结合Sentinel-1B和Landsat8数据的针叶林叶片含水量反演研究
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摘要
为研究SAR影像结合光学影像反演叶片含水量的可行性,本文以吉林省长春市净月潭国家森林公园为研究区,以Sentinel-1B、Landsat8 OLI遥感影像和通过外业调查获取的叶片含水量为数据源,通过相关性分析,选取出与叶片含水量相关性较大的波段组合和植被指数,并对其进行主成分提取,建立主成分与叶片含水量之间的线性、二次多项式、三次多项式和指数模型,并利用精度最高的模型反演出净月潭国家森林公园的叶片含水量。结果表明:(1) Sentinel-1B的VV极化、VH/VV极化比和OLI传感器的短波红外1波段、短波红外2波段、归一化水分指数(NDWI)、比值植被指数(RVI)与叶片含水量相关性较大;(2) Sentinel-1B和Landsat8 OLI数据结合相较于仅使用Landsat8 OLI数据、提取出的主成分与叶片含水量相关性较高;(3)利用提取出的主成分与叶片含水量建立的反演模型中三次多项式模型的拟合精度最高(R2=0.629 9,RMSE=0.035 8)。表明Sentinel-1B结合Landsat8 OLI数据能较好得反演出针叶林的叶片含水量。
In order to study the feasibility of inverting leaf water content from SAR images combined with optical images, the paper takes the moon lake national forest park in Changchun City, Jilin Province as the study area, and uses Sentinel-1 B and Landsat 8 OLI remote sensing images and the leaf water content obtained through field investigation as the data source. Through the correlation analysis, the band combinations and vegetation indices with large correlation with the leaf water content are selected and the principal components are extracted. The linear, quadratic polynomial, cubic polynomial and exponential models between the principal components and the leaf water content are established. Finally, the paper uses the model with the highest precision to reverse the leaf water content of the moon lake national forest park. The results show that: Sentinel-1 B VVpolarization, VH/VV polarization ratio and OLI sensor short wave infrared 1 band, short wave infrared 2 band, the normalized difference water index(NDWI), the ratio vegetation index(RVI) have large correlation with leaf water content. The principal components extracted from the combination of Sentinel-1 B and Landsat8 OLI data are more correlated with leaf water content than Landsat8 OLI data alone. The cubic polynomial model in the inversion model established using the extracted principal components and leaf water content has the highest fitting accuracy(R2=0.6299, RMSE=0.0358). It shows that Sentinel-1 B combined with Landsat8 OLI data can better reverse the leaf water content of coniferous forest.
In order to study the feasibility of inverting leaf water content from SAR images combined with optical images, the paper takes the moon lake national forest park in Changchun City, Jilin Province as the study area, and uses Sentinel-1 B and Landsat 8 OLI remote sensing images and the leaf water content obtained through field investigation as the data source. Through the correlation analysis, the band combinations and vegetation indices with large correlation with the leaf water content are selected and the principal components are extracted. The linear, quadratic polynomial, cubic polynomial and exponential models between the principal components and the leaf water content are established. Finally, the paper uses the model with the highest precision to reverse the leaf water content of the moon lake national forest park. The results show that: Sentinel-1 B VVpolarization, VH/VV polarization ratio and OLI sensor short wave infrared 1 band, short wave infrared 2 band, the normalized difference water index(NDWI), the ratio vegetation index(RVI) have large correlation with leaf water content. The principal components extracted from the combination of Sentinel-1 B and Landsat8 OLI data are more correlated with leaf water content than Landsat8 OLI data alone. The cubic polynomial model in the inversion model established using the extracted principal components and leaf water content has the highest fitting accuracy(R2=0.6299, RMSE=0.0358). It shows that Sentinel-1 B combined with Landsat8 OLI data can better reverse the leaf water content of coniferous forest.
引文
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[14]Wang D W, Wang J D. Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model[J].Science China Earth Sciences, 2010, 53(5):721-730.
[15]He L I, Jiang Z W, Chen Z X, et al. Assimilation of temporalspatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation[J]. Journal of Integrative Agriculture, 2017, 16(10):2283-2299.
