离散Landsat归一化植被指数的时间序列融合构建

doi:10.14088/j.cnki.issn0439-8114.2019.12.012

湖北农业科学 ›› 2019, Vol. 58 ›› Issue (12): 45-51.doi: 10.14088/j.cnki.issn0439-8114.2019.12.012

离散Landsat归一化植被指数的时间序列融合构建

曾德裕, 卢晓宁, 黄玥, 杨柳青, 孟成真

成都信息工程大学资源环境学院,成都 610225

收稿日期:2018-09-11 出版日期:2019-06-25 发布日期:2019-12-05
作者简介:曾德裕（1994-）,男,湖南临湘人,在读硕士研究生,研究方向为资源环境遥感,（电话）18708121831（电子信箱）673526122@qq.com。
基金资助:
四川省教育厅重点（自然科学）项目（17ZA0075）; 成都市科技局科技惠民技术研发项目（2016-HM01-00392-SF）; 国家自然科学基金项目（41401103; 41471305; 41505012）; 四川省科技厅重点项目（2017SZ0169）; 四川省科技厅软科学项目（2016ZR0102）; 四川省旱涝与重点实验室2018重点项目

Time series fusion construction of discrete Landsat normalized vegetation index

ZENG De-yu, LU Xiao-ning, HUANG Yue, YANG Liu-qing, MENG Cheng-zhen

College of Resources and Environment,Chengdu University of Information Technology,Chengdu 610225,China

Received:2018-09-11 Online:2019-06-25 Published:2019-12-05

摘要/Abstract

摘要： 结合滑动窗的使用,提出了一种基于傅里叶逼近和线性融合降尺度的时间序列构建方法。在时间序列上逐像元对离散的Landsat数据进行傅里叶逼近,并同分辨率较低的AVHRR传感器数据进行融合,实现了试验区Landsat植被指数的时间序列构建,结合作为实际观测数据的Landsat数据对模型结果的精度进行验证。结果表明,模型结果在色调与纹理等图像特征方面与实际观测数据一致,构建了白色冰雪覆盖区域的NDVI,构建结果与周边地表具有非常好的空间连续性,且整幅影像不存在明显的块状效应;模型结果与实际观测数据具有较高的相关性（平均0.869 2）和较低的均方根误差（平均0.043 5）,平均高达75.04%的像元误差在±0.05之间,高达97.64%的像元误差在±0.1之间,并且误差呈良好的正态分布。模型精度对输入数据量存在一定依赖,同时对混合像元的处理方面尚存在一定的改进空间。

关键词: Landsat, NDVI, 数据融合, 傅里叶分析

Abstract: In combination with the use of sliding windows, a time series construction method based on Fourier approximation and linear fusion downscaling is proposed. The Fourier approximation of discrete Landsat data is performed on a time-by-pixel basis and merged with the lower resolution AVHRR sensor data to realize the time series construction of the Landsat vegetation index in experimental area, combined with Landsat as the actual observation data. The data verifies the accuracy of the model results. The results show thatThe model results in the hue and texture, image features are consistent with the actual observation data, and the NDVI of the white snow-covered area is constructed. The construction results have very good spatial continuity with the surrounding surface, and there is no obvious blocky effect in the entire image; Model results and actual observed data has a high correlation （mean 0.869 2） and a low root mean square error （average of 0.043 5）, averaging up to 75.04% of the pixels between ±0.05, up to 97.64% of the pixel error between ±0.1, and the error is in a good normal distribution. The accuracy of the model has a certain dependence on the amount of input data, and there is still room for improvement in the processing of mixed pixels.

Key words: Landsat, NDVI, data fusion, Fourier analysis

中图分类号:

TP79

曾德裕, 卢晓宁, 黄玥, 杨柳青, 孟成真. 离散Landsat归一化植被指数的时间序列融合构建[J]. 湖北农业科学, 2019, 58(12): 45-51.

ZENG De-yu, LU Xiao-ning, HUANG Yue, YANG Liu-qing, MENG Cheng-zhen. Time series fusion construction of discrete Landsat normalized vegetation index[J]. HUBEI AGRICULTURAL SCIENCES, 2019, 58(12): 45-51.

