长江流域城市湖泊夜光影像（NPP-VIIRS-NTL）空间降尺度方法适用性研究

查文娴; 赵红梅; 邓志刚; 李文凯

doi:10.11834/jrs.20255083

城市与陆地 | 浏览量 : 0 下载量: 211 CSCD: 0

R-PDF
PDF
导出
分享
收藏
专辑

长江流域城市湖泊夜光影像（NPP-VIIRS-NTL）空间降尺度方法适用性研究
Spatial downscaling method applicability for NPP-VIIRS-NTL of urban lakes， Yangtze River Basin
2026年30卷第1期页码：79-92
收稿：2025-02-11，

纸质出版：2026-01-07
DOI： 10.11834/jrs.20255083
稿件说明：

移动端阅览

查文娴，赵红梅，邓志刚，李文凯.2026.长江流域城市湖泊夜光影像（NPP-VIIRS-NTL）空间降尺度方法适用性研究.遥感学报，30（1）： 79-92 DOI： 10.11834/jrs.20255083.

Zha W X，Zhao H M，Deng Z G and Li W K. 2026. Spatial downscaling method applicability for NPP-VIIRS-NTL of urban lakes， Yangtze River Basin. National Remote Sensing Bulletin， 30（1）：79-92 DOI： 10.11834/jrs.20255083.

摘要

夜光遥感影像已广泛应用于城市环境、社会经济、生态保护、武装冲突灾后评估等领域。然而，由于长时序高空间分辨率夜光遥感数据的缺失，城市水体环境评估在夜光遥感研究中相对匮乏。本研究旨在弥补夜光遥感在内陆水体环境研究中的缺失，以长江流域面积＞3 km

的形态各异的城市湖泊为研究对象，综合湖泊水体亮度温度LBT（Lake Brightness Temperature）、浮游藻类指数FAI（Floating Algae Index）和水色指数FUI（Forel-Ule Index）等水环境遥感评估参数，借鉴DisTrad、地理加权回归GWR（Geographically weighted Regression）和随机森林RF（Random Fores

t）等适用于热红外遥感数据的空间降尺度方法，引入面向对象的理论和相关性统计方法，探索适用于不同形态内陆水体区域Suomi极轨卫星Suomi-NPP（Suomi National Polar-orbiting Partnership）可见光红外成像辐射计VIIRS（Visible Infrared Imaging Radiometer Suite）夜光影像NTL（Nighttime Light）的最佳空间降尺度方法。结果表明：基于LBT和FAI，面向对象的DisTrad方法（OD-TA）具有一定的稳定性，而基于LBT和FUI，面向对象的地理加权回归方法（OGWR-TU）在滇池和巢湖等大面积（

10 km

），形状复杂度较高（PARA

65）的城市湖泊中表现出色；湖泊空间形态、湖滨夜间灯光的社会人文属性是影响空间降尺度方法适用性的主要因子。本研究结果不仅弥补了水体区域长时序高空间分辨率NTL影像的缺失，还可为城市湖泊光污染评估及滨湖环境社会人文环境分析等提供方法和数据支撑。

Abstract

Remote sensing images of nighttime lights (NTL) have been widely used in diverse fields

including urban environment

socioeconomics

ecological protection

post-conflict assessment of armed conflicts. However

NTL pollution in urban water lakes is seldom studied due to the coarse spatial resolution of existing NTL images

such as those from the Defense Meteorological Satellite Program and the Suomi National Polar-Orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS). Spatial downscaling techniques are needed to support NTL pollution studies for urban lakes. Urban lakes of provincial capitals in Yangtze River Basin are selected as our study area

which had been polluted by NTL because of rapid urbanization. This study aims to develop optimal spatial downscaling methods for NPP-VIIRS NTL images of urban lakes

using NPP-VIIRS NTL

LJ-1 01 NTL

and Landsat 8 OLI/TIRS images

to overcome the absence of remote sensing images of NTL in the study of inland water environment.

Lake brightness temperature (LBT)

floating algae index (FAI)

and Forel-Ule index (FUI) are selected as interpretive factors of urban lakes. These interpretive factors have four combinations: LBT(T)

LBT and FAI(TA)

LBT and FUI(TU)

LBT

FAI and FUI(TAU). Three downscaling models

namely

disaggregation procedure for radiometric surface temperature (DisTrad)

geographically weighted regression (GWR)

and random forest (RF)

are used with these combinations. A total of 11 object-oriented do

wnscaling methods are employed for every urban lake. These methods are evaluated using the coefficient of determination (

²) and root mean square error (RMSE) to identify the most effective approach for different types of urban lakes. Finally

this study employed the area and perimeter area ratio

average brightness of NTL (ANTL) and brightness threshold (RNTL)

and lakeside greenness to explore the impact factors on the applicability of NTL spatial downscaling methods.

