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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (3) : 128-135     DOI: 10.6046/gtzyyg.2018.03.18
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Processing analysis of Sentinel-2A data and application to arid valleys extraction
Bin YANG, Dan LI, Guisheng GAO, Cai CHEN, Lei WANG
College of Environment and Resource, Southwest University of Science and Technology, Mianyang 621010, China
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Abstract  

As a new optical remote sensing satellite, Sentinel-2A has become a hot spot in optical remote sensing applications because it has wide bandwidth, multi-spectrum, high spatial-temporal resolution and free sharing. In this study, we chose Heishui River basin as the study area and selected Sentinel-2A satellite data from European Space Agency. The authors obtained aerosol optical data, water vapor data, scene classification data and biomass factor data through analysis of data arguments, organization form, product grade and data format by using the sen2cor processing module of SNAP. The distribution areas of arid valley in the study area were extracted by using the vegetation ecological index data and digital elevation model, combined with the expert decision classification method with the analyses of biophysical index data. The result shows that Sentinel-2A satellite data have good quality in that they enrich the application field of remote sensing technology greatly. L2A level data have more positive application value for the monitoring and evaluation of global ecological vegetation environment change.
Keywords Sentinel-2A      vegetation biophysical index      atmospheric correction      dry valleys      European Space Agency     
:  TP79  
Issue Date: 10 September 2018
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Bin YANG
Dan LI
Guisheng GAO
Cai CHEN
Lei WANG
Cite this article:   
Bin YANG,Dan LI,Guisheng GAO, et al. Processing analysis of Sentinel-2A data and application to arid valleys extraction[J]. Remote Sensing for Land & Resources, 2018, 30(3): 128-135.
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https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.03.18     OR     https://www.gtzyyg.com/EN/Y2018/V30/I3/128
Landsat8(OLI传感器) Sentinel-2A (MSI传感器)
波段
号		 波段
名称		 波长范围/μm 空间分
辨率/m		 辐射分
辨率/bit		 时间分
辨率/d		 幅宽/
km		 波段
号		 波段
名称		 波长范围/μm 空间分
辨率/m		 辐射分
辨率/bit		 时间分
辨率/d		 幅宽/
km
1 深蓝 0.433~0.453 30 1 深蓝 0.430~0.457 60
2 蓝 0.450~0.515 30 2 蓝 0.440~0.538 10
3 绿 0.525~0.600 30 3 绿 0.537~0.582 10
4 红 0.630~0.680 4 红 0.646~0.684 10
8 全色 0.500~0.680 15 5 红边1 0.694~0.713 20
5 近红外 0.845~0.885 30 6 红边2 0.731~0.749 20
9 卷云 1.360~1.390 30 12 16 185 7 红边3 0.769~0.797 20 12 10 290
6 短波红外 1.560~1.660 30 8 近红外 0.760~0.908 10
7 短波红外 2.100~2.300 30 8a 窄近红外 0.848~0.881 20
9 水汽波段 0.932~0.958 60
10 卷云 1.337~1.412 60
11 短波红外 1.539~1.682 20
12 短波红外 2.078~2.320 20
Tab.1  Comparison of spectral bands data between two satellites
Fig.1  Band distribution of Sentinel-2A satellite
数据类型 说明
影像数据文件 利用JPEG2000影像压缩算法存储一系列瓦片数据,其中每个瓦片的单位面积为100 km2,瓦片数据经过了正射校正,瓦片数据中每个波段储存为单独的JPEG2000数据文件
影像数据辅助文件 影像数据的辐射特性、几何特性、影像内容特性及与影像内容有关的检测信息等(L1C级别基于L1B级别数据生成)
影像质量指标文件 主要包括正射校正参考指标和大气环境相关指标,其中正射校正参考指标包括地表影像处理参数(ground image processing parameter,GIPP)、数字高程模型(digital elevation model,DEM)和 地面参考信息(ground referenced information,GRI)数据,大气环境相关指标包括欧洲中期天气预报中心参数(臭氧层含量、水汽含量和地中海平面大气压力)
元数据信息 产品基础介绍及瓦片组合数据(数据获取时间、太阳高度角和方位角等,基于L1B级别数据生成)
Tab.2  Data illustrate of L1C basic unit
Fig.2  Distribution of Sentinel-2A data in the research area
Fig.3  L2A data and correlative information in the research area
波段 最小值 最大值 平均值 标准差 第一主成分/%
B2 3 17 412 673.47 1 051.01 11.82
B3 5 16 179 817.98 964.32 10.84
B4 3 15 218 749.28 943.74 10.61
B5 0 15 388 1 084.49 897.18 10.09
B6 10 14 702 1 782.19 893.99 10.05
B7 38 14 141 1 952.12 895.17 10.06
B8 58 15 113 2 081.41 937.41 10.54
B8a 64 13 601 2 117.36 877.70 9.87
B11 65 8 674 1 771.34 761.93 8.57
B12 40 9 620 1 184.13 672.41 7.56
Tab.3  Each band basic statistics of the L2A data in the research area
Fig.4  Spectral characteristics analysis of typical object
Fig.5  Distribution of vegetable-biophysical index data in the research area
生物量 最小值 最大值 均值 标准差
LAI -0.521 1 15.231 0 1.051 8 0.457 6
FAPAR -1.615 3 0.910 7 0.321 2 0.190 1
CAB -75.971 8 328.030 1 43.433 4 22.758 7
CWC -0.360 8 0.447 2 0.022 9 0.032 7
FVC 0.000 0 0.951 1 0.303 4 0.155 1
NDVI -0.888 0 0.986 0 0.536 7 0.217 4
Tab.4  Analysis of image feature based on every vegetable-biophysical data
Fig.6  Flowchart of decision tree classification
Fig.7  Distribution of dry river valleys
[1] Suhet. Sentinel-2 User Handbook[M]. [s.l.]:European Space Agency, 2013: 1.
