A review of spatiotemporal fusion methods for remotely sensed land surface temperature

LI Ran; WANG Mengmeng; ZHANG Zhengjia; HU Tian; LIU Xiuguo

doi:10.11834/jrs.20210294

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A review of spatiotemporal fusion methods for remotely sensed land surface temperature
Vol. 26, Issue 12, Pages: 2433-2450(2022)
Received：28 July 2020，

Published：07 December 2022
DOI： 10.11834/jrs.20210294
稿件说明：

移动端阅览

李冉，王猛猛，张正加，胡添，刘修国.2022.遥感地表温度产品时空融合方法研究综述.遥感学报，26（12）： 2433-2450 DOI： 10.11834/jrs.20210294.

Li R，Wang M M，Zhang Z J，Hu T and Liu X G. 2022. A review of spatiotemporal fusion methods for remotely sensed land surface temperature. National Remote Sensing Bulletin， 26（12）：2433-2450 DOI： 10.11834/jrs.20210294.

摘要

受传感器性能的制约，利用单一卫星热红外遥感数据反演到的地表温度产品难以兼顾时间分辨率和空间分辨率，而时空融合技术能够发挥多传感器互补的优势，获得时间密集度高、空间细节丰富的地表温度产品，从而在算法层面上解决这一矛盾。随着时空融合研究的深入，地表温度开始显露出与其他产品有着明显区别的融合特性，而内在机理和应用潜力都有待被整理和挖掘。本文就立足于地表温度与时空融合的交叠区，对两者结合而生的研究成果进行收集、分析和总结，系统地概述了该领域的研究背景、原理、方法和应用，并重点突出与泛在时空融合技术的之间的联系与区别；最后，基于地表温度数据的特点和时空融合的局限性，总结了该领域所面临的主要挑战，并对可行的解决方法和发展方向进行了展望。

Abstract

Remotely sensed Land Surface Temperature (LST) from a single source rarely has high temporal and spatial resolutions due to the sensors’ optical characteristics. Spatiotemporal fusion uses data from multiple sources to retrieve LST with high temporal frequency and spatial detail

and the spatiotemporal contradiction is disentangled in the fusion process. According to an in-depth study of spatiotemporal fusion

LST exhibits unique features distinct from other land surface variables. However

the inherent mechanism and potential application of LST spatio-temporal fusion have yet to be compiled and extensively explored. Based on the intersection between LST and spatiotemporal fusion

this work collects

analyzes

and summarizes the state-of-the-art developments in LST spatio-temporal fusion. The research background

principles

methods

and applications of this field are systematically elaborated. In particular

the relations and differences with the ubiquitous spatiotemporal fusion technology are emphasized.

In essence

spatiotemporal fusion methods extract exquisite temporal variation of pixels from the low spatial resolution images and obtain spatial correspondence from images at various scales to predict high spatial resolution images. The spatiotemporal fusion shows great promise over homogeneous and stable land surface

but has an unsatisfactory performance over heterogeneous landscapes with unstable thermal conditions. In comparison with Land Surface Reflectance (LSR)

the spatiotemporal fusion for LST can be less sensitive to the land cover classification uncertainties because of its lower spatial resolution and lower diversity among different land types

but it is difficult to achieve using the general laws for accurate prediction due to the drastic temporal variation of LST.

After spatiotemporal fusion was successfully implemented in LSR

several studies adapted it to LST with some improvements based on the thermal characteristics. In the existing five categories of spatiotemporal fusion models based on weight and learning

Bayesian and hybrid models have been applied to LST. Among these models

the weight models are more mature

robust

and effective

but they cannot easily capture the temporal change of LST. Furthermore

the improvement is relatively limited based on STARFM

ESTARFM

or other classical weight models. Learning models can realize a nonlinear prediction based on the structural similarity of training data when supported by reliable network architecture and abundant training. In particular

the deep learning models have more superior ability to depict and extract the LST with weak spectral characteristics

but suitable neural networks and model parameters must be selected and optimized. Although fusion studies based on the Bayesian framework (including maximum a posterior and Bayesian maximum entropy) are relatively rare

they have shown great potential for achieving unbiased and nonlinear predictions and low-quality requirements for the initial data as LST. The hybrid models can integrate the preponderances of the above-mentioned models and acquire more flexible

efficient

and accurate prediction results compared with a single fusion model

which could be the mainstream of the future spatiotemporal fusion model.