[16]董磊,廖静娟,沈国状.基于神经网络算法的多极化雷达数据估算鄱阳湖生物量[J].遥感技术与应用, 2009,24(3):325-330.Dong L, Liao J J, Shen G Z. Estimation of Poyang lake biomass using multi-polarized radar data based on neural network algorithm[J]. Remote Sensing Technology and Application,2009, 24(3):325-330.
[17]牛晓丽.机载合成孔径雷达实时成像处理器若干信号处理技术的研究[D].合肥:中国科学技术大学, 2001.Niu X L. Research on several signal processing techniques for airborne synthetic aperture radar real-time imaging processor[D].Hefei:University of Science and Technology of China, 2001.
[18]李增元,车学俭,刘闽,等. ERS-1SAR影像森林应用研究初探[J].林业科学研究, 1994(6):692-696.Li Z Y, Che X J, Liu M, et al. Preliminary study on forest application of ERS-1 SAR imagery[J]. Forestry Science Research, 1994(6):692-696.
[19]张王菲,徐天蜀,姬永杰.高山松生物物理参数SAR信号响应研究[J].江苏农业科学, 2011, 39(5):464-466.Zhang W F, Xu T S, Ji Y J. Study on response of biophysical parameterSARsignaloftakayamapine[J].Jiangsu Agricultural Sciences, 2011, 39(5):464-466.
[20]沙莎,胡蝶,王丽娟,等.基于Landsat8 OLI数据的黄土高原植被含水量的估算模型研究[J].遥感技术与应用,2016, 31(3):558-563.Sha S, Hu D, Wang L J, et al. Estimation model of vegetation moisture in the Loess Plateau based on Landsat8 OLI data[J].Remote Sensing Technology and Application, 2016, 31(3):558-563.
[2]杜晓.植被叶面水遥感监测及其时空特征分析[D].北京:中国科学院研究生院遥感应用研究所, 2006.Du X. Monitoring vegetation foliar water remote sensing and its temporal and spatial characteristics[D]. Beijing:Graduate University of Chinese Academy of Sciences(Remote Sensing Application Research Institute), 2006.
[3]张佳华,许云,姚凤梅,等.植被含水量光学遥感估算方法研究进展[J].中国科学:技术科学, 2010,40(10):1121-1129.Zhang J H, Xu Y, Yao F M, et al. Research progress on optical remote sensing estimation method for vegetation moisture content[J].Science in China:Technical Science, 2010,40(10):1121-1129.
[4]张友水,谢元礼. MODIS影像的NDVI和LSWI植被水分含量估算[J].地理科学, 2008, 28(1):72-76.Zhang Y S, Xie Y L. Estimation of NDVI and LSWI vegetation moisture content in MODIS images[J]. Geographical Sciences,2008, 28(1):72-76.
[5]韩晓勇.基于MODIS和地物光谱的植被水分定量遥感研究[D].西安:长安大学, 2009.Han X Y. Quantitative remote sensing study of vegetation water based on MODIS and feature spectrum[D]. Xi’an:Chang’an University, 2009.
[6]张蓓.基于MODIS数据的植被水监测及其应用研究[D].北京:中国科学院研究生院(遥感应用研究所), 2004.Zhang B. Vegetation water monitoring based on MODIS data and its application[D]. Beijing:Graduate University of Chinese Academy of Sciences(Remote Sensing Application Institute), 2004.
[7]闻熠,黄春林,卢玲,等.基于ASTER数据黑河中游植被含水量反演研究[J].遥感技术与应用, 2015, 30(5):876-883.Wen Y, Huang C L, Lu L, et al. Research on vegetation water content retrieval in the middle Heihe river based on ASTER data[J]. Remote Sensing Technology and Application, 2015,30(5):876-883.
[8]李玉霞,杨武年,童玲,等.基于光谱指数法的植被含水量遥感定量监测及分析[J].光学学报, 2009, 29(5):1403-1407.Li Y X, Yang W N, Tong L, et al. Remote sensing quantitative monitoring and analysis of vegetation water content based on spectral index method[J]. Journal of Optics, 2009, 29(5):1403-1407.
