浏览全部资源
扫码关注微信
Published： 07 March 2021 ，
扫 描 看 全 文
史凯琦，邹斌，陈树果，薛程，石立坚，张亭禄.2021.中分辨率成像光谱仪的海冰密集度遥感反演.遥感学报，25（3）： 753-764
Shi K Q，Zou B，Chen S G，Xue C，Shi L J and Zhang T L. 2021. Remote sensing inversion of sea ice concentration by a middle-resolution imaging spectrometer. National Remote Sensing Bulletin， 25（3）：753-764
海冰信息在船舶运输、天气预报和全球气候预测等领域都起着重要作用。一直以来微波遥感是卫星监测海冰密集度的主要手段，目前基于可见光遥感的中分辨率海冰密集度产品还较少，其中只有NOAA发布了相关业务化产品，但其所采用的算法对低密集度海冰反演准确性仍存在提升空间。本文在Liu提出的算法基础上进行改进，提出了最邻近像素法确定纯冰典型反射率的改进算法，使用MODIS数据作为数据源计算海冰密集度，并使用30 m空间分辨率的Landsat 8 OLI数据作为验证数据进行对比验证。结果表明改进算法可以提高低密集度海冰的反演准确性，改善Liu算法存在过高估计的不足，在海冰密集度0—50%的情况下，Liu算法的平均偏差为13%，标准偏差为38%，改进算法的平均偏差为5%，标准偏差为32%；在海冰密集度0—100%的情况下，Liu算法的平均偏差为4%，标准偏差为32%，改进算法的平均偏差为-3%，标准偏差为28%。针对冰水过渡、碎冰覆盖等低密集度海冰区域，改进算法准确性更高。
Sea ice concentration
which refers to the percentage of sea ice in an area
is an important parameter describing the characteristics of sea ice. Remote sensing monitoring of sea ice is crucial to understand the role of polar regions in the global climate system and global warming. Sea ice information is of great importance in ship transportation
weather forecasting
and global climate forecast. At present
passive microwave radiometers are the main means of monitoring sea ice concentration; however
because microwaves present wavelength limitations
conventional sea ice concentration products cannot be used in practical applications in small areas. Visible-light-infrared remote sensing can retrieve sea ice concentrations
and its advantages over other sensing methods include high spatial resolution.
This work focuses on the development of a sea ice concentration algorithm suitable for medium-resolution images. Few medium-resolution sea ice concentration products based on visible-light remote sensing are available
and only NOAA has released relevant operational products. However
the accuracy of its algorithm for low-concentration sea ice inversion is low. This paper proposes an improved algorithm based on the existing algorithm to determine the ice node via the nearest-pixel method. MODIS data are used as a data source to calculate the sea ice concentration
and Landsat 8 OLI data with a spatial resolution of 30 m are used for comparative verification.
Results show that the improved algorithm can improve the inversion accuracy of low-concentration sea ice. The Liu algorithm has the disadvantage of overestimation. In the case of sea ice concentrations of 0% — 50%
the average deviation of the Liu algorithm is 13%
and its standard deviation is 38%. By comparison
the average deviation of the improved algorithm is 5%
and its standard deviation is 32%. In the case of sea ice concentrations of 0% — 100%
the average deviation of the Liu algorithm is 4%
and its standard deviation is 32%. By comparison
the average deviation of the improved algorithm is -3%
and its standard deviation is 28%. In the case of sea ice concentrations of 0% —50%
the accuracy of the improved algorithm is better than that of the Liu algorithm. When the sea ice concentration is close to 100%
the results of the two algorithms are highly similar. Overall
the improvement effect of the proposed algorithm is related to the actual sea ice concentration
and the improved algorithm is more accurate than the Liu algorithm for low-concentration sea ice regions
such as ice-water transitions and broken ice coverage.
