耦合空域重建的植被指数时空融合方法

唐以洁; 王群明

doi:10.11834/jrs.20254449

浏览量 : 0 下载量: 3 CSCD: 0

R-PDF
PDF
导出
分享
收藏
专辑

耦合空域重建的植被指数时空融合方法
Spatial reconstruction-aided spatio-temporal fusion of NDVI images
2025年页码：1-12
网络出版日期：2025-03-19，
DOI： 10.11834/jrs.20254449
稿件说明：

移动端阅览

唐以洁，王群明.XXXX.耦合空域重建的植被指数时空融合方法.遥感学报，XX（XX）： 1-12 DOI： 10.11834/jrs.20254449.

TANG Yijie，WANG Qunming. XXXX. Spatial reconstruction-aided spatio-temporal fusion of NDVI images. National Remote Sensing Bulletin， XX（XX）：1-12 DOI： 10.11834/jrs.20254449.

摘要

高时空分辨率归一化植被指数（Normalized Difference Vegetation Index，NDVI）数据于作物生长监测及参数反演等具有不可或缺的重要性。卫星遥感数据的红波段和近红外波段可作为NDVI的重要数据源。然而，由于卫星传感器功率的限制，所获取遥感数据的空间分辨率和时间分辨率之间通常存在相互制约，难以获得兼具高时空分辨率的NDVI数据。时空融合技术旨在融合具有高时间分辨率、但空间分辨率较低的NDVI数据和具有高空间分辨率、但时间分辨率较低的NDVI数据，以生成兼具高时空分辨率NDVI数据。然而，NDVI时空融合面临一较大挑战，即已知辅助数据所在时刻与预测时刻之间，地表覆盖通常呈现较大变化。为应对这一挑战，本文提出了一种耦合空域重建的时空融合方法（Spatio-temporal fusion then spatial reconstruction，STFSR）方法，用于NDVI的时空融合。STFSR方法充分利用了在时间上接近预测时刻，但含数据缺失（遥感影像云覆盖导致）的高空间分辨率数据来辅助NDVI时空融合预测。该类辅助影像的利用，有效减小了NDVI变化对时空融合的影响。在本文所选取的三个实验区中，所提出的STFSR方法被证明比常用的时空自适应反射率融合模型（Spatial and Temporal Adaptive Reflectance Fusion Model，STARFM）和基于空间加权和虚拟图像对的时空融合（spatial weighting-based virtual image pair-based spatio-temporal fusion，VIPSTF-SW）方法，具有更高的精度。三个实验区中STFSR方法的平均均方根误差（Root Mean Square Error，RMSE）比STARFM和VIPSTF-SW分别降低了0.0217和0.0188，平均相关系数（Correlation Coefficient，CC）分别提升了0.0820和0.0742，相对全局综合误差（Relative Global-dimensional Synthesis Error，ERGAS）分别降低了4.3170和3.8535。此外，当辅助数据云区域面积增大时，STFSR方法虽在精度上呈现下降态势，但整体上仍优于STARFM和VIPSTF-SW方法。STFSR方法为高时空分辨率NDVI数据的生成提供了一种新的思路，且鉴于其原理与优势，该模型于其他高时空分辨率植被指数数据，例如增强型植被指数（EVI）的生成亦具有可观的应用潜力。

Abstract

Normalized Difference Vegetation Index (NDVI) images with fine spatial and temporal resolutions are important data for real-time precise vegetation monitoring. Remote sensing image

as an important data source for producing NDVI data

however

always present a trade-off between the spatial and temporal resolutions due to the limitation in the power of satellites. Generally

sensors with fine spatial resolution always have a long revisit time (e.g.

Landsat images)

while sensors with a short revisit period always have coarse spatial resolution (e.g.

Moderate-resolution Imaging Spectroradiometer (MODIS) images). Spatio-temporal fusion technique can be applied to generate NDVI images with both fine spatial and temporal resolutions

by fusing NDVI images acquired from these two categories of sensors. The existing spatio-temporal fusion methods

however

suffer from a long-standing challenge

that is

the NDVI change between the images at the known and prediction times

which restricts the accuracy of spatio-temporal fusion prediction greatly. In this paper

a spatio-temporal fusion then spatial reconstruction (STFSR) method was proposed to cope with the NDVI change issue in predicting the 30 m Landsat NDVI images.Generally

when predicting the missing Landsat NDVI image by spatio-temporal fusion

a pair of spatially complete fine and coarse spatial resolution NDVI images is also required (probably temporally far from the prediction time). Except for the original auxiliary images

the proposed STFSR method also included the fine spatial resolution image temporally closer to the prediction time

but with different degrees of data loss caused by cloud cover (hereafter

simplified as auxiliary cloudy NDVI image) in prediction. The implementation of STFSR is divided into two steps: (1) Reconstructing the non-cloud area (the corresponding non-cloud area in the auxiliary cloudy image

but in the image to be predicted) using the spatial and temporal adaptive reflectance fusion model (STARFM). (2) Reconstructing the cloud area (the corresponding cloud area in the auxiliary cloudy image

but in the image to be predicted) by a spatial-temporal random forest (STRF) algorithm

a spatial reconstruction method integrating the information from both fine and coarse spatial resolution NDVI images.In the experiments in three regions

the effectiveness of the proposed STFSR method was evaluated

by comparing with two commonly used spatio-temporal fusion methods

the STARFM and the spatial weighting-based virtual image pair-based spatio-temporal fusion (VIPSTF-SW) algorithms. The results demonstrate that the proposed STFSR can produce greater accuracy than the other two methods for all three regions. Furthermore

when the cloud coverage increases to a certain percentage (e.g.

80%) in the auxiliary cloudy image

the STFSR method can still provide a more satisfactory prediction than two benchmark methods. Specifically

the average Root Mean Square Error (RMSE) of STFSR is 0.0217 and 0.0188 smaller than that of STARFM and VIPSTF-SW

respectively. The corresponding average Correlation Coefficient (CC) is 0.0820 and 0.0742 larger

and the corresponding average Relative Global-dimensional Synthesis Error (ERGAS) is 4.3170 and 3.8535 smaller.The proposed STFSR method takes full advantage of the important information in the cloudy

but temporally closer NDVI image

which fails to be utilized in existing spatio-temporal fusion methods. Generally

the proposed STFSR method provides a flexible solution to deal with the NDVI change in spatio-temporal fusion. Moreover

this model has great potential for the generation of other vegetation index data with fine spatial and temporal resolutions

such as the Enhanced Vegetation Index (EVI) and the Leaf Area Index (LAI).

关键词

Keywords

references

Amorós-López J , Gómez-Chova L , Alonso L , Guanter L , Zurita-Milla R , Moreno J , Camps-Valls G , 2013 . Multitemporal fusion of Landsat/TM and ENVISAT/MERIS for crop monitoring . International Journal of Applied Earth Observation and Geoinformation , 23 : 132 - 141 [ DOI: 10.1016/j.jag.2012.12.004 http://dx.doi.org/10.1016/j.jag.2012.12.004 ]

Busetto L , Meroni M , Colombo R , 2008 . Combining medium and coarse spatial resolution satellite data to improve the estimation of sub-pixel NDVI time series . Remote Sensing of Environment , 112 ( 1 ): 118 - 131 [ DOI: 10.1016/j.rse.2007.04.004 http://dx.doi.org/10.1016/j.rse.2007.04.004 ]

Cao R , Chen Y , Chen J , Zhu X , Shen M , 2020 . Thick cloud removal in Landsat images based on autoregression of Landsat time-series data . Remote Sensing of Environment , 249 : 112001 [ DOI: 10.1016/j.rse.2020.112001 http://dx.doi.org/10.1016/j.rse.2020.112001 ]

Chen Y , Cao R , Chen J , Liu L , Matsushita B , 2021 . A practical approach to reconstruct high-quality Landsat NDVI time-series data by gap filling and the Savitzky–Golay filter . ISPRS Journal of Photogrammetry and Remote Sensing , 180 : 174 - 190 [ DOI: 10.1016/j.isprsjprs.2021.08.015 http://dx.doi.org/10.1016/j.isprsjprs.2021.08.015 ]

