Abstract：The spatial resolution and accuracy of existing aerosol optical depth (AOD) products cannot satisfy the demand for fine-scale air pollution control. To make up for such deficiencies, this study proposed a novel modeling approach named Statistical Downscaling model combined with Bias Correction (SDBC). Based on the hypothesis of "spatial scale invariance", this model introduced additional spatial information of driving factors to downscale AOD products and further improved the accuracy of downscaled results using bias correction. Take 1-km resolution MAIAC AOD products as an example, we examined the proposed model in three typical areas: Beijing, Greater Bay Area, and Taiwan Island. Results showed that: (1) Digital elevation model, normalized difference vegetation index, population, and land cover were the fine driving factors affecting AOD variations. Taking these factors into consideration, the spatial downscaling model can effectively improve the spatial resolution (1km) of the original product to 500m, and the highest validated R2 was up to 0.88. (2) In addition, the accuracy of downscaled AOD products can be further improved by bias correction coupling with satellite observation geometry, quality flag, atmospheric water vapor column, aerosol model, and other factors. The validated R2 of the three areas were all larger than 0.85, and the highest was 0.93. (3) The information entropy evaluation results showed the 500m AOD product generated by the SDBC model increased the spatial information of the original MAIAC AOD product. Based on retaining the AOD spatial distribution pattern of the MAIAC AOD product, the details and texture features were enhanced, and the boundary phenomenon and mosaic effect were also eliminated. These results confirm that the SDBC model can effectively improve the spatial resolution and accuracy of existing AOD products simultaneously, which can lift the operational capability of remote sensing precision monitoring of atmospheric pollution in China.
张华玉, 邹滨, 刘宁, 李莎. 空间分辨率与精度协同改进的卫星AOD产品降尺度模型[J]. 中国环境科学, 2022, 42(9): 4033-4042.
ZHANG Hua-yu, ZOU Bin, LIU Ning, LI Sha. A downscaling model for satellite AOD product improvement in spatial resolution and accuracy. CHINA ENVIRONMENTAL SCIENCECE, 2022, 42(9): 4033-4042.
毛节泰,张军华,王美华.中国大气气溶胶研究综述 [J]. 气象学报, 2002,60(5):625-634. Mao J T, Zhang J H, Wang M H. Summary comment on research of atmospheric aerosol in China [J]. Acta Meteorologica Sinica, 2002,60(5):625-634.
[2]
黄明祥,魏 斌,郝千婷,等.PM2.5遥感反演技术研究进展 [J]. 环境污染与防治, 2015,37(10):70-76,85. Huang M X, Wei B, Hao Q T, et al. A review on research of PM2.5 retrieval by remote sensing technology [J]. Environmental Pollution and Control, 2015,37(10):70-76,85.
[3]
Wei X L, Chang N B, Bai K X, et al. Satellite remote sensing of aerosol optical depth: advances, challenges, and perspectives [J]. Critical Reviews in Environmental Science and Technology, 2020,50(16):1640-1725.
[4]
Tian X P, Liu Q, Gao Z Q, et al. Improving MODIS aerosol estimates over land with the surface BRDF reflectances using the 3-D discrete cosine transform and RossThick-LiSparse models [J]. IEEE Transactions on Geoscience and Remote Sensing, 2021,59(12):9851-9860.
[5]
王中挺,厉 青,陶金花,等.环境一号卫星CCD相机应用于陆地气溶胶的监测 [J]. 中国环境科学, 2009,29(9):902-907. Wang Z T, Li Q, Tao J H, et al. Monitoring of aerosol optical depth over land surface using CCD camera on HJ-1satellite [J]. China Environmental Science, 2009,29(9):902-907.
[6]
Bao F W, Gu X F, Cheng T H, et al. High-Spatial-Resolution aerosol optical properties retrieval algorithm using Chinese High-Resolution Earth Observation Satellite I [J]. IEEE Transactions on Geoscience and Remote Sensing, 2016,54(9):5544-5552.
[7]
Lin H, Li S W, Xing J, et al. Fusing retrievals of high resolution aerosol optical depth from Landsat-8and Sentinel-2observations over urban areas [J]. Remote Sensing, 2021,13(20):4140-4160.
[8]
Li Z B, Roy D P, Zhang H K, et al. Evaluation of Landsat-8and Sentinel-2A aerosol optical depth retrievals across Chinese cities and implications for medium spatial resolution urban aerosol monitoring [J]. Remote Sensing, 2019,11(2):122-136.