The downscaling NPP NTL images are significantly correlated with LJ-1 01 NTL images for most of the urban lakes

except South Lake of Wuhan and Yao Lake of Nanchang. The RMSE between downscaling NPP images and LJ-1 01 images are less than 3 nW/(cm²∙sr) for urban lakes except for highly influenced urban lakes

such as East Lake

Huangjia Lake

South Lake

and Qingshan Lake. Among object-oriented DisTrad (OD)

object-oriented GWR (OGWR)

and object-oriented random forest (ORF) models

OD is robust

while ORF is unstable

the performance of OGWR is relatively moderate. The optimal combination of interpretive factors is different for the three spatial downscaling models. LBT and FUI (-TU) is the best combination for the OGWR model

while LBT and FAI (-TA) is the optimal combination for the OD model. No stable and ideal interpretive factor combination is available for the ORF model. The morphology of urban lakes and the intensity of human activities affect the applicability of NTL downscaling methods considerably. Meanwhile

the weather conditions of Landsat 8 OLI/TIRS acquisition dates may affect the performance of each model in different urban lakes.

OD-TA is the most robust

while OGWR-TU is most suitable for lakes with large areas (

10 km

) and high PARA (

65). OD-TA is mainly applicable to urban lakes with area

30 km

PARA

and greenness between 30—50. OGWR-TU is most suitable for Dianchi Lake and Chaohu Lake. The study underscores the importance of spatial morphology and built-up

area attributes in the downscaling method’s performance

providing a methodological foundation for urban lake NTL pollution assessment and environmental analysis.

关键词

Keywords

references

Agam N , Kustas W P , Anderson M C , Li F Q and Neale C M U . 2007 . A vegetation index based technique for spatial sharpening of thermal imagery . Remote Sensing of Environment , 107 ( 4 ): 545 - 558 [ DOI: 10.1016/j.rse.2006.10.006 http://dx.doi.org/10.1016/j.rse.2006.10.006 ]

Breiman L . 2001 . Random forests . Machine Learning , 45 ( 1 ): 5 - 32 [ DOI: 10.1023/A:1010933404324 http://dx.doi.org/10.1023/A:1010933404324 ]

Chen C , Zhao S H , Duan Z and Qin Z H . 2015 . An improved spatial downscaling procedure for TRMM 3 B 43 precipitation product using geographical ly weighted regression. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 8 ( 9 ): 4592 - 4604 [ DOI: 10.1109/JSTARS.2015.2441734 http://dx.doi.org/10.1109/JSTARS.2015.2441734 ]

Chen T H K , Prishchepov A V , Fensholt R and Sabel C E . 2019 . Detecting and monitoring long-term landslides in urbanized areas with nighttime light data and multi-seasonal Landsat imagery across Taiwan from 1998 to 2017 . Remote Sensing of Environment , 225 : 317 - 327 [ DOI: 10.1016/j.rse.2019.03.013 http://dx.doi.org/10.1016/j.rse.2019.03.013 ]

Chen X L , Zhao H M , Li P X and Yin Z Y . 2006 . Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes . Remote Sensing of Environment , 104 ( 2 ): 133 - 146 [ DOI: 10.1016/j.rse.2005.11.016 http://dx.doi.org/10.1016/j.rse.2005.11.016 ]

Cheng B , Chen Z Q , Yu B L , Li Q X , Wang C X , Li B B , Wu B , Li Y and Wu J P . 2020 . Automated extraction of street lights from J L1 - 3 B nighttime light data and assessment of their solar energy potential. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 13 : 675 - 684 [ DOI: 10.1109/JSTARS.2020.2971266 http://dx.doi.org/10.1109/JSTARS.2020.2971266 ]

Davies T W , Duffy J P , Bennie J and Gaston K J . 2016 . Stemming the tide of light pollution encroaching into marine protected areas . Conservation Letters , 9 ( 3 ): 164 - 171 [ DOI: 10.1111/conl.12191 http://dx.doi.org/10.1111/conl.12191 ]

