,李慧蕾,刘焱序,等.2018.雄安新区生态安全格局识别与优化策略[J].地理学报,73(4):701-710.[Peng J,Li H L,Liu Y X,et al.2018.Identification and optimization of ecological security pattern in Xiong'an new area.Acta Geographica Sinica,73(4):701-710.]
沈海迪,万振凯.2017.基于指数平滑法的动态预测机制[J].计算机技术与发展,27(7):6-9.[Shen H D,Wan Z K.2017.A dynamic prediction mechanism based on exponential smoothing method.Computer Technology and Development,27(7):6-9.]
宋中民,邓聚龙.2001.反向累加生成及灰色GOM(1,1)模型[J].系统工程,(1):66-69.[Song Z M,Deng J L.2001.The accumulated generating operation in opposite direction and its use in gray model GOM(1,1).Systems Engineering,(1):66-69.]
严兰兰,韩旭里,饶智勇.2017.具有指定多项式重构精度和连续阶的插值曲线构造方法[J].计算机辅助设计与图形学学报,29(4):707-719.[Yan L L,Han X L,Rao Z Y.Construction method of interpolation curve with given polynomial reconstruction precision and continuity order.Journal of Computer-Aided Design&Computer Graphics,29(4):707-719.]
张嘉望,郭军献,李福松.2015.基于最优平滑系数三次指数平滑法的转速预测[J].探测与控制学报,37(5):43-46.[Zhang J W,Guo J X,Li F S.2015.Rotation speed prediction based on cubic exponential smoothing method.Journal of Detection&Control,37(5):43-46.]
张晓瑞,方创琳,王振波,等.2013.基于RBF神经网络的城市建成区面积预测研究:兼与BP神经网络和线性回归对比分析[J].长江流域资源与环境,22(6):691-697.[Zhang X R,Fang C L,Wang Z B,et al.2013.Prediction of urban built-up area based on RBF neural network.Resources and Environment in the Yangtze Basin,22(6):691-697.]
赵杰,花向红,刘闯,等.2015.ARIMA-BP组合模型在高铁沉降预报中的应用研究[J].测绘地理信息,40(4):54-56.[Zhao J,Hua X H,Liu C,et al.2015.Application of ARI-MA-BP model in the settlement prediction of high speed railway.Journal of Geomatics,40(4):54-56.]
赵志伟,杨景明,呼子宇,等.2016.基于一次指数平滑法的自适应差分进化算法[J].控制与决策,31(5):790-796[Zhao Z W,Yang J M,Hu Z Y,et al.2016.Self-adaptive differential evolution algorithm based on exponentia smoothing.Control and Decision,31(5):790-796.]
Bennett M M,Smith L C.2017.Advances in using multitemporal night-time lights satellite imagery to detect,estimate and monitor socioeconomic dynamics[J].Remote Sensing of Environment,192:176-197.
Chen X,Nordhaus W.2015.A test of the new VIIRS lights data set:Population and economic output in Africa[J].Remote Sensing,7(4):4937-4947.
Elvidge C D,Baugh K E,Zhizhin M,et al.2013.Why VIIRSdata are superior to DMSP for mapping nighttime lights[J].Proceedings of the Asia-Pacific Advanced Network,3562-69.
Fritsch F N,Carlson R E.1980.Monotone piecewise cubic interpolation[J].SIAM Journal on Numerical Analysis,17(2):238-246.
Han X.2018.Shape-preserving piecewise rational interpolation with higher order continuity[J].Applied Mathematics&Computation,337:1-13.
Hyndman R J,Athanasopoulos G.2014.Forecasting:Principles and practice[M].Australia:OTexts.
Lee T E,Miller S D,Turk F J,et al.2006.The NPOESS VI-IRS day/night visible sensor[J].Bulletin of the American Meteorological Society,87(2):191-199.
Levin N.2017.The impact of seasonal changes on observed nighttime brightness from 2014 to 2015 monthly VIIRSDNB composites[J].Remote Sensing of Environment193:150-164.
Li X,Li D,Xu H,et al.2017.Intercalibration between DMSPOLS and VIIRS night-time light images to evaluate city light dynamics of Syria's major human settlement during Syrian Civil War[J].International Journal of Remote Sensing,38(21):5934-5951.
Li X,Xu H,Chen X,et al.2013.Potential of NPP-VIIRSnighttime light imagery for modeling the regional economy of China[J].Remote Sensing,5(6):3057-3081.
Li X,Zhang R,Huang C,et al.2015.Detecting 2014 Northern Iraq insurgency using night-time light imagery[J].International Journal of Remote Sensing,36(13):3446-3458.
Liao L B,Weiss S,Mills S,et al.2013.Suomi NPP VIIRS daynight band on-orbit performance[J].Journal of Geophysical Research:Atmospheres,118(22):12705-12718.
Ma T,Zhou C,Pei T,et al.2014.Responses of Suomi-NPPVIIRS-derived nighttime lights to socioeconomic activity in China's cities[J].Remote Sensing Letters,5(2):165-174.
Miller S D,Straka W,Mills S P,et al.2013.Illuminating the capabilities of the Suomi national polar-orbiting partnership(NPP)visible infrared imaging radiometer suite(VI-IRS)day/night band[J].Remote Sensing,5(12):6717-6766.
Mills S,Weiss S,Liang C.2013.VIIRS day/night band(DNB)stray light characterization and correction[C]//Proc.SPIE8866,Earth Observing Systems XVIII.doi:10.1117/12.2023107.
Qiu S,Shao X,Cao C Y,et al.2017.Assessment of straylight correction performance for the VIIRS day/night band using Dome-C and Greenland under lunar illumination[J].International Journal of Remote Sensing,38(21):5880-5898.
Román M O,Wang Z,Sun Q,et al.2018.NASA's Black Marble nighttime lights product suite[J].Remote Sensing of Environment,210:113-143.
Shi K,Yu B,Huang Y,et al.2014.Evaluating the ability of NPP-VIIRS nighttime light data to estimate the gross domestic product and the electric power consumption of China at multiple scales:A comparison with DMSP-OLS data[J].Remote Sensing,6(2):1705-1724.
Zhang Q,Wang P,Chen H,et al.2017.A novel method for urban area extraction from VIIRS DNB and MODIS NDVIdata:A case study of Chinese cities[J].International Journal of Remote Sensing,38(21):6094-6109.
Zhao N,Hsu F C,Cao G,et al.2017.Improving accuracy of economic estimations with VIIRS DNB image products[J].International Journal of Remote Sensing,38(21):5899-5918.
© 2004-2018 中国地质图书馆版权所有 京ICP备05064691号 京公网安备11010802017129号 地址：北京市海淀区学院路29号 邮编：100083 电话：办公室：(+86 10)66554848；文献借阅、咨询服务、科技查新：66554700 |