中国区域MODIS与VIIRS气溶胶遥感产品的性能评估及其时空演变分析

周志高; 何利杰; 钟洋; 王伦澈; 覃文敏; 张翔

PDF(11705 KB)

中国环境科学 ›› 2024, Vol. 44 ›› Issue (8) : 4211-4229.

大气污染与控制

中国区域MODIS与VIIRS气溶胶遥感产品的性能评估及其时空演变分析

周志高¹, 何利杰², 钟洋³, 王伦澈⁴, 覃文敏⁴, 张翔⁴

作者信息 +

Evaluation and analysis of MODIS and VIIRS satellite aerosol optical depth products over China

ZHOU Zhi-gao¹, HE Li-jie², ZHONG Yang³, WANG Lun-che⁴, QIN Wen-min⁴, ZHANG Xiang⁴

Author information +

文章历史 +

摘要

基于气溶胶地基观测网络数据(AERONET),对MODIS与VIIRS的六种气溶胶光学厚度(AOD)产品在反演精度、空间覆盖范围、空间分布特征、时间反演频率以及对个例的极端天气事件的反演性能等方面进行评估,并从土地利用类型、气溶胶模态、季节变化等角度分析其误差来源.结果表明:从与AERONET地基观测对比来看,MODIS AOD产品反演精度优于VIIRS AOD反演产品,其中以MAIAC AOD产品精度最高,其相关系数R值为0.83,落入期望误差(EE)内的比例达65.03%;从空间覆盖范围以及时间反演频率来看,VIIRS AOD产品显著优于MODIS;从对个例的极端天气事件反演性能来看,MODIS AOD产品对沙尘事件的反演性能优于VIIRS,但所有产品对森林火灾的反演性能还需进一步提升;土地利用类型、气溶胶模态和季节变化对AOD产品反演精度有着显著的影响:六种AOD产品在建设用地的反演精度较高,在农用地的反演精度则较低;六种AOD产品对粗模态气溶胶以低估为主,而对细模态气溶胶则以高估为主;六种AOD产品在秋冬季节反演精度最高,夏季最低.

Abstract

This study evaluates six aerosol optical depth (AOD) products from MODIS and VIIRS in terms of retrieval accuracy, spatial coverage, spatial distribution, retrieval frequency, and their performance in retrieving extreme weather events based on the Aerosol Ground-based Observation Network Data (AERONET). It also analyzes the error sources from the perspectives of land use type, aerosol mode, and seasonal variation. The results indicate that MODIS AOD products outperform VIIRS AOD products. Among them, the MAIAC AOD product has the highest accuracy, with a correlation coefficient R value of 0.83 and a proportion of 65.03% falling within the expected error (EE); In terms of spatial coverage and retrieval frequency, VIIRS AOD products significantly surpass MODIS; From respect of retrieving extreme weather events for individual cases, MODIS AOD products demonstrate better performance in dust events, but all products require further improvement in retrieving forest fire events; The land use type, aerosol mode, and seasonal variation have a significant impact on the retrieval accuracy of the six AOD products. Specifically, the six AOD products show the following characteristics. They show higher accuracy in built-up land but lower accuracy in cropland, and they mainly underestimate coarse mode aerosols while overestimating fine mode aerosols. They achieve the highest retrieval accuracy in autumn and winter, while the lowest in summer. The expected research results can provide scientific guidance for the rational use of MODIS and VIIRS AOD products and the improvement of their retrieval algorithms.

导出引用

周志高, 何利杰, 钟洋, 王伦澈, 覃文敏, 张翔. 中国区域MODIS与VIIRS气溶胶遥感产品的性能评估及其时空演变分析[J]. 中国环境科学. 2024, 44(8): 4211-4229

ZHOU Zhi-gao, HE Li-jie, ZHONG Yang, WANG Lun-che, QIN Wen-min, ZHANG Xiang. Evaluation and analysis of MODIS and VIIRS satellite aerosol optical depth products over China[J]. China Environmental Science. 2024, 44(8): 4211-4229