参考文献

[1] BARET F,GUYOT G,MAJOR D J.TSAVI:A vegetation index which minimizes soil brightness effects on LAI and APAR estimation[A].Geoscience and Remote Sensing Symposium,1989.IGARSS'89.12th Canadian symposium on remote sensing[C].IEEE,1989.1355-1358.
[2] 史军,孙国武,崔林丽.中国东部NDVI时空变化及对东亚夏季风响应[J].高原气象,2008(S1):170-174.
[3] 邹文涛,吴炳方,张淼,等.农作物长势综合监测——以印度为例[J].遥感学报,2015,19(4):539-549.
[4] 刘佳,王利民,杨福刚,等.基于HJ时间序列数据的农作物种植面积估算[J].农业工程学报,2015,31(3):199-206.
[5] 刘焕军,孟令华,张新乐,等.基于时间序列Landsat影像的棉花估产模型[J].农业工程学报,2015,31(17):215-220.
[6] 李鑫川,徐新刚,王纪华,等.基于时间序列环境卫星影像的作物分类识别[J].农业工程学报,2013,29(2):169-176.
[7] 李登科,郭铌.基于植被生长规律的陕西省植被遥感分类[J].高原气象,2008,27(1):215-221.
[8] 李儒,张霞,刘波,等.遥感时间序列数据滤波重建算法发展综述[J].遥感学报,2009,13(2):335-341.
[9] GAO F,MASEK J,SCHWALLER M,et al.On the blending of the Landsat and MODIS surface reflectance:Predicting daily Landsat surface reflectance[J].IEEE transactions on geoscience & remote sensing,2006,44(8):2207-2218.
[10] ZHU X,CHEN J,GAO F,et al.An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions[J].Remote sensing of environment,2010,114:2610-2623.
[11] HWANG T,SONG C,BOLSTAD P V,et al.Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain[J].Remote sensing of environment,2011,115:2499-2512.
[12] ZHU Z,WOODCOCK C E,HOLDEN C,et al.Generating synthetic Landsat images based on all available Landsat data:Predicting Landsat surface reflectance at any given time[J].Remote sensing of environment,2015,162:67-83.
[13] BINDHU V M,NARASIMHAN B.Development of a spatio-temporal disaggregation method (DisNDVI) for generating a time series of fine resolution NDVI images[J].ISPRS journal of photogrammetry and remote sensing,2015,101:57-68.
[14] GEVAERT C M,GARCÍA-HARO F J. A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion[J].Remote sensing of environment,2015,156:34-44.
[15] 谢登峰,张锦水,孙佩军,等.结合像元分解和 STARFM模型的遥感数据融合[J].遥感学报,2016,20(1):62-72.
[16] ZHAO W,LI A.A downscaling method for improving the spatial resolution of AMSR-E derived soil moisture product based on MSG-SEVIRI data[J].Remote sensing,2013,5(12):6790-6811.
[17] 蒙继华,吴炳方,杜鑫,等.高时空分辨率NDVI数据集构建方法[J].遥感学报,2011,15(1):52-59.
[18] BUSETTO L,MERONI M,COLOMBO R.Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series[J].Remote sensing of environment,2008,112:118-131.
[19] ZHAN W F,CHEN Y H,ZHOU J,et al.Spatial simulation of urban heat island intensity based on the support vector machine technique:A case study in Beijing[J].Acta geodaetica et cartographica sinica,2011,40(1):96-103.
[20] 林忠辉,莫兴国. NDVI时间序列谐波分析与地表物候信息获取[J].农业工程学报,2006,22(12):138-144.
[21] KWON H,NASRABADI N M.Kernel orthogonal subspace projection for hyperspectral signal classification[J].IEEE transactions on geoscience and remote sensing,2005,43(12):2952-2962.
[22] 赵辽英,张凯,厉小润.高光谱图像目标检测的核信号空间正交投影法[J].遥感学报,2011,15(1):13-28.
[23] BECK H E,MCVICAR T R,VAN DIJK A I J M,et al. Global evaluation of four AVHRR-NDVI data sets:Inter-comparison and assessment against Landsat imagery[J].Remote sensing of environment,2011,115:2547-2563.
[24] 马明国,宋怡,王旭峰,等.AVHRR,VEGETATION和MODIS时间系列遥感数据产品现状与应用研究进展[J].遥感技术与应用,2012,27(5):663-670.

离散Landsat归一化植被指数的时间序列融合构建

Time series fusion construction of discrete Landsat normalized vegetation index

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