[2] Drusch M, Del Bello U, Carlier S , et al. Sentinel-2:ESA’s optical high-resolution mission for GMES operational services[J]. Remote Sensing of Environment, 2012,120:25-36.
doi: 10.1016/j.rse.2011.11.026 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425712000636
[3] Malenovský Z, Rott H, Cihlar J , et al. Sentinels for science:Potential of Sentinel-1,-2,and -3 missions for scientific observations of ocean,cryosphere,and land[J]. Remote Sensing of Environment, 2012,120:91-101.
doi: 10.1016/j.rse.2011.09.026 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425712000648
[4] Laurent V C E, Schaepman M E, Verhoef W , et al. Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image[J]. Remote Sensing of Environment, 2014,140:318-329.
doi: 10.1016/j.rse.2013.09.005 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425713003088
[5] Hagolle O, Huc M, Pascual D V , et al. A multi-temporal method for cloud detection,applied to FORMOSAT-2,VENμS,LANDSAT and SENTINEL-2 images[J]. Remote Sensing of Environment, 2010,114(8):1747-1755.
doi: 10.1016/j.rse.2010.03.002 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425710000908
[6] Hill M J . Vegetation index suites as indicators of vegetation state in grassland and savanna:An analysis with simulated SENTINEL 2 data for a North American transect[J]. Remote Sensing of Environment, 2013,137:94-111.
doi: 10.1016/j.rse.2013.06.004 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425713001971
[7] Verrelst J, Rivera J P, Veroustraete F , et al. Experimental Sentinel-2 LAI estimation using parametric,non-parametric and physical retrieval methods-A comparison[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2015,108:260-272.
doi: 10.1016/j.isprsjprs.2015.04.013 url: http://linkinghub.elsevier.com/retrieve/pii/S0924271615001239
[8] Fernández-Manso A, Fernández-Manso O, Quintano C . Sentinel-2A red-edge spectral indices suitability for discriminating burn severity[J]. International Journal of Applied Earth Observation and Geoinformation, 2016,50:170-175.
doi: 10.1016/j.jag.2016.03.005 url: http://linkinghub.elsevier.com/retrieve/pii/S0303243416300368
[9] Verrelst J, Rivera J P, Moreno J , et al. Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval[J]. ISPRS Journal of Photogrammetry and Remote Sensing, 2013,86:157-167.
doi: 10.1016/j.isprsjprs.2013.09.012 url: http://linkinghub.elsevier.com/retrieve/pii/S092427161300213X
[10] Zurita-Milla R, Laurent V C E,Van Gijsel J A E .Visualizing the ill-posedness of the inversion of a canopy radiative transfer model:A case study for Sentinel-2[J]. International Journal of Applied Earth Observation and Geoinformation, 2015,43:7-18.
doi: 10.1016/j.jag.2015.02.003 url: http://linkinghub.elsevier.com/retrieve/pii/S0303243415000355
[11] Herrmann I, Pimstein A, Karnieli A , et al. LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands[J]. Remote Sensing of Environment, 2011,115(8):2141-2151.
doi: 10.1016/j.rse.2011.04.018 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425711001465
[12] Verrelst J, Muñoz J, Alonso L , et al. Machine learning regression algorithms for biophysical parameter retrieval:Opportunities for Sentinel-2 and -3[J]. Remote Sensing of Environment, 2012,118:127-139.
doi: 10.1016/j.rse.2011.11.002 url: http://linkinghub.elsevier.com/retrieve/pii/S003442571100397X
[13] Van Der Meer F D, Van Der Werff H M A, Van Ruitenbeek F J A . Potential of ESA’s Sentinel-2 for geological applications[J]. Remote Sensing of Environment, 2014,148:124-133.
doi: 10.1016/j.rse.2014.03.022 url: http://linkinghub.elsevier.com/retrieve/pii/S0034425714001084
[14] Cleversa J G P W, Gitelson A A . Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3[J]. International Journal of Applied Earth Observation and Geoinformation, 2013,23:344-351.
doi: 10.1016/j.jag.2012.10.008 url: http://linkinghub.elsevier.com/retrieve/pii/S0303243412002097
[15] Atzberger C, Richter K . Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery[J]. Remote Sensing of Environment, 2012,120:208-218.
doi: 10.1016/j.rse.2011.10.035 url: http://linkinghub.elsevier.com/retrieve/pii/S003442571200079X
[16] Novelli A, Aguilar M A, Nemmaoui A , et al. Performance evaluation of object based greenhouse detection from Sentinel-2 MSI and Landsat 8 OLI data:A case study from Almería (Spain)[J]. International Journal of Applied Earth Observation and Geoinformation, 2016,52:403-411.
doi: 10.1016/j.jag.2016.07.011 url: https://linkinghub.elsevier.com/retrieve/pii/S0303243416301180
[17] ESA. Sentinel-2:ESA’s Optical High-Resolution Mission for GMES Operational Services[M]. Leiden,the Netherlands:European Space Agency, 2012: 3.
[18] Müller-Wilm U. Sen2Cor 2.2.1 - Software Release Note[M]. [s.l.]:European Space Agency, 2016.
[19] Louis J . Sentinel 2 MSI-Level 2A Product Definition[M]. European Space Agency, 2016: 4.
[20] Louis J . Sentinel 2 MSI-Level 2A Product Format Specifications Technical Note[R].S2PAD-VEGA-TN-PFS-0001,Darmstadt, Germany:European Space Agency, 2016: 4.
[21] Müller-Wilm U . Sentinel-2 MSI-Level-2A Prototype Processor Installation and User Manual[R].S2PAD-VEGA-SUM-0001,Darmstadt, Germany:European Space Agency, 2016: 4.
[22] Müller-Wilm U . Sentinel-2 MSI-Level-2A Prototype Processor Installation and User Manual[R].S2PAD-VEGA-SUM-0001,Darmstadt, Germany:European Space Agency, 2016: 4.
[23] 韩忻忆, 颉耀文 . 基于决策树的干旱区湿地信息自动提取——以疏勒河流域为例[J]. 遥感技术与应用, 2015,30(6):1146-1152.
doi: 10.11873/j.issn.1004-0323.2015.6.1146
[23] Han X Y, Xie Y W . The automatic extraction of wetland information in arid zone based on decision tree algorithm:A case study in the Shule River basin[J]. Remote Sensing Technology and Application, 2015,30(6):1146-1152.
[24] 娄雪婷, 曾源, 吴炳方 . 森林地上生物量遥感估测研究进展[J]. 国土资源遥感, 2011,23(1):1-8.doi: 10.6046/gtzyyg.2011.01.01.
doi: 10.6046/gtzyyg.2011.01.01 url: 年度引用
[24] Lou X T, Zeng Y, Wu B F . Advances in the estimation of above-ground biomass of forest using remote sensing[J]. Remote Sensing for Land and Resources, 2011,23(1):1-8.doi: 10.6046/gtzyyg.2011.01.01.
[25] 杨斌, 詹金凤, 李茂娇 . 岷江上游流域环境脆弱性评价[J]. 国土资源遥感, 2014,26(4):138-144.doi: 10.6046/gtzyyg.2014.04.22.
doi: 10.6046/gtzyyg.2014.04.22
[25] Yang B, Zhan J F, Li M J . Evaluation of environmental vulnerability in the upper reaches of the Minjiang River[J]. Remote Sensing for Land and Resources, 2014,26(4):138-144.doi: 10.6046/gtzyyg.2014.04.22.
[26] 包维楷, 庞学勇, 李芳兰 , 等. 干旱河谷生态恢复与持续管理的科学基础[M]. 北京: 科学出版社, 2012.
[26] Bao W K, Pang X Y, Li F L , et al. The Scientific Basis for Ecological Restoration and Sustainable Management in Arid Valley[M]. Beijing: Science Press, 2012.
[27] 杨兆平, 常禹, 杨孟 , 等. 岷江上游干旱河谷景观边界动态及其影响域[J]. 应用生态学报, 2007,18(9):1972-1976.
doi:
[27] Yang Z P, Chang Y, Yang M , et al. Dynamics and influence width of dry valley landscape boundary in upper reaches of Minjiang River[J]. Chinese Journal of Applied Ecology, 2007,18(9):1972-1976.
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