中图分类号： X87

参考文献

[1] Zhou Z G, Lin A W, Wang L C, et al. Estimation of the losses in potential concentrated solar thermal power electricity production due to air pollution in China [J]. Science of The Total Environment, 2021,784:147214.
[2] 李占清.气溶胶对中国天气,气候和环境影响综述[J]. 大气科学学报, 2020,43(1):17. Li Z Q. Impact of aerosols on the weather, climate and environment of China:an overview [J]. Transactions of Atmospheric Sciences, 2020, 43(1):17.
[3] Wei J, Li Z Q, Lyapustin A, et al. Reconstructing 1-km-resolution high-quality PM_2.5 data records from 2000 to 2018 in China: spatiotemporal variations and policy implications [J]. Remote Sensing of Environment, 2021,252(1):112136.
[4] Yang Y, Ruan Z L, Wang X J, et al. Short-term and long-term exposures to fine particulate matter constituents and health: A systematic review and meta-analysis [J]. Environmental Pollution, 2019,247(4):874-882.
[5] 李恺霖,张春桂,王宏,等.基于葵花-8卫星的东南沿海气溶胶时空分布及其变化[J]. 应用海洋学学报, 2019,38(3):318-328. Li K L, Zhang C G, Wang H, et al. Spatial and temporal distribution and variation of aerosl optical depth in coastal southeast China based on Himawari-8satellite [J]. Journal of Applied Oceanography, 2019, 38(3):318-328.
[6] Wei J, Li Z Q, Sun L, et al. MODIS Collection 6.13km resolution aerosol optical depth product: global evaluation and uncertainty analysis [J]. Atmospheric Environment, 2020,240:117768.
[7] 唐燕,许睿,孟繁玥.中国东部典型城市群AOD时空演变及预测[J]. 大气与环境光学学报, 2021,16(4):320-330. Tang Y, Xu R, Meng F Y. Spatiotemporal evolution and prediction of AOD in typical urban agglomerations in Eastern China [J]. Journal of Atmospheric and Environmental Optics, 2021,16(4):320-330.
[8] 刘海知,郭海燕,马振峰,等.2001~2017年全国气溶胶光学厚度时空分布及变化趋势[J]. 环境科学, 2019,40(9):3886-3897. Liu H Z, Guo H Y, Ma Z F, et al. Temporal-spatial characteristics and variability in aerosol optical depth over China during 2001~2017[J]. Environmental Science, 2019,40(9):3886-3897.
[9] He L J, Wang L C, Lin A W, et al. Performance of the NPP-VIIRS and aqua-MODIS aerosol optical depth products over the Yangtze River Basin [J]. Remote Sensing, 2018,10(1):117.
[10] Sayer A. M, Hsu N. C, C. B, et al. Validation and uncertainty estimates for MODIS Collection 6"Deep Blue" aerosol data [J]. Journal of Geophysical Research: Atmospheres, 2013,118(14):1-9.
[11] Che H Z, Gui K, Xia X A, et al. Large contribution of meteorological factors to inter-decadal changes in regional aerosol optical depth [J]. Atmospheric Chemistry and Physics, 2019,19(16):10497-10523.
[12] 王晓玲,吕睿,王艳杰,等.基于MODIS C6产品研究湖北省气溶胶光学特性的时空分布[J]. 生态环境学报, 2018,27(6):1099-1106. Wang X L, Lv R, Wang Y J, et al. Seasonal variation and spatial distribution of columnar aerosol optical properties in collection 6aerosol products derived from MODIS over Hubei province [J]. Ecology and Environmental Science, 2018,27(6):1099-1106.
[13] 王海林,刘琼,陈勇航,等.MODIS C006气溶胶光学厚度产品在京津冀典型环境背景下的适用性[J]. 环境科学, 2019,40(1):44-53. Wang H L, Liu Q, Chen Y H, et al. Applicability of MODIS C006 aerosol products in a typical environmental area of the Beijing- Tianjin-Hebei region [J]. Environmental Science, 2019,40(1):44-53.
[14] 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.
[15] Wei J, Peng Y R, Mahmood R, et al. Intercomparison in spatial distributions and temporal trends derived from multi-source satellite aerosol products [J]. Atmospheric Chemistry and Physics, 2019,19(10): 7183-7207.
[16] Osgouei P E, Roberts G, Kaya S, et al. Evaluation and comparison of MODIS and VIIRS aerosol optical depth (AOD) products over regions in the Eastern Mediterranean and the Black Sea [J]. Atmospheric Environment, 2022,268:118784.
[17] Sayer A M, Munchak L A, Hsu N C, et al. MODIS Collection 6 aerosol products: Comparison between Aqua's e-Deep Blue, Dark Target, and "merged" data sets, and usage recommendations [J]. Journal of Geophysical Research: Atmospheres, 2014,119(24):13965- 13989.
[18] Tao M H, Chen L F, Wang Z F, et al. Evaluation of MODIS deep blue aerosol algorithm in desert region of East Asia: ground validation and intercomparison [J]. Journal of Geophysical Research: Atmospheres, 2017,122(19):10357-10368.
[19] Tao M H, 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.
[20] Hsu N C, Lee J, Sayer A M, et al. VIIRS deep blue aerosol products over land: extending the EOS long-term aerosol data records [J]. Journal of Geophysical Research: Atmospheres, 2019,124(7):4026- 4053.
[21] 陈香月,丁建丽,王敬哲,等.MODIS MAIAC高分辨率气溶胶光学厚度产品在干旱区的适用性研究[J]. 遥感学报, 2023,27(2):406-419. Chen X Y, Ding J L, Wang J Z, et al. Validation of the fine resolution of the MODIS MAIAC aerosol optical depth product over arid areas [J]. National Remote Sensing Bulletin, 2023,27(2):406-419.
[22] He L J, Wang L C, Li Z Q, et al. VIIRS Environmental Data Record and Deep Blue aerosol products: validation, comparison, and spatiotemporal variations from 2013 to 2018 in China [J]. Atmospheric Environment, 2021,250:118265.
[23] 毛颖,郑君亮,丘仲锋,等.黄渤海气溶胶光学厚度的多源卫星遥感产品精度分析和时空分布特征研究[J]. 环境科学学报, 2021,41(7): 2550-2559. Mao Y, Zheng J L, Qiu Z F, et al. Precision analysis and spatiotemporal distribution characteristics from multi-source satellite aerosol optical depth data in the Yellow Sea and Bohai Sea [J]. Acta Scientiac Circumstantias, 2021,41(7):2550-2559.
[24] 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 B Z, 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.
[25] 王安怡,康平,张洋,等.2003~2018年四川盆地气溶胶光学厚度空间分异及驱动因子[J]. 中国环境科学, 2022,42(2):528-538. Wang A Y, Kang P, Zhang Y, et al. Spatial differentiation and driving factors of aerosol optical depth in Sichuan Basin from 2003 to 2018[J]. China Environmental Science, 2022,42(2):528-538.
[26] 王萍,汤庆新,梁天全,等.基于MODIS数据的山东省近十年AOD时空变化特征[J]. 中国环境科学, 2021,41(11):5019-5026. Wang P, Tang Q X, Liang T Q, et al. Spatiotemporal variation of AOD in Shandong Province in recent ten years based on MODIS data [J]. China Environmental Science, 2021,41(11):5019-5026.
[27] 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.
[28] 金囝囡,杨兴川,晏星,等.京津冀及周边MAIAC AOD和PM_2.5质量浓度特征及相关性分析[J]. 环境科学, 2021,42(6):2604-2615. Jin N N, Yang X C, Yan X, et al. MAIAC AOD and PM_2.5 mass concentrations characteristics and correlation analysis in Beijing- Tianjin-Hebei and surrounding areas [J]. Environmental Science, 2021,42(6):2604-2615.
[29] 郭霖,孟飞,马明亮.华北平原AOD时空演化与影响因素[J]. 环境科学, 2022,43(7):3483-3493. Guo L, Meng F, Ma M L. Spatiotemporal variation and influencing factors of AOD in the North China Plain [J]. Environmental Science, 2022,43(7):3483-3493.
[30] Tao M H, Li R, Wang L C, et al. A critical view of long-term AVHRR aerosol data record in China: Retrieval frequency and heavy pollution [J]. Atmospheric Environment, 2020,223:117246.
[31] 韩阳,康凌,宋宇.2000~2019年东北三省气溶胶光学厚度的时空分布特征[J]. 北京大学学报:自然科学版, 2021,57(6):1027-1034. Han Y, Kang L, Song Y. Spatial-temporal distribution of aerosol optical depth over Northeastern China during 2000~2019[J]. Acta Scientiarum Naturalium Universitatis Pekinensis, 2021,57(6):1027- 1034.
[32] 张颖蕾,崔希民.京津冀地区MODIS 3km气溶胶光学厚度产品与10km产品的对比分析[J]. 环境科学学报, 2020,40(2):429-437. Zhang Y L, Cui X M. A comparative study of MODIS 3km aerosol optical depth products and 10km products in the Beijing-Tianjin- Hebei region [J]. Acta Scientiac Circumstantias, 2020,40(2):429-437.
[33] Wang Y, Yuan Q Q, Shen H F, et al. Investigating multiple aerosol optical depth products from MODIS and VIIRS over Asia: Evaluation, comparison, and merging [J]. Atmospheric Environment, 2020,230(6): 117548.
[34] Tao M H, Gui L, Li R, et al. Tracking prevailing dust aerosol over the air pollution in central China with integrated satellite and ground observations [J]. Atmospheric Environment, 2021,253(3):118369.
[35] 陈香月,丁建丽,王敬哲,等.艾比湖流域气溶胶光学厚度时空演变及影响因素[J]. 环境科学, 2019,40(11):4824-4832. Chen X Y, Ding J L, Wang J Z, et al. Spatiotemporal evolution and driving mechanism of aerosol optical depth in the Ebinur Lake Basin [J]. Environmental Science, 2019,40(11):4824-4832.
[36] 陈翔,汪洋,周佩,等.中国地区MODIS Terra/Aqua MAIAC气溶胶光学厚度(AOD)产品反演误差对比分析[J]. 环境科学学报, 2023,43(7):220-232. Chen X, Wang Y, Zhou P, et al. Comparative analysis of retrieval errors of MODIS Terra/Aqua MAIAC aerosol optical depth (AOD) products in China [J]. Acta Scientiac Circumstantias, 2023,43(7):220- 232.