遥感海冰冰水识别MODIS最邻近像素海冰密集度算法
remote sensingsea iceice water recognitionMODISnearest pixelSIC algorithm
Aldenhoff W, Berg A and Eriksson L E B. 2016. Sea ice concentration estimation from Sentinel-1 synthetic aperture radar images over the Fram Strait//2016 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). Beijing, China: IEEE: 7675-7677 [DOI: 10.1109/IGARSS.2016.7731001http://dx.doi.org/10.1109/IGARSS.2016.7731001]
Baldwin D, Tschudi M, Pacifici F and Liu Y H. 2017. Validation of Suomi-NPP VIIRS sea ice concentration with very high-resolution satellite and airborne camera imagery. ISPRS Journal of Photogrammetry and Remote Sensing, 130: 122-138 [DOI: 10.1016/j.isprsjprs.2017.05.018http://dx.doi.org/10.1016/j.isprsjprs.2017.05.018]
Cavalieri D J, Markus T, Hall D K, Ivanoff A and Glick E. 2010. Assessment of AMSR-E Antarctic winter sea-ice concentrations using Aqua MODIS. IEEE Transactions on Geoscience and Remote Sensing, 48(9): 3331-3339 [DOI: 10.1109/tgrs.2010.2046495http://dx.doi.org/10.1109/tgrs.2010.2046495]
Drüe C and Heinemann G. 2004. High-resolution maps of the sea-ice concentration from MODIS satellite data. Geophysical Research Letters, 31(20): L20403 [DOI: 10.1029/2004GL020808http://dx.doi.org/10.1029/2004GL020808]
Guo Y Y and Jiao M L. 2010. Using MODIS data to retrieve distribution of sea ice in Bohai Sea. Journal of Huaihai Institute of Technology (Natural Science Edition), 19(1): 84-87
郭衍游, 焦明连, 2010. 利用MODIS数据反演渤海海冰分布. 淮海工学院学报(自然科学版), 19(1): 84-87 [DOI: 10.3969/j.issn.1672-6685.2010.01.022http://dx.doi.org/10.3969/j.issn.1672-6685.2010.01.022]
Hall D K, Riggs G A and Salomonson V V. 2001. Algorithm theoretical basis document (ATBD) for the MODIS snow and sea ice-mapping algorithms[EB/OL]. (2001-09-01). https://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd_mod10.pdfhttps://eospso.gsfc.nasa.gov/sites/default/files/atbd/atbd_mod10.pdf
Han S Q, Li Z F and Sun Z G. 2005. Observational study of MODIS data on sea ice of China’s Bohai Sea. Scientia Meteorologica Sinica, 25(6): 624-628
韩素芹, 黎贞发, 孙治贵. 2005. EOS/MODIS卫星对渤海海冰的观测研究. 气象科学, 25(6): 624-628 [DOI: 10.3969/j.issn.1009-0827.2005.06.009http://dx.doi.org/10.3969/j.issn.1009-0827.2005.06.009]
Knight E J and Kvaran G. 2014. Landsat-8 operational land imager design, characterization and performance. Remote Sensing, 6(11): 10286-10305 [DOI: 10.3390/rs61110286http://dx.doi.org/10.3390/rs61110286]
Laxon S W, Giles K A, Ridout A L, Wingham D J, Willatt R, Cullen R, Kwok R, Schweiger A, Zhang J L, Haas C, Hendricks S, Krishfield R, Kurtz N, Farrell S and Davidson M. 2013. CryoSat-2 estimates of Arctic sea ice thickness and volume. Geophysical Research Letters, 40(4): 732-737 [DOI: 10.1002/grl.50193http://dx.doi.org/10.1002/grl.50193]
Liu Y H, Key J and Mahoney R. 2016. Sea and freshwater ice concentration from VIIRS on Suomi NPP and the future JPSS satellites. Remote Sensing, 8(6): 523 [DOI: 10.3390/rs8060523http://dx.doi.org/10.3390/rs8060523]
Perovich D K. 1996. The Optical Properties of Sea Ice, CRREL Monogr. No. 96-1. U.S. Army Cold Regions Research and Engineering Laboratory: 25
Riggs G A and Hall D K. 2015. MODIS sea ice products user guide to collection 6[EB/OL]. (2015-03-17). https://landweb.modaps.eosdis.nasa.gov/QA_WWW/forPage/user_guide/MODISC6SeaIceproductsUserguide.pdfhttps://landweb.modaps.eosdis.nasa.gov/QA_WWW/forPage/user_guide/MODISC6SeaIceproductsUserguide.pdf
Riggs G A, Hall D K and Ackerman S A. 1999. Sea ice extent and classification mapping with the moderate resolution imaging spectroradiometer airborne simulator. Remote Sensing of Environment, 68(2): 152-163 [DOI: 10.1016/S0034-4257(98)00107-2http://dx.doi.org/10.1016/S0034-4257(98)00107-2]