Although the spatiotemporal fusion models are consistently developed

most of them only focus on generating fused products

with a lack of quantitative and qualitative analysis with respect to the practical applications of the fused LST products

such as agriculture and ecology. In this work

the applications in this field are divided into six aspects: land temperature

sea surface temperature

agroforestry

urban heat island

public health

and others

which cover the majority of remote sensing service fields. However

the breadth and depth of the application of the LST fusion products are less than those of LSR fusion products. The mutual development between theoretical research and application demand is urgently needed.

The primary impediment to the application and dissemination of spatiotemporal fusion is the data itself

as evidenced by the diversity of multi-source data

the spatial continuity of image

and the sensitivity of temperature in time series. The angular effect

unstable inversion accuracy

and dramatical diurnal variation significantly constrain their potential applications. Considering these characteristics of LST and existing defects of the spatiotemporal fusion model

this work proposed the future work prospects

such as improving LST inversion accuracy

complementing the strengths of multi-source data

employing a deep learning model

enhancing algorithm flexibility

and constructing a spatiotemporal fusion integrated procedure. The implementation of these strategies will propel the development of theoretical research and operational application of LST with the spatiotemporal fusion technology.

关键词

Keywords

references

Alsweiss S , Jelenak Z and Chang P . 2017 . Remote sensing of sea surface temperature using AMSR-2 measurements . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 10 ( 9 ): 3948 - 3954 [ DOI: 10.1109/JSTARS.2017.2737470 http://dx.doi.org/10.1109/JSTARS.2017.2737470 ]

Amorós-López J , Gómez-Chova L , Alonso L , Guanter L , Zurita-Milla R , Moreno J and 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 ]

Anderson M C , Norman J M , Kustas W P , Houborg R , Starks P J and Agam N . 2008 . A thermal-based remote sensing technique for routine mapping of land-surface carbon, water and energy fluxes from field to regional scales . Remote Sensing of Environment , 112 ( 12 ): 4227 - 4241 [ DOI: 10.1016/j.rse.2008.07.009 http://dx.doi.org/10.1016/j.rse.2008.07.009 ]

Bai J , Liu S M and Hu G . 2008 . Inversion and verification of land surface temperature with remote sensing TM/ETM + data . Transactions of the CSAE , 24 ( 9 ): 148 - 154

白洁 , 刘绍民 , 扈光 . 2008 . 针对TM/ETM+遥感数据的地表温度反演与验证 . 农业工程学报 , 24 ( 9 ): 148 - 154 [ DOI: 10.3321/j.issn:1002-6819.2008.09.030 http://dx.doi.org/10.3321/j.issn:1002-6819.2008.09.030 ]

Belgiu M and Stein A . 2019 . Spatiotemporal image fusion in remote sensing . Remote Sensing , 11 ( 7 ): 818 [ DOI: 10.3390/rs11070818 http://dx.doi.org/10.3390/rs11070818 ]

Bhattarai N , Quackenbush L J , Dougherty M and Marzen L J . 2015 . A simple Landsat-MODIS fusion approach for monitoring seasonal evapotranspiration at 30 m spatial resolution . International Journal of Remote Sensing , 36 ( 1 ): 115 - 143 [ DOI: 10.1080/01431161.2014.990645 http://dx.doi.org/10.1080/01431161.2014.990645 ]

Boyte S P , Wylie B K , Rigge M B and Dahal D . 2018 . Fusing MODIS with Landsat 8 data to downscale weekly normalized difference vegetation index estimates for Central Great Basin rangelands, USA . Giscience and Remote Sensing , 55 ( 3 ): 376 - 399 [ DOI: 10.1080/15481603.2017.1382065 http://dx.doi.org/10.1080/15481603.2017.1382065 ]