Davies T W and Smyth T . 2018 . Why artificial light at night should be a focus for global change research in the 21st century . Global Change Biology , 24 ( 3 ): 872 - 882 [ DOI: 10.1111/gcb.13927 http://dx.doi.org/10.1111/gcb.13927 ]

Depledge M H , Godard-Codding C A J and Bowen R E . 2010 . Light pollution in the sea . Marine Pollution Bulletin , 60 ( 9 ): 1383 - 1385 [ DOI: 10.1016/j.marpolbul.2010.08.002 http://dx.doi.org/10.1016/j.marpolbul.2010.08.002 ]

Firozjaei M K , Kiavarz M and Alavipanah S K . 2024 . An adjacency-effect-based approach for accuracy improvement in satellite land surface temperature disaggregation . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 17 : 1066 - 1083 [ DOI: 10.1109/JSTARS.2023.3325920 http://dx.doi.org/10.1109/JSTARS.2023.3325920 ]

Gao F , Masek J , Schwaller M and Hall F . 2006 . On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance . IEEE Transactions on Geoscience and Remote Sensing , 44 ( 8 ): 2207 - 2218 [ DOI: 10.1109/TGRS.2006.872081 http://dx.doi.org/10.1109/TGRS.2006.872081 ]

Gualtieri J A and Chettri S . 2000 . Support vector machines for classification of hyperspectral data // IEEE 2000 International Geoscience and Remote Sensing Symposium . Taking the Pulse of the Planet : The Role of Remote Sensing in Managing the Environment. Proceedings (Cat. No . 00 CH 37120 ). Honolulu: IEEE: 813 - 815 [ DOI: 10.1109/IGARSS.2000.861712 http://dx.doi.org/10.1109/IGARSS.2000.861712 ]

Hölker F , Bolliger J , Davies T W , Giavi S , Jechow A , Kalinkat G , Longcore T , Spoelstra K , Tidau S , Visser M E and Knop E . 2021 . 11 Pressing research questions on how light pollution affects biodiversity . Frontiers in Ecology and Evolution , 9 : 767177 [ DOI: 10.3389/fevo.2021.767177 http://dx.doi.org/10.3389/fevo.2021.767177 ]

Hu C M . 2009 . A novel ocean color index to detect floating algae in the global oceans . Remote Sensing of Environment , 113 ( 10 ): 2118 - 2129 [ DOI: 10.1016/j.rse.2009.05.012 http://dx.doi.org/10.1016/j.rse.2009.05.012 ]

Jechow A and Hölker F . 2019 . How dark is a river? Artificial light at night in aquatic systems and the need for comprehensive night-time light measurements . WIREs Water , 6 ( 6 ): e 1388 [ DOI: 10.1002/wat2.1388 http://dx.doi.org/10.1002/wat2.1388 ]

Jiang W , He G J , Leng W C , Long T F , Wang G Z , Liu H C , Peng Y , Yin R Y and Guo H X . 2018a . Characterizing light pollution trends across protected areas in China using nighttime light remote sensing data . ISPRS International Journal of Geo-Information , 7 ( 7 ): 243 [ DOI: 10.3390/ijgi7070243 http://dx.doi.org/10.3390/ijgi7070243 ]

Jiang W , He G J , Long T F , Guo H X , Yin R Y , Leng W C , Liu H C and Wang G Z . 2018b . Potentiality of using Luojia 1 - 01 nighttime light imagery to investigate artificial light pollution. Sensors , 18 ( 9 ): 2900 [ DOI: 10.3390/s18092900 http://dx.doi.org/10.3390/s18092900 ]

Jin Y , Ge Y , Wang J H , Heuvelink G and Wang L . 2018 . Geographically weighted area-to-point regression kriging for spatial downscaling in remote sensing . Remote Sensing , 10 ( 4 ): 579 [ DOI: 10.3390/rs10040579 http://dx.doi.org/10.3390/rs10040579 ]

Kamrowski R L , Limpus C , Moloney J and Hamann M . 2012 . Coastal light pollution and marine turtles: assessing the magnitude of the problem . Endangered Species Research , 19 ( 1 ): 85 - 98 [ DOI: 10.3354/esr00462 http://dx.doi.org/10.3354/esr00462 ]

Kuechly H U , Kyba C C M , Ruhtz T , Lindemann C , Wolter C , Fischer J and Hölker F . 2012 . Aerial survey and spatial analysis of sources of light pollution in Berlin, Germany . Remote Sensing of Environment , 126 : 39 - 50 [ DOI: 10.1016/j.rse.2012.08.008 http://dx.doi.org/10.1016/j.rse.2012.08.008 ]