Salomonson V V, Barnes W and Masuoka E J. 2006. Introduction to MODIS and an overview of associated activities//Qu J J, Gao W, Kafatos M, Murphy R E and Salomonson V V, eds. Earth Science Satellite Remote Sensing. Berlin, Heidelberg: Springer: 12-32 [DOI: 10.1007/978-3-540-37293-6_2http://dx.doi.org/10.1007/978-3-540-37293-6_2]
Su J, Hao G H, Ye X X and Wang W B. 2013. The experiment and validation of sea ice concentration AMSR-E retrieval algorithm in polar region. Journal of Remote Sensing, 17(3): 495-513
苏洁, 郝光华, 叶鑫欣, 王维波. 2013. 极区海冰密集度AMSR-E数据反演算法的试验与验证. 遥感学报, 17(3): 495-513 [DOI: 10.11834/jrs.20132043http://dx.doi.org/10.11834/jrs.20132043]
Wang X Y, Guan L and Li L L. 2018. Comparison and validation of sea ice concentration from FY-3B/MWRI and Aqua/AMSR-E observations. Journal of Remote Sensing, 22(5): 723-736
王晓雨, 管磊, 李乐乐. 2018. FY-3B/MWRI和Aqua/AMSR-E海冰密集度比较及印证. 遥感学报, 22(5): 723-736 [DOI: 10.11834/jrs.20187419http://dx.doi.org/10.11834/jrs.20187419]
Wu K Q, Xu Y and Hao Y M. 2005. Application in sea ice remote sensing of MODIS data. Marine Forecasts, 22(S1): 44-49
吴奎桥, 徐莹, 郝轶萌. 2005. MODIS数据在海冰遥感中的应用. 海洋预报, 22(S1): 44-49 [DOI: 10.3969/j.issn.1003-0239.2005.z1.007http://dx.doi.org/10.3969/j.issn.1003-0239.2005.z1.007]
Wu L T, Wu H D, Sun L T, Zhang Y F, Liu Y and Wei X Q. 2006. Retrieval of sea ice in the Bohai sea from MODIS data. Periodical of Ocean University of China, 36(2): 173-179
吴龙涛, 吴辉碇, 孙兰涛, 张蕴斐, 刘煜, 韦小琴. 2006. MODIS渤海海冰遥感资料反演. 中国海洋大学学报, 36(2): 173-179 [DOI: 10.3969/j.issn.1672-5174.2006.02.001http://dx.doi.org/10.3969/j.issn.1672-5174.2006.02.001]
Ye X X, Su J, Wang Y, Hao G H and Hou J Q. 2011. Assessment of AMSR-E sea ice concentration in ice margin zone using MODIS data//2011 International Conference on Remote Sensing, Environment and Transportation Engineering. Nanjing, China: IEEE: 3869-3873 [DOI: 10.1109/RSETE.2011.5965163http://dx.doi.org/10.1109/RSETE.2011.5965163]
Zhang N and Zhang Q H. 2014. Retrieval of the sea ice area from MODIS data by CART decision tree method. Marine Science Bulletin, 33(3): 321-327
张娜, 张庆河. 2014. 基于CART决策树方法的MODIS数据海冰反演. 海洋通报, 33(3): 321-327 [DOI: 10.11840/j.issn.1001-6392.2014.03.011http://dx.doi.org/10.11840/j.issn.1001-6392.2014.03.011]
Zhang S G, Zhao J P, Li M, Liu S X and Zhang S W. 2018. An improved dual-polarized ratio algorithm for sea ice concentration retrieval from passive microwave satellite data and inter-comparison with ASI, ABA and NT2. Journal of Oceanology and Limnology, 36(5): 1494-1508 [DOI: 10.1007/s00343-018-7077-xhttp://dx.doi.org/10.1007/s00343-018-7077-x]
Zhang X, Zhou C X, E D C and An J C. 2014. Monitoring of Antarctic sea ice based on the multichannel MODIS data. Geomatics and Information Science of Wuhan University, 39(10): 1194-1198
张辛, 周春霞, 鄂栋臣, 安家春. 2014. MODIS多波段数据对南极海冰变化的监测研究. 武汉大学学报·信息科学版, 39(10): 1194-1198 [DOI: 10.13203/j.whugis20130007http://dx.doi.org/10.13203/j.whugis20130007]
Zhao J C, Zhou X, Sun X Y, Cheng J J, Hu B and Li C H. 2017. The inter comparison and assessment of satellite sea-ice concentration datasets from the arctic. Journal of Remote Sensing, 21(3): 351-364
赵杰臣, 周翔, 孙晓宇, 程净净, 胡波, 李春花. 2017. 北极遥感海冰密集度数据的比较和评估. 遥感学报, 21(3): 351-364 [DOI: 10.11834/jrs.20176136http://dx.doi.org/10.11834/jrs.20176136]
Zhao X, Su H Y, Shi Z Y and Pang X P. 2015. Intercomparison of multi-sources sea ice concentration data in Antarctic. Geomatics and Information Science of Wuhan University, 40(11): 1460-1466
赵羲, 苏昊月, 石中玉, 庞小平. 2015. 南极海冰密集度多源数据的交叉检验. 武汉大学学报·信息科学版, 40(11): 1460-1466 [DOI: 10.13203/j.whugis20150250http://dx.doi.org/10.13203/j.whugis20150250]
Zou B, Lin M S, Shi L J, Zou Y R, Jia Y J and Zeng T. 2018. Application of remote sensing technology in ocean disaster. City and Disaster Reduction, 123(6): 61-65
邹斌, 林明森, 石立坚, 邹亚荣, 贾永君, 曾韬. 2018. 遥感技术在海洋灾害监测中的应用. 城市与减灾, 123(6): 61-65 [DOI: 10.3969/j.issn.1671-0495.2018.06.012http://dx.doi.org/10.3969/j.issn.1671-0495.2018.06.012]
相关文章
相关作者
相关机构
京公网安备11010802024621