Cheng Q , Liu H Q , Shen H F , Wu P H and Zhang L P . 2017 . A spatial and temporal nonlocal filter-based data fusion method . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 8 ): 4476 - 4488 [ DOI: 10.1109/tgrs.2017.2692802 http://dx.doi.org/10.1109/tgrs.2017.2692802 ]

Cline B L . 1970 . New eyes for epidemiologists: aerial photography and other remote sensing techniques . American Journal of Epidemiology , 92 ( 2 ): 85 - 89 [ DOI: 10.1093/oxfordjournals.aje.a121188 http://dx.doi.org/10.1093/oxfordjournals.aje.a121188 ]

DeLang M , Becker J , Chang K-L , Serre M , Cooper O , Schultz M , Schroeder S , Lu X , Zhang L , Deushi M , Josse B , Keller C A , Lamarque J-F , Lin M , Liu J , Marécal V , Strode S A , Sudo K , Tilmes S , Zhang L , Cleland S E , Collins E L , Brauer M , West J J . 2021 . Mapping yearly fine resolution global surface ozone through the Bayesian maximum entropy data fusion of observations and model output for 1990—2017 . Environmental Science & Technology , 55 ( 8 ): 4389 - 4398 . [ DOI: 10.1021/acs.est.0c07742 http://dx.doi.org/10.1021/acs.est.0c07742 ]

Ding R J and Zhao C F . 2018 . Study on the merging sea surface temperature data based on optimal interpolation and bayesian maximum entropy method . Journal of Ocean Technology , 37 ( 2 ): 35 - 42

丁润杰 , 赵朝方 . 2018 . 基于最优插值和贝叶斯最大熵的海表温度融合方法研究 . 海洋技术学报 , 37 ( 2 ): 35 - 42 [ DOI: 10.3969/j.issn.1003-2029.2018.02.007 http://dx.doi.org/10.3969/j.issn.1003-2029.2018.02.007 ]

Dong W Q and Meng J H . 2018 . Review of spatiotemporal fusion model of remote sensing data . Remote Sensing for Land and 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 ]

Fu D J , Chen B Z , Wang J , Zhu X L and Hilker T . 2013 . An improved image fusion approach based on enhanced spatial and temporal the adaptive reflectance fusion model . Remote Sensing , 5 ( 12 ): 6346 - 6360 [ DOI: 10.3390/rs5126346 http://dx.doi.org/10.3390/rs5126346 ]

Gao F , Masek J , Schwaller M and 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 ]

Gao H , Zhu X , Guan Q , Yang X , Yao Y , Zeng W and Peng X . 2021 . cuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units . IEEE Transactions on Geoscience and Remote Sensing , 60 : 1 - 16 .[ DOI: 10.1109/TGRS.2021.3080384 http://dx.doi.org/10.1109/TGRS.2021.3080384 ]

Gevaert C M and García-Haro F J . 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 ]

Gong C , Zhou C , Xiao H S , Wu X and Zhang G . 2018 . Dynamic monitoring of forest land drought in Hunan based on multisource remote sensing data fusion . Journal of Central South University of Forestry and Technology , 38 ( 10 ): 27 - 33

龚成 , 周璀 , 肖化顺 , 吴鑫 , 张贵 . 2018 . 基于多源遥感数据融合的湖南林地干旱动态监测研究 . 中南林业科技大学学报 , 38 ( 10 ): 27 - 33 [ DOI: 10.14067/j.cnki.1673-923x.2018.10.005 http://dx.doi.org/10.14067/j.cnki.1673-923x.2018.10.005 ]

Guan X D , Liu G H , Huang C , Liu Q S , Wu C S , Jin Y and Li Y F . 2017 . An object-based linear weight assignment fusion scheme to improve classification accuracy using Landsat and MODIS data at the decision level . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 12 ): 6989 - 7002 [ DOI: 10.1109/tgrs.2017.2737780 http://dx.doi.org/10.1109/tgrs.2017.2737780 ]

Guo D Z , Shi W Z , Hao M and Zhu X L . 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 ]

