基于高分3号和高分2号叶面积指数反演与分析
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摘要
针对光学数据反演叶面积指数(LAI)容易受到云雾遮挡和光学遥感信息饱和的问题,根据雷达散射机制和Yamaguchi分解,提出了极化分解植被指数,利用光学植被指数和极分解植被指数融合形成光学与微波极化分解融合植被指数;利用光学与微波极化分解融合植被指数与实测数据建立回归模型反演叶面积指数,并对该模型精度评价.实验表明:光学与微波极化分解融合植被指数与实测数据建立回归模型反演叶面积指数的精度要优于极化分解植被指数和光学植被指数与实测数据建立的回归模型,其中MRVI与LAI建立回归模型是最优的,R~2达到0.7262,RMSE到达0.3548.综上所述,光学与微波极化分解融合植被指数不仅充分利用雷达能够穿透浓密植物的特性,而且融合光学数据对叶面积指数的反演敏感性,更能准确的反演叶面积指数.
The inversion of leaf area index(LAI) for optical data is susceptible to cloud occlusion and saturation of optical remote sensing information. Based on the radar scattering mechanism and Yamaguchi decomposition, this paper proposes a polarization decomposition vegetation index, and uses the optical vegetation index and the polar decomposition vegetation index to form an optical and microwave polarization decomposition fusion vegetation index, which, combined with the measured data, is used to establish a regression model so as to invert the leaf area index, and evaluate the accuracy of the model. Experiments show that the optical and microwave polarization decomposition fusion vegetation index and measured data establish a regression model that inverts the accuracy of leaf area index better than does the regression model established by polarization decomposition vegetation index and optical vegetation index and measured data, among which the regression model formed by MRVI and LAI is optimal, with R~2 reaching 0.726 2 and RMSE reaching 0.354 8. In summary,the optical and microwave polarization decomposition fusion vegetation index are able to fully utilize the characteristics of radar capable of penetrating dense plants; the inversion sensitivity of the fusion optical data to the leaf area index is capable of inverting the leaf area index more accurately.
The inversion of leaf area index(LAI) for optical data is susceptible to cloud occlusion and saturation of optical remote sensing information. Based on the radar scattering mechanism and Yamaguchi decomposition, this paper proposes a polarization decomposition vegetation index, and uses the optical vegetation index and the polar decomposition vegetation index to form an optical and microwave polarization decomposition fusion vegetation index, which, combined with the measured data, is used to establish a regression model so as to invert the leaf area index, and evaluate the accuracy of the model. Experiments show that the optical and microwave polarization decomposition fusion vegetation index and measured data establish a regression model that inverts the accuracy of leaf area index better than does the regression model established by polarization decomposition vegetation index and optical vegetation index and measured data, among which the regression model formed by MRVI and LAI is optimal, with R~2 reaching 0.726 2 and RMSE reaching 0.354 8. In summary,the optical and microwave polarization decomposition fusion vegetation index are able to fully utilize the characteristics of radar capable of penetrating dense plants; the inversion sensitivity of the fusion optical data to the leaf area index is capable of inverting the leaf area index more accurately.
引文
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[19]许涛,廖静娟,沈国状,等.基于高分一号与Radarsat-2的鄱阳湖湿地植被叶面积指数反演[J].红外与毫米波学报,2016,35(3):332-340.Xu T,Liao J J,Sheng G Z,et al.Estimation of wetland vegetation LAI in the Poyang Lake area using GF-1 and Radarsat-2 data[J].Journal of Infrared and Millimeter Waves,2016,35(3):332-340.
[20]Dany?ec K,Mazur J,Kozak K,et al.Determination of the thoron emanation coefficient using a powder sandwich technique[J].Journal of Environmental Radioactivity,2018,195:109-113.DOI:10.1016/j.jenvrad.2018.10.004.
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[22]史磊,李平湘,杨杰.极化方位角对Yamaguchi参数分解的影响[J].中国图象图形学报,2011,16(11):1 989-1 995.DOI:10.11834/jig.20111105.Shi L,Li P X,Yang J.Effect of polarization azimuth on the decomposition of Yamaguchi parameters[J].Chinese Journal of Image Graphics,2011,16(11):1 989-1 995.
[2]Arndt P,Maren D,Christiane W,et al.Influence of woody tissue and leaf clumping on vertically resolved leaf area index an dangular gap probability estimates[J].Forest Ecology and Management,2015,340:103-113.DOI:10.1016/j.foreco.2014.12.026.
[3]Soudani K,Fran?ois C,Maire G L,et al.Comparative analysis of IKONOS,SPOT,and ETM+data for leaf area index estimation in temperate coniferous and deciduous forest stands[J].Remote Sensing of Environment,2006,102(1):161-175.
[4]Liu J,Pattey E,Jégo G.Assessment of vegetation indices for regional crop green LAI estimation from Landsat images over multiple growing seasons[J].Remote Sensing of Environment,2012,123(3):347-358.
[5]于泉洲,周蕾,王绍强,等.基于EO-1 Hyperion的中国典型森林冠层高光谱特征分析[J].云南大学学报:自然科学版,2018,40(5):947-954.Yu Q Z,Zhou L,Wang S Q,et al.An analysis on the spectrum characteristics of Chinese typical forest canopy in growing season based on EO-1 Hyperion images[J].Journal of Yunnan University:Natural Sciences Edition,2018,40(5):947-954.
[6]靳华安,李爱农,边金虎,等.西南地区不同山地环境梯度叶面积指数遥感反演[J].遥感技术与应用,2016,31(1):42-50.Jin H A,Li A M,Bian J H,et al.Remote sensing inversion of gradient leaf area index in different mountain environments in the Southwest[J].Remote Sensing Technology and Applications,2016,31(1):42-50.
[7]高帅,牛铮,刘晨洲.基于RADARSAT SAR估测热带人工林叶面积指数研究[J].国土资源遥感,2008,20(4):35-38.Gao S,Niu Z,Liu C Z.The estimation of tropical plantation forest leaf area index based on RADARSAT SARdata[J].Remote Sensing of Territorial Resources,2008,20(4):35-38.
[8]马红章,刘素美,朱晓波,等.基于被动微波遥感技术的玉米冠层叶面积指数反演[J].国土资源遥感,2013,25(3):66-71.Ma H Z,Liu S M,Zhu X B,et al.Inversion of corn crown leaf area index based on passive microwave remote sensing technology[J].Remote Sensing of Territorial Resources,2013,25(3):66-71.
[9]林岳峰,柳钦火,李静,等.最小二乘法联合光学与雷达遥感数据估算玉米叶面积指数[J].遥感技术与应用,2016,31(4):691-701.Lin Y F,Liu Q H,Li J,et al.Least square method combined with optical and radar remote sensing data estimates corn leaf area index[J].Remote Sensing Technology and Applications,2016,31(4):691-701.
[10]苏伟,侯宁,李琪,等.基于Sentinel-2遥感影像的玉米冠层叶面积指数反演[J].农业机械学报,2018,49(1):151-156.Su W,Hou N,Li Q,et al.Inversion of corn canopy leaf area index based on Sentiel-2 remote sensing images[J].Journal of Agricultural Machinery,2018,49(1):151-156.
[11]Nariane B,Fernanda W,Thanan R,et al.Atmospheric correction issues for retrieving total suspended matter concentrations in inland waters using OLI/Landsat-8image[J].Advances in Space Research,2017,59(9):2 335-2 348.DOI:10.1016/j.asr.2017.02.017.
[12]王海,杨祖祥,王麟,等.TVDI在云南2009/2010年干旱监测中的应用[J].云南大学学报:自然科学版,2014,36(1):59-65.Wang H,Yang Z X,Wang L,et al.The application of TVDI in drought monitoring over Yunnan Province during 2009 to 2010[J].Journal of Yunnan University:Natural Sciences Edition,2014,36(1):59-65.
[13]彭泽瑜,彭明春,王崇云,等.金沙江流域(云南部分)植被光谱特征季节变化研究[J].云南大学学报:自然科学版,2013,35(S2):373-377.Peng Z Y,Peng M C,Wang C Y,et al.Study on seasonal variation of vegetation spectral characteristics at Jin Shajiang River Basin(Yunnan part)[J].Journal of Yunnan University:Natural Science Edition,2013,35(S2):373-377.
[14]Huete A R.A soil-adjusted vegetation index(SAVI)[J].Remote Sensing of Environment,1988,25(3):295-309.DOI:10.1016/0034-4257(88)90106-X.
[15]Liu H Q,Huete A R.A feedback based modification of the NDVI to minimize canopy background and atmospheric noise[J].IEEE Transactions on Geoscience and Remote Sensing,1995,33(2):457-465.DOI:10.1109/36.377946.
[16]Chen J M,Pavlic G,Brown L,et al.Derivation and validation of Canada-wide coarse-resolution leaf area index maps using hig resolution satellite imagery and ground measurements[J].Remote Sensing of Environment,2002,80(1):165-184.DOI:10.1016/S0034-4257(01)00300-5.
[17]Li H,Chen Z X,Jiang Z W,et al.Comparative analysis of GF-1,HJ-1,and Landsat-8 data for estimating the leaf area index of winter wheat[J].Journal of Integrative Agriculture,2017,16(2):266-285.DOI:10.1016/S2095-3119(15)61293-X.
[18]JoséP S V,Paulo B.Post-fire vegetation regrowth detection in the Deiva Marina region(Liguria-Italy)using Landsat TM and ETM+data[J].Ecological Modelling,2009,221(1):75-84.DOI:10.1016/j.ecolmodel.2009.03.011.
[19]许涛,廖静娟,沈国状,等.基于高分一号与Radarsat-2的鄱阳湖湿地植被叶面积指数反演[J].红外与毫米波学报,2016,35(3):332-340.Xu T,Liao J J,Sheng G Z,et al.Estimation of wetland vegetation LAI in the Poyang Lake area using GF-1 and Radarsat-2 data[J].Journal of Infrared and Millimeter Waves,2016,35(3):332-340.
[20]Dany?ec K,Mazur J,Kozak K,et al.Determination of the thoron emanation coefficient using a powder sandwich technique[J].Journal of Environmental Radioactivity,2018,195:109-113.DOI:10.1016/j.jenvrad.2018.10.004.
[21]Afan G S,Yaya H,Edi A,et al.Single layer&multi-layer Long Short-Term Memory(LSTM)model with intermediate variables for weather forecasting[J].Procedia Computer Science,2018,135:89-98.DOI:10.1016/j.procs.2018.08.153.
[22]史磊,李平湘,杨杰.极化方位角对Yamaguchi参数分解的影响[J].中国图象图形学报,2011,16(11):1 989-1 995.DOI:10.11834/jig.20111105.Shi L,Li P X,Yang J.Effect of polarization azimuth on the decomposition of Yamaguchi parameters[J].Chinese Journal of Image Graphics,2011,16(11):1 989-1 995.
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