Kustas W P , Norman J M , Anderson M C and French A N . 2003 . Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship . Remote Sensing of Environment , 85 ( 4 ): 429 - 440 [ DOI: 10.1016/S0034-4257(03)00036-1 http://dx.doi.org/10.1016/S0034-4257(03)00036-1 ]

Levin N and Zhang Q L . 2017 . A global analysis of factors controlling VIIRS nighttime light levels from densely populated areas . Remote Sensing of Environment , 190 : 366 - 382 [ DOI: 10.1016/j.rse.2017.01.006 http://dx.doi.org/10.1016/j.rse.2017.01.006 ]

Li G Y , Gao J X , Li L Z and Hou P . 2020 . Human pressure dynamics in protected areas of China based on nighttime light . Global Ecology and Conservation , 24 : e 01222 [ DOI: 10.1016/j.gecco.2020.e01222 http://dx.doi.org/10.1016/j.gecco.2020.e01222 ]

Li X and Li D R . 2014 . Can night-time light images play a role in evaluating the Syrian Crisis? International Journal of Remote Sensing , 35 ( 18 ): 6648 - 6661 [ DOI: 10.1080/01431161.2014.971469 http://dx.doi.org/10.1080/01431161.2014.971469 ]

Li X M and Zhou W Q . 2018 . Dasymetric mapping of urban population in China based on radiance corrected DMSP-OLS nighttime light and land cover data . Science of the Total Environment , 643 : 1248 - 1256 [ DOI: 10.1016/j.scitotenv.2018.06.244 http://dx.doi.org/10.1016/j.scitotenv.2018.06.244 ]

Liang M G , Zhang L F , Wu S S , Zhu Y L , Dai Z , Wang Y Y , Qi J , Chen Y J and Du Z H . 2023 . A high-resolution land surface temperature downscaling method based on geographically weighted neural network regression . Remote Sensing , 15 ( 7 ): 1740 [ DOI: 10.3390/rs15071740 http://dx.doi.org/10.3390/rs15071740 ]

Liu S J , So C W and Pun C S J . 2025 . Analyzing the sources and variations of nighttime lights in Hong Kong from VIIRS monthly data . Remote Sensing , 17 ( 8 ): 1447 [ DOI: 10.3390/rs17081447 http://dx.doi.org/10.3390/rs17081447 ]

Liu S Q , Zhao X Z , Zhang F H , Qiu A G , Chen L J , Huang J , Chen S and Zhang S . 2022 . Spatial downscaling of NPP-VIIRS nighttime light data using multiscale geographically weighted regression and multi-source variables . Remote Sensing , 14 ( 24 ): 6400 [ DOI: 10.3390/rs14246400 http://dx.doi.org/10.3390/rs14246400 ]

Lowe D G . 1999 . Object recognition from local scale-invariant features // Proceedings of the Seventh IEEE International Conference on Computer Vision . Kerkyra : IEEE: 1150 - 1157 [ DOI: 10.1109/ICCV.1999.790410 http://dx.doi.org/10.1109/ICCV.1999.790410 ]

Lu B B , Charlton M , Harris P and Fotheringham A S . 2014 . Geographically weighted regression with a non-Euclidean distance metric: a case study using hedonic house price data . International Journal of Geographical Information Science , 28 ( 4 ): 660 - 681 [ DOI: 10.1080/13658816.2013.865739 http://dx.doi.org/10.1080/13658816.2013.865739 ]

Mpelasoka F S , Mullan A B and Heerdegen R G . 2001 . New Zealand climate change information derived by multivariate statistical and artificial neural networks approaches . International Journal of Climatology , 21 ( 11 ): 1415 - 1433 [ DOI: 10.1002/joc.617 http://dx.doi.org/10.1002/joc.617 ]

Tobler W R . 1970 . A computer movie simulating urban growth in the Detroit region . Economic Geography , 46 : 234 - 240 [ DOI: 10.2307/143141 http://dx.doi.org/10.2307/143141 ]

Tziokas N , Zhang C , Drolias G C and Atkinson P M . 2023 . Downscaling satellite night-time lights imagery to support within-city applications using a spatially non-stationary model . International Journal of Applied Earth Observation and Geoinformation , 122 : 103395 [ DOI: 10.1016/j.jag.2023.103395 http://dx.doi.org/10.1016/j.jag.2023.103395 ]

Van der Woerd H J and Wernand M R . 2018 . Hue-angle product for low to medium spatial resolution optical satellite sensors . Remote Sensing , 10 ( 2 ): 180 [ DOI: 10.3390/rs10020180 http://dx.doi.org/10.3390/rs10020180 ]

Xiang W L and Tan M H . 2017 . Changes in light pollution and the causing factors in China’s protected areas , 1992 - 2012 . Remote Sensing, 9 ( 10 ): 1026 [ DOI: 10.3390/rs9101026 http://dx.doi.org/10.3390/rs9101026 ]

Xu H Q . 2006 . Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery . International Journal of Remote Sensing , 27 ( 14 ): 3025 - 3033 [ DOI: 10.1080/01431160600589179 http://dx.doi.org/10.1080/01431160600589179 ]

Xu J H , Zhang F F , Jiang H , Hu H D , Zhong K W , Jing W L , Yang J and Jia B H . 2020 . Downscaling aster land surface temperature over urban areas with machine learning-based area-to-point regression kriging . Remote Sensing , 12 ( 7 ): 1082 [ DOI: 10.3390/rs12071082 http://dx.doi.org/10.3390/rs12071082 ]

Yang Y B , Cao C , Pan X , Li X L and Zhu X . 2017 . Downscaling land surface temperature in an arid area by using multiple remote sensing indices with random forest regression . Remote Sensing , 9 ( 8 ): 789 [ DOI: 10.3390/rs9080789 http://dx.doi.org/10.3390/rs9080789 ]

Ye Y , Huang L Y , Zheng Q M , Liang C X , Dong B Y , Deng J S and Han X Z . 2021 . A feasible framework to downscale NPP-VIIRS nighttime light imagery using multi-source spatial variables and geographically weighted regression . International Journal of Applied Earth Observation and Geoinformation , 104 : 102513 [ DOI: 10.1016/j.jag.2021.102513 http://dx.doi.org/10.1016/j.jag.2021.102513 ]

Yoon J , Oh B H , Cho J W , Kim Y T and Hwang S H . 2017 . Spatial downscaling of daily rainfall using geographically weighted regression model . Korean Society of Hazard Mitigation , 17 ( 3 ): 305 - 318 [ DOI: 10.9798/KOSHAM.2017.17.3.305 http://dx.doi.org/10.9798/KOSHAM.2017.17.3.305 ]

Zha Y , Gao J and Ni S . 2003 . Use of normalized difference built-up index in automatically mapping urban areas from TM imagery . International Journal of Remote Sensing , 24 ( 3 ): 583 - 594 [ DOI: 10.1080/01431160304987 http://dx.doi.org/10.1080/01431160304987 ]

Zhang D Q , Li D P , Zhou L and Wu J J . 2023 . Fine classification of UAV urban nighttime light images based on object-oriented approach . Sensors , 23 ( 4 ): 2180 [ DOI: 10.3390/s23042180 http://dx.doi.org/10.3390/s23042180 ]

Zhao H M , Zhou Y Y , Li X M , Liu C Y and Chen X L . 2019 . The influence of wind speed on infrared temperature in impervious surface areas based on in situ measurement data . GIScience and Remote Sensing , 56 ( 6 ): 843 - 863 [ DOI: 10.1080/15481603.2019.1572322 http://dx.doi.org/10.1080/15481603.2019.1572322 ]

Zhao X , Li D R , Li X , Zhao L X and Wu C Q . 2018 . Spatial and seasonal patterns of night-time lights in global ocean derived from VIIRS DNB images . International Journal of Remote Sensing , 39 ( 22 ): 8151 - 8181 [ DOI: 10.1080/01431161.2018.1482022 http://dx.doi.org/10.1080/01431161.2018.1482022 ]

Zheng Z H , Wu Z F , Chen Y B , Guo G H , Cao Z , Yang Z W and Marinello F . 2021 . Africa’s protected areas are brightening at night: a long-term light pollution monitor based on nighttime light imagery . Global Environmental Change , 69 : 102318 [ DOI: 10.1016/j.gloenvcha.2021.102318 http://dx.doi.org/10.1016/j.gloenvcha.2021.102318 ]

Zhu XL , Chen J , Gao F , Chen X H and Masek J G . 2010 . An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions . Remote Sensing of Environment , 114 ( 11 ): 2610 - 2623 [ DOI: 10.1016/j.rse.2010.05.032 http://dx.doi.org/10.1016/j.rse.2010.05.032 ]