[9]
夏 天,吴文嘉,吴文斌,等.地理科学中数据空间重构最新研究进展 [J]. 经济地理, 2020,40(11):47-55,94. Xia T, Wu W J, Wu W B, et al. Research progress of geographic data by space reconstruction [J]. Economic Geography, 2020,40(11):47- 55,94.
[10]
全金玲,占文凤,陈云浩,等.遥感地表温度降尺度方法比较——性能对比及适应性评价 [J]. 遥感学报, 2013,17(2):374-387,361-373. Quan J L, Zhan W F, Chen Y H, et al. Downscaling remotely sensed land surface temperatures: A comparison of typical methods [J]. Journal of Remote Sensing, 2013,17(2):374-387,361-373.
[11]
舒建川,蒋兴文,黄小梅,等.中国西南夏季降水预测的统计降尺度建模分析 [J]. 高原气象, 2019,38(2):349-358. Shu J C, Jiang X W, Huang X M, et al. Statistical downscaling modeling analysis of summer precipitation in southwest China [J]. Plateau Meteorology, 2019,38(2):349-358.
[12]
孙 灏,周柏池,李 欢,等.耦合MOD16和SMAP的微波土壤湿度降尺度研究 [J]. 遥感学报, 2021,25(3):776-790. Sun H, Zhou B C, Li H, et al. A primary study on downscaling microwave soil moisture with MOD16 and SMAP [J]. Journal of Remote Sensing, 2021,25(3):776-790.
[13]
白淑英,史建桥,卜 军,等.近年来长江流域气溶胶光学厚度时空变化特征分析 [J]. 生态环境学报, 2012,21(9):1567-1573. Bai S Y, Shi J Q, Bo J, et al. Spatio-temporal variations of aerosol optical depth in the Yangtze River Basin during 2000~2011 [J]. Ecology and Environmental Sciences, 2012,21(9):1567-1573.
[14]
孙忠保,程先富,夏晓圣.中国气溶胶光学厚度的时空分布及影响因素分析 [J]. 中国环境科学, 2021,41(10):4466-4475. Sun Z B, Cheng X F, Xia X S. Spatial-temporal distribution and impact factors of aerosol optical depth over China [J]. China Environmental Science, 2021,41(10):4466-4475.
[15]
Kustas W P, Norman J M, Anderson M C, et al. Estimating subpixel surface temperatures and energy fluxes from the vegetation index–radiometric temperature relationship [J]. Remote Sensing of Environment, 2003,85(4):429-440.
[16]
Breiman L. Random Forests [J]. Machine Learning, 2001,45(1):5-32.
[17]
Li L F, Franklin M, Girguis M, et al. Spatiotemporal imputation of MAIAC AOD using deep learning with downscaling [J]. Remote Sensing of Environment, 2020,237:1-17.
[18]
Lipponen A, Kolehmainen V, Kolmonen P, et al. Model-enforced post-process correction of satellite aerosol retrievals [J]. Atmospheric Measurement Techniques, 2021,14(4):2981-2992.
[19]
王祎婷,谢东辉,李小文.构造地理要素趋势面的尺度转换普适性方法探讨 [J]. 遥感学报, 2014,18(6):1139-1146. Wang Y T, Xie D H, Li X W. Universal scaling methodology in remote sensing science by constructing geographic trend surface [J]. Journal of Remote Sensing, 2014,18(6):1139-1146.
[20]
Lary D J, Remer L A, Macneill D, et al. Machine learning and bias correction of MODIS aerosol optical depth [J]. IEEE Geoscience and Remote Sensing Letters, 2009,6(4):694-698.
[21]
Sun Y L, Wang Z F, Du W, et al. Long-term real-time measurements of aerosol particle composition in Beijing, China: seasonal variations, meteorological effects, and source analysis [J]. Atmospheric Chemistry and Physics, 2015,15(17):10149-10165.
[22]
Li L, Qian J, Ou C Q, et al. Spatial and temporal analysis of Air Pollution Index and its timescale-dependent relationship with meteorological factors in Guangzhou, China, 2001~2011 [J]. Environmental Pollution, 2014,190:75-81.
[23]
Raes F, Dingenen R V, Vignati E, et al. Formation and cycling of aerosols in the global troposphere [J]. Atmospheric Environment, 2000,34(25):4215-4220.
[24]
Cheng L, Li L, Chen L Q, et al. Spatiotemporal variability and influencing factors of aerosol optical depth over the Pan Yangtze River Delta during the 2014~2017 period [J]. International Journal of Environmental Research and Public Health, 2019,16(19):3522-3547.
[25]
景 悦,孙艳玲,高 爽,等.京津冀地区AOD时空变化及影响因子的地理探测 [J]. 干旱区地理, 2020,43(1):87-98. Jing Y, Sun Y L, Gao S, Chen L, et al. Spatiotemporal variations of AOD and geographical detection of its influence factors in Beijing- Tianjin-Hebei region [J]. Arid Land Geography, 2020,43(1):87-98.