Chu D , Shen H , Guan X , Chen J M , Li X , Li J , Zhang L , 2021 . Long time-series NDVI reconstruction in cloud-prone regions via spatio-temporal tensor completion . Remote Sensing of Environment , 264 : 112632 [ DOI: https://doi.org/10.1016/j.rse.2021.112632 https://doi.org/10.1016/j.rse.2021.112632 ]

Das M , Ghosh S K , 2016 . Deep-STEP: a deep Learning approach for spatiotemporal prediction of remote sensing data . IEEE Geoscience and Remote Sensing Letters , 13 ( 12 ): 1984 - 1988 [ DOI: 10.1109/lgrs.2016.2619984 http://dx.doi.org/10.1109/lgrs.2016.2619984 ]

Gao F , Masek J , Schwaller M , Hall F , 2006 . On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance . IEEE Transactions on Geoscience and Remote Sensing , 44 ( 8 ): 2207 - 2218 [ DOI: 10.1109/tgrs.2006.872081 http://dx.doi.org/10.1109/tgrs.2006.872081 ]

Gevaert C , García-Haro F , 2015 . A comparison of STARFM and an unmixing-based algorithm for Landsat and MODIS data fusion . Remote Sensing of Environment , 156 : 34 - 44 [ DOI: 10.1016/j.rse.2014.09.012 http://dx.doi.org/10.1016/j.rse.2014.09.012 ]

Guo D , Shi W , Hao M , Zhu X , 2020 . FSDAF 2 .0: Improving the performance of retrieving land cover changes and preserving spatial details. Remote Sensing of Environment , 248: 111973 [ DOI: 10.1016/j.rse.2020.111973 http://dx.doi.org/10.1016/j.rse.2020.111973 ]

Hilker T , Wulder M A , Coops N C , Linke J , McDermid G , Masek J G , Gao F , White J C , 2009 . A new data fusion model for high spatial- and temporal-resolution mapping of forest disturbance based on Landsat and MODIS . Remote Sensing of Environment , 113 ( 8 ): 1613 - 1627 [ DOI: 10.1016/j.rse.2009.03.007 http://dx.doi.org/10.1016/j.rse.2009.03.007 ]

Ke Y , Im J , Park S , Gong H , 2016 . Downscaling of MODIS one kilometer evapotranspiration using Landsat-8 data and machine learning approaches . Remote Sensing , 8 ( 3 ): 215 [ DOI: 10.3390/rs8030215 http://dx.doi.org/10.3390/rs8030215 ]

Li X , Foody G M , Boyd D S , Ge Y , Zhang Y , Du Y , Ling F , 2020 . SFSDAF: An enhanced FSDAF that incorporates sub-pixel class fraction change information for spatio-temporal image fusion . Remote Sensing of Environment , 237 : 111537 [ DOI: 10.1016/j.rse.2019.111537 http://dx.doi.org/10.1016/j.rse.2019.111537 ]

Liu M , Yang W , Zhu X , Chen J , Chen X , Yang L , Helmer E H , 2019 . An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series . Remote Sensing of Environment , 227 : 74 - 89 [ DOI: 10.1016/j.rse.2019.03.012 http://dx.doi.org/10.1016/j.rse.2019.03.012 ]

Maselli F , 2001 . Definition of spatially variable spectral endmembers by locally calibrated multivariate regression analysis . Remote Sensing of Environment , 75 : 29 - 38 [ DOI: 10.1016/S0034-4257(00)00153-X http://dx.doi.org/10.1016/S0034-4257(00)00153-X ]

Ranchin T , Wald L , 2000 . Fusion of high spatial and spectral resolution images: the ARSIS concept and its implementation . Photogrammetric Engineering & Remote Sensing , 66 : 49 - 61 [

Song H , Huang B , 2012 . Spatiotemporal reflectance fusion via sparse representation . IEEE Transactions on Geoscience and Remote Sensing , 50 ( 10 ): 3707 - 3716 [ DOI: 10.1109/TGRS.2012.2186638 http://dx.doi.org/10.1109/TGRS.2012.2186638 ]