[9]白晓静.基于多波段多极化SAR数据的草原地表土壤水分反演方法研究[D].成都:电子科技大学, 2017.Bai X J. Research on the method of inversion of soil moisture in grassland based on multi-band multi-polarization SAR data[D].Chengdu:Electronic Science and Technology University, 2017.
[10]余凡,赵英时. ASAR和TM数据协同反演植被覆盖地表土壤水分的新方法[J].中国科学:地球科学, 2011,41(4):532-540.Yu F, Zhao Y S. A new method for retrieving soil moisture from vegetation coverage using ASAR and TM data[J]. Chinese Science of Earth Sciences, 2011,41(4):532-540.
[11]高帅,牛铮.热带人工林SAR散射组成及对遥感估测叶面积指数的影响[J].地球科学进展, 2008, 23(9):982-989.Gao S, Niu Z. Effects of SAR scattering on tropical plantation and estimation of leaf area index by remote sensing[J]. Advances in Earth Science, 2008, 23(9):982-989.
[12]高帅,牛铮,邬明权.基于ENVISAT/ASAR的神经网络反演人工林叶面积指数研究[J].遥感技术与应用, 2013,28(2):205-211.Gao S, Niu Z, Wu M Q. Retrieval of artificial forest leaf area index based on ENVISAT/ASAR neural network[J]. Remote Sensing Technology and Application, 2013, 28(2):205-211.
[13]肖婷. ENVISAT ASAR在入侵植物叶面积指数反演中的应用研究[D].上海:上海师范大学, 2010.Xiao T. Application study of ENVISAT ASAR in retrieval of leaf area index of invasive plants[D]. Shanghai:Shanghai Normal University, 2010.
[14]Wang D W, Wang J D. Retrieving crop leaf area index by assimilation of MODIS data into a crop growth model[J].Science China Earth Sciences, 2010, 53(5):721-730.
[15]He L I, Jiang Z W, Chen Z X, et al. Assimilation of temporalspatial leaf area index into the CERES-Wheat model with ensemble Kalman filter and uncertainty assessment for improving winter wheat yield estimation[J]. Journal of Integrative Agriculture, 2017, 16(10):2283-2299.
[16]董磊,廖静娟,沈国状.基于神经网络算法的多极化雷达数据估算鄱阳湖生物量[J].遥感技术与应用, 2009,24(3):325-330.Dong L, Liao J J, Shen G Z. Estimation of Poyang lake biomass using multi-polarized radar data based on neural network algorithm[J]. Remote Sensing Technology and Application,2009, 24(3):325-330.
[17]牛晓丽.机载合成孔径雷达实时成像处理器若干信号处理技术的研究[D].合肥:中国科学技术大学, 2001.Niu X L. Research on several signal processing techniques for airborne synthetic aperture radar real-time imaging processor[D].Hefei:University of Science and Technology of China, 2001.
[18]李增元,车学俭,刘闽,等. ERS-1SAR影像森林应用研究初探[J].林业科学研究, 1994(6):692-696.Li Z Y, Che X J, Liu M, et al. Preliminary study on forest application of ERS-1 SAR imagery[J]. Forestry Science Research, 1994(6):692-696.
[19]张王菲,徐天蜀,姬永杰.高山松生物物理参数SAR信号响应研究[J].江苏农业科学, 2011, 39(5):464-466.Zhang W F, Xu T S, Ji Y J. Study on response of biophysical parameterSARsignaloftakayamapine[J].Jiangsu Agricultural Sciences, 2011, 39(5):464-466.
[20]沙莎,胡蝶,王丽娟,等.基于Landsat8 OLI数据的黄土高原植被含水量的估算模型研究[J].遥感技术与应用,2016, 31(3):558-563.Sha S, Hu D, Wang L J, et al. Estimation model of vegetation moisture in the Loess Plateau based on Landsat8 OLI data[J].Remote Sensing Technology and Application, 2016, 31(3):558-563.