[26]
He Q, Zhang M, Huang B. Spatio-temporal variation and impact factors analysis of satellite-based aerosol optical depth over China from 2002 to 2015 [J]. Atmospheric Environment, 2016,129:79-90.
[27]
Aldabash M, Bektas Balcik F, Glantz P. Validation of MODIS C6.1and MERRA-2AOD using AERONET observations: A comparative study over Turkey [J]. Atmosphere, 2020,11(9):905-922.
[28]
Li L L, Wang Y P. What drives the aerosol distribution in Guangdong - the most developed province in Southern China? [J]. Scientific Reports, 2014,4:5972-5980.
[29]
Liu N, Zou B, Feng H H, et al. Evaluation and comparison of MAIAC, DT and DB aerosol products over China [J]. Atmospheric Chemistry and Physics, 2019,19(12):8243-8268.
[30]
Mhawish A, Banerjee T, Sorek-Hamer M, et al. Comparison and evaluation of MODIS Multi-Angle Implementation of Atmospheric Correction (MAIAC) aerosol product over South Asia [J]. Remote Sensing of Environment, 2019,224:12-28.
[31]
Tao M, Wang J, Li R, et al. Performance of MODIS high-resolution MAIAC aerosol algorithm in China: Characterization and limitation [J]. Atmospheric Environment, 2019,213:159-169.
[32]
Yang J, Huang X. 30m annual land cover and its dynamics in China from 1990 to 2019 [J]. Earth System Science Data, 2021,13(8):3907-3925.
[33]
Liu Y, Sarnat J A, Coull B A, et al. Validation of Multiangle Imaging Spectroradiometer (MISR) aerosol optical thickness measurements using Aerosol Robotic Network (AERONET) observations over the contiguous United States [J]. Journal of Geophysical Research: Atmospheres, 2004,109(6):1-9.
[34]
王劲峰,徐成东.地理探测器:原理与展望 [J]. 地理学报, 2017, 72(1):116-134. Wang J F, Xu C D. Geodetector: Principle and prospective [J]. Acta Geographica Sinica, 2017,72(1):116-134.
[35]
李忠宾,王 楠,张自力,等.中国地区MODIS气溶胶光学厚度产品综合验证及分析 [J]. 中国环境科学, 2020,40(10):4190-4204. Li Z B, Wang N, Zhang Z L, et al. Validation and analyzation of MODIS aerosol optical depth products over China [J]. China Environmental Science, 2020,40(10):4190-4204.
[36]
沈焕锋,李同文.大气PM2.5遥感制图研究进展 [J]. 测绘学报, 2019,48(12):1624-1635. Shen H F, Li T W. Progress of remote sensing mapping of atmospheric PM2.5 [J]. Acta Geodaetica et Cartographica Sinica, 2019,48(12): 1624-1635.
[37]
杨艳春,李 娇,王阳萍.图像融合质量评价方法研究综述 [J]. 计算机科学与探索, 2018,12(7):1021-1035. Yang Y C, Li J, Wang Y P. Review of image fusion quality evaluation methods [J]. Journal of Frontiers of Computer Science and Technology, 2018,12(7):1021-1035.
[38]
吴一全,史骏鹏.基于多尺度Retinex的非下采样Contourlet域图像增强 [J]. 光学学报, 2015,35(3):87-96. Wu Y Q, Shi J P. Image enhancement in non-subsampled contourlet transform domain based on multi-scale retinex [J]. Acta Optica Sinic, 2015,35(3):87-96.
[39]
Lyapustin A, Wang Y, Korkin S, et al. MODIS Collection 6MAIAC algorithm [J]. Atmospheric Measurement Techniques, 2018,11(10): 5741-5765.
[40]
张润森,濮励杰,刘 振.土地利用/覆被变化的大气环境效应研究进展 [J]. 地域研究与开发, 2013,32(4):123-128. Zhang R S, Pu L J, Liu Z. Advances in research on atmospheric environment effects of land use and land cover change [J]. Areal Research and Development, 2013,32(4):123-128.
[41]
吴健生,王 茜,李嘉诚,等.PM2.5浓度空间分异模拟模型对比:以京津冀地区为例 [J]. 环境科学, 2017,38(6):2191-2201. Wu J S, Wang X, Li J C, et al. Comparison of models on spatial variation of PM2.5 concentration: A case of Beijing-Tianjin-Hebei Region [J]. Environmental Science, 2017,38(6):2191-2201.