Song H , Huang B , 2013 . Spatiotemporal satellite image fusion through one-pair image learning . IEEE Transactions on Geoscience and Remote Sensing , 51 ( 4 ): 1883 - 1896 [ DOI: 10.1109/tgrs.2012.2213095 http://dx.doi.org/10.1109/tgrs.2012.2213095 ]

Song H , Liu Q , Wang G , Hang R , Huang B , 2018 . Spatiotemporal satellite image fusion using deep convolutional neural networks . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 11 ( 3 ): 1 - 9 [ DOI: 10.1109/JSTARS.2018.2797894 http://dx.doi.org/10.1109/JSTARS.2018.2797894 ]

Verger A , Baret F , Weiss M , Kandasamy S , Vermote E , 2013 . The CACAO method for smoothing, gap filling, and characterizing seasonal anomalies in satellite time series . IEEE Transactions on Geoscience and Remote Sensing , 51 ( 4 ): 1963 - 1972 [ DOI: 10.1109/tgrs.2012.2228653 http://dx.doi.org/10.1109/tgrs.2012.2228653 ]

Wang Q , Atkinson P M , 2018 . Spatio-temporal fusion for daily Sentinel-2 images . Remote Sensing of Environment , 204 : 31 - 42 [ DOI: 10.1016/j.rse.2017.10.046 http://dx.doi.org/10.1016/j.rse.2017.10.046 ]

Wang Q , Tang Y , Tong X , Atkinson P M , 2020 . Virtual image pair-based spatio-temporal fusion . Remote Sensing of Environment , 249 : 112009 [ DOI: 10.1016/j.rse.2020.112009 http://dx.doi.org/10.1016/j.rse.2020.112009 ]

Wang Q , Tang Y , Tong X , Atkinson P M , 2024 . Filling gaps in cloudy Landsat LST product by spatial-temporal fusion of multi-scale data . Remote Sensing of Environment , 306 : 114142 [ DOI: 10.1016/j.rse.2024.114142 http://dx.doi.org/10.1016/j.rse.2024.114142 ]

Wu M , Niu Z , Wang C , Wu C , Wang L , 2012 . Use of MODIS and Landsat time series data to generate high-resolution temporal synthetic Landsat data using a spatial and temporal reflectance fusion model . Journal of Applied Remote Sensing , 6 ( 1 ): 063507 [ DOI: 10.1117/1.Jrs.6.063507 http://dx.doi.org/10.1117/1.Jrs.6.063507 ]

Xu H , Ren M , Yang L , 2023 . Evaluating the consistency of surface brightness, greenness, and wetness observations between Landsat-8 OLI and Landsat-9 OLI2 through underfly images . International Journal of Applied Earth Observation and Geoinformation , 124 : 103546 [ DOI: 10.1016/j.jag.2023.103546 http://dx.doi.org/10.1016/j.jag.2023.103546 ]

Xu Y , Huang B , Dr Y , Cao K , Guo C , Meng D , 2015 . Spatial and temporal image fusion via regularized spatial unmixing . IEEE Geoscience and Remote Sensing Letters , 12 ( 6 ): 1362 - 1366 [ DOI: 10.1109/lgrs.2015.2402644 http://dx.doi.org/10.1109/lgrs.2015.2402644 ]

Yin G , Li A , Jin H , Zhao W , Bian J , Qu Y , Zeng Y , Xu B J A , Meteorology F , 2017 . Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO . 233(Complete) : 209- 221 [

Zhang H , Chen J M , Huang B , Song H , Li Y , 2014 . Reconstructing seasonal variation of Landsat vegetation index related to leaf area index by fusing with MODIS data . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 7 ( 3 ): 950 - 960 [ DOI: 10.1109/jstars.2013.2284528 http://dx.doi.org/10.1109/jstars.2013.2284528 ]

Zhu X , Cai F , Tian J , Williams T K-A , 2018 . Spatiotemporal fusion of multisource remote sensing data: literature survey, taxonomy, principles, applications, and future directions . Remote Sensing , 10 ( 4 ): 527 [ DOI: 10.3390/rs10040527 http://dx.doi.org/10.3390/rs10040527 ]

Zhu X , Chen J , Gao F , Chen X , Masek J G , 2010 . An enhanced spatial and temporal adaptive reflectance fusion model for complex heterogeneous regions . Remote Sensing of Environment , 114 ( 11 ): 2610 - 2623 [ DOI: 10.1016/j.rse.2010.05.032 http://dx.doi.org/10.1016/j.rse.2010.05.032 ]

Zhu X , Helmer E H , Gao F , Liu D , Chen J , Lefsky M A , 2016 . A flexible spatiotemporal method for fusing satellite images with different resolutions . Remote Sensing of Environment , 172 : 165 - 177 [ DOI: 10.1016/j.rse.2015.11.016 http://dx.doi.org/10.1016/j.rse.2015.11.016 ]

Zhukov B , Oertel D , Lanzl F , Reinhackel G , 1999 . Unmixing-based multisensor multiresolution image fusion . IEEE Transactions on Geoscience and Remote Sensing , 37 ( 3 ): 1212 - 1226 [ DOI: 10.1109/36.763276 http://dx.doi.org/10.1109/36.763276 ]

Zurita-Milla R , Gomez-Chova L , Guanter L , Clevers J G P W , Camps-Valls G , 2011 . Multitemporal unmixing of Medium-Spatial-Resolution Satellite Images: a case study using MERIS images for land-cover mapping . IEEE Transactions on Geoscience and Remote Sensing , 49 ( 11 ): 4308 - 4317 [ DOI: 10.1109/tgrs.2011.2158320 http://dx.doi.org/10.1109/tgrs.2011.2158320 ]

Dong W , Meng J . Review of spatiotemporal fusion model of remote sensing data, 2018 . Remote Sensing for Natural Resources , 30 ( 2 ): 1 - 11

董文全 , 蒙继华 , 2018 . 遥感数据时空融合研究进展及展望 . 国土资源遥感 , 30 ( 2 ): 1 - 11 [ DOI: 10.6046/gtzyyg.2018.02.01 http://dx.doi.org/10.6046/gtzyyg.2018.02.01 ]

Liu J , Ma Y , Wu Y , Chen F , 2016 . Review of methods and applications of high spatiotemporal fusion of remote sensing data . Journal of Remote Sensing , 20 ( 5 ): 1038 - 1049

刘建波 , 马勇 , 武易天 , 陈甫 , 2016 . 遥感高时空融合方法的研究进展及应用现状 . 遥感学报 , 20 ( 5 ): 1038 - 1049 [ DOI: 10.11834/jrs.20166218 http://dx.doi.org/10.11834/jrs.20166218 ]

Wang Q , Zhang Z , Zhang C , 2023 . Downscaling GF-5 hyperspectral images by fusing with Sentinel-2 images . Journal of Remote Sensing , 27 ( 8 ): 1936 - 1950

王群明 , 张智昊 , 张成媛 , 2023 . 融合Sentinel-2数据的高分五号高光谱数据降尺度 . 遥感学报 , 27 ( 8 ): 1936 - 1950 [ DOI： 10.11834/jrs.20211420 http://dx.doi.org/10.11834/jrs.20211420 ]

Zhang Z , Wang Q , Ding X , 2024 . FVC-Net： A fusion network for producing fine spatial resolution fractional vegetation cover . Journal of Remote Sensing , 28 ( 12 ): 3184 - 3196

张智昊 , 王群明 , 丁欣宇 , . 2024 . FVC-Net：高空间分辨率植被覆盖度的融合网络 . 遥感学报 , 28 ( 12 ): 3184 - 3196 [ DOI： 10.11834/jrs.20243112 http://dx.doi.org/10.11834/jrs.20243112 ]

文章被引用时，请邮件提醒。

提交

基于IDL语言的植被指数UNVI软件插件

融合Landsat ETM+和MODIS数据估算高时空分辨率地表短波反照率

基于超分辨率重建的多时相MODIS与Landsat反射率融合方法

MODIS植被指数时间序列Savitzky-Golay滤波算法重构