Guo Y L , Li Y M , Zhu L , Liu G , Wang S and Du C G . 2015 . An improved unmixing-based fusion method: potential application to remote monitoring of inland waters . Remote Sensing , 7 ( 2 ): 1640 - 1666 [ DOI: 10.3390/rs70201640 http://dx.doi.org/10.3390/rs70201640 ]

Hall D L and Llinas J . 1997 . An introduction to multisensor data fusion . Proceedings of the IEEE , 85 ( 1 ): 6 - 23 [ DOI: 10.1109/5.554205 http://dx.doi.org/10.1109/5.554205 ]

Han X J , Duan S B , Huang C and Li Z L . 2019 . Cloudy land surface temperature retrieval from three-channel microwave data . International Journal of Remote Sensing , 40 ( 5-6 ): 1793 - 1807 . [ DOI: 10.1080/01431161.2018.1471552 http://dx.doi.org/10.1080/01431161.2018.1471552 ]

Hazaymeh K and Hassan Q K . 2015 . Spatiotemporal image-fusion model for enhancing the temporal resolution of Landsat-8 surface reflectance images using MODIS images . Journal of Applied Remote Sensing , 9 ( 1 ): 1 - 14 [ DOI: 10.1117/1.Jrs.9.096095 http://dx.doi.org/10.1117/1.Jrs.9.096095 ]

Hilker T , Wulder M A , Coops N C , Linke J , McDermid G , Masek J G , Gao F and 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 ]

Houborg R , McCabe M F and Gao F . 2016 . A spatio-temporal enhancement method for medium resolution LAI (STEM-LAI) . International Journal of Applied Earth Observation and Geoinformation , 47 : 15 - 29 [ DOI: 10.1016/j.jag.2015.11.013 http://dx.doi.org/10.1016/j.jag.2015.11.013 ]

Hu H L , Cheng Y H and Gong A D . 2005 . Advances in the application of remotely sensed data to the study of urban heat island . Remote Sensing for Land and Resources , 17 ( 3 ): 5 - 9 , 13

胡华浪 , 陈云浩 , 宫阿都 . 2005 . 城市热岛的遥感研究进展 . 国土资源遥感 , 17 ( 3 ): 5- 9 , 13 [ DOI: 10.3969/j.issn.1001-070X.2005.03.002 http://dx.doi.org/10.3969/j.issn.1001-070X.2005.03.002 ]

Hu T , Li H , Cao B , van Dijk A I J M , Renzullo L J , Xu Z H , Zhou J , Du Y M and Liu Q H . 2019 . Influence of emissivity angular variation on land surface temperature retrieved using the generalized split-window algorithm . International Journal of Applied Earth Observation and Geoinformation , 82 : 1 - 11 [ DOI: 10.1016/j.jag.2019.101917 http://dx.doi.org/10.1016/j.jag.2019.101917 ]

Hu X R . 2018 . Spatial and Temporal Distribution Characteristics of Sea Surface Temperature Based on Ensemble Kalman Filtering Fusion in the Northwest Pacific Ocean . Shanghai Ocean University

胡旭冉 . 2018 . 基于集合卡尔曼滤波融合的西北太平洋海表面温度时空分布特征研究 . 上海海洋大学

Huang B and Song H H . 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 ]

Huang B , Wang J , Song H H , Fu D J and Wong K . 2013a . Generating high spatiotemporal resolution land surface temperature for urban heat island monitoring . IEEE Geoscience and Remote Sensing Letters , 10 ( 5 ): 1011 - 1015 [ DOI: 10.1109/lgrs.2012.2227930 http://dx.doi.org/10.1109/lgrs.2012.2227930 ]

Huang B , Zhang H K , Song H H , Wang J and Song C Q . 2013b . Unified fusion of remote-sensing imagery: generating simultaneously high-resolution synthetic spatial-temporal-spectral earth observations . Remote Sensing Letters , 4 ( 6 ): 561 - 569 [ DOI: 10.1080/2150704x.2013.769283 http://dx.doi.org/10.1080/2150704x.2013.769283 ]

Huang B and Zhao Y Q . 2017 . Research status and prospect of spatiotemporal fusion of multi-source satellