Impact of spatial variability on the validation of ocean chlorophyll-a concentration remote sensing product

JIANG Jin'gang; FENG Huiyun; ZHANG Yaguo; HE Xianqiang

doi:10.3969/j.issn.1001-909X.2021.01.002

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Journal of Marine Sciences ›› 2021, Vol. 39 ›› Issue (1) : 9-19. DOI: 10.3969/j.issn.1001-909X.2021.01.002

Impact of spatial variability on the validation of ocean chlorophyll-a concentration remote sensing product

JIANG Jin'gang^1,2, FENG Huiyun^*2, ZHANG Yaguo², HE Xianqiang^1,3

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Abstract

Ocean chlorophyll-a concentration remote sensing product is important data sources for the studies of ocean primary productivity and carbon sequestration capacity of ocean ecosystems. In order to ensure the data reliability, it is particularly important to verify the accuracy of remote sensing product and analyze the causes of validation errors. In the verification of remote sensing products, due to the existence of spatial variation, the measured data within the remote sensing pixel have different statistical distribution characteristics, and thus produce different statistical error results. In this work, chlorophyll-a concentration remote sensing products derived from MODIS-Aqua, MODIS-Terra, MERIS and SeaWiFS satellite sensors, were quantitatively analyzed on the relationship between spatial variability and validation accuracy. The results of statistical analysis show that spatial variation is one of the direct causes of validation error. The relationship between Mean Absolute Percentage Error (MAPE) and spatial Coefficient of Variation (CV) is in accordance with power exponential model. When CV<0.05, MAPE increases significantly with CV, and when CV>0.15, MAPE changes gently. The validation results of different chlorophyll-a concentration remote sensing products show that SeaWiFS has the highest accuracy, MERIS takes the second place, and MODIS-Terra has the worst accuracy.

Key words

spatial variability / satellite remote sensing / chlorophyll-a / validation

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JIANG Jin'gang, FENG Huiyun, ZHANG Yaguo, HE Xianqiang. Impact of spatial variability on the validation of ocean chlorophyll-a concentration remote sensing product[J]. Journal of Marine Sciences. 2021, 39(1): 9-19 https://doi.org/10.3969/j.issn.1001-909X.2021.01.002

References

[1] 林明森,张有广,袁欣哲.海洋遥感卫星发展历程与趋势展望[J].海洋学报,2015,37(1):1-10.
LIN Mingsen, ZHANG Youguang, YUAN Xinzhe. The development course and trend of ocean remote sensing satellite[J]. Haiyang Xuebao,2015,37(1):1-10.
[2] 陈清莲,唐军武,王项南.海洋光学遥感器的辐射定标与数据真实性检验综述[J].海洋技术,1998,17(3):14-26.
CHEN Qinglian, TANG Junwu, WANG Xiangnan. Review on the calibration and validation for ocean color sensing[J]. Ocean Technology, 1998, 17(3): 14-26.
[3] WERDELL P J, BAILEY S W. An improved bio-optical data set for ocean color algorithm development and satellite data product validation[J]. Remote Sensing of Environment, 2005, 98(1): 122-140.
[4] BAILEY S W, WERDELL P J. A multi-sensor approach for the on-orbit validation of ocean color satellite data products[J]. Remote Sensing of Environment, 2006, 102(2): 12-23.
[5] MOORE T S, CAMPBELL J W, DOWELL M D. A class-based approach to characterizing and mapping the uncertainty of the MODIS ocean chlorophyll product[J]. Remote Sensing of Environment, 2009, 113(11): 2424-2430.
[6] CUI Tingwei, ZHANG Jie, TANG Junwu, et al. Assessment of satellite ocean color products of MERIS, MODIS and SeaWiFS along the East China Coast(in the Yellow Sea and East China Sea)[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2014, 87: 137-151.
[7] CUI T W, ZHANG J, WANG K,et al. Remote sensing of chlorophyll a concentration in turbid coastal waters based on a global optical water classification system[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2020, 163(5): 187-201.
[8] PEREIRA E S, GARCIA C. Evaluation of satellite-derived MODIS chlorophyll algorithms in the northern Antarctic Peninsula[J]. Deep-Sea Research, 2018, 149(3): 124-137.
[9] WANG Menghua, SON S H, WEI Shi. Evaluation of MODIS SWIR and NIR-SWIR atmospheric correction algorithms using SeaBASS data[J]. Remote Sensing of Environment, 2009, 113(3): 635-644.
[10] 叶小敏,丁静,丘仲锋,等.水色水温遥感产品真实性检验误差分析[J].遥感技术与应用,2015,30(4):719-724.
YE Xiaomin, DING Jing, QIU Zhongfeng, et al. Error analysis for the validation of ocean color and sea surface temperature remote sensing products[J]. Remote Sensing Technology and Application, 2015, 30(4): 719-724.
[11] 蒋锦刚,徐曜,聂晨晖,等.海表面温度时空变异特征及对验证误差影响[J].遥感学报,2019,23(2):336-348.
JIANG Jin'gang, XU Yao, NIE Chenhui,et al. Influence of temporal and spatial variation of sea surface temperature on validation errors[J]. Journal of Remote Sensing, 2019,23(2):336-348.
[12] 李豪,何贤强,丁静,等.春季辽东湾静止轨道海洋水色遥感产品的真实性检验[J].光学学报,2016,36(4):9-20.
LI Hao, HE Xianqiang, DING Jing,et al. Validation of the remote sensing products retrieved by geostationary ocean color imager in Liaodong Bay in spring[J]. Acta Optica Sinica, 2016, 36(4): 9-20.
[13] O'REILLY J E, MARITORENA S, MITCHELL B G, et al. Ocean color chlorophyll algorithms for SeaWiFS[J]. Journal of Geophysical Research, 1998, 103(C11): 24937-24953.
[14] O'REILLY J E, WERDELL P J. Chlorophyll algorithms for ocean color sensors-OC4, OC5 & OC6[J].Remote Sensing of Environment, 2019, 229(8): 32-47.
[15] HU Chuanmin. An empirical approach to derive MODIS ocean color patterns under severe sun glint[J].Geophysical Research Letters, 2011, 38(L01603): 1-5.
[16] HU Chuanmin, LEE Zhongping, FRANZ B. Chlorophyll-a algorithms for oligotrophic oceans: A novel approach based on three-band reflectance difference[J]. Journal of Geophysical Research, 117(C1): 1-25.
[17] 沈永林,刘修国,吴立新,等.Hyperion高光谱影像坏线修复的局部空间-光谱相似性测度方法[J].武汉大学学报(信息科学版),2017,42(4):456-462.
SHEN Yonglin, LIU Xiuguo, WU Lixin, et al.A local spectral-spatial similarity measure for bad line correction in hyperion hyperspectral data[J]. Geomatics and Information Science of Wuhan University, 2017, 42(4): 456-462.
[18] JAY S, GUILLAUME M. A novel maximum likelihood based method for mapping depth and water quality from hyperspectral remote-sensing data[J]. Remote Sensing of Environment, 2014, 147(18): 121-132.
[19] ZIBORDI G, HOLBEN B, SLUTSKER I,et al.AERONET-OC: A network for the validation of ocean color primary products[J]. Jornal of Atmospheric and Oceanic Technology, 2009, 26: 1634-1651.
[20] 文斐,孙晓霞,郑珊,等.2011年春、夏季黄、东海叶绿素a和初级生产力的时空变化特征[J].海洋与湖沼,2012,43(3):438-444.
WEN Fei, SUN Xiaoxia, ZHENG Shan, et al. Spatial and seasonal variations of chlorophyll a and primary productivity in spring and summer in the Yellow Sea and East China Sea[J]. Oceanologla et Limnologla Sinica, 2012, 43(3): 438-444.
[21] 陈法锦,曾珍,孟亚飞,等.2013年夏季琼东海域营养盐与叶绿素a的周日波动及其影响因素[J].海洋学报,2016,38(4):76-83.
CHEN Fajin, ZENG Zhen, MENG Yafei, et al. Diel variation of nutrients and chlorophyll a concentration in the Qiongdong sea region during the summer of 2013[J]. Haiyang Xuebao, 2016, 38(4): 76-83.
[22] SÁ C, D'ALIMONTE D, BRITO A C,et al.Validation of standard and alternative satellite ocean-color chlorophyll products off Western Iberia[J].Remote Sensing of Environment, 2015, 168(10): 403-419.
[23] HE Junyu, CHEN Yijun, WU Jiaping, et al. Space-time chlorophyll-a retrieval in optically complex waters that accounts for remote sensing and modeling uncertainties and improves remote estimation accuracy[J]. Water Research, 2020, 171(3): 115403.
[24] SINGHAL G, BANSOD B, MATHEW L,et al. Chlorophyll estimation using multi-spectral unmanned aerial system based on machine learning techniques[J]. Remote Sensing Applications: Society and Environment, 2019, 15(8): 100235.
[25] YUAN Qiangqiang, SHEN Huanfeng, LI Tongwen,et al.Deep learning in environmental remote sensing: Achievements and challenges[J]. Remote Sensing of Environment, 2020, 241(5): 111716.
[26] MÉLIN F, VANTREPOTTE V, CHUPRIN A, et al. Assessing the fitness-for-purpose of satellite multi-mission ocean color climate data records: A protocol applied to OC-CCI chlorophyll-a data[J].Remote Sensing of Environment, 2017,203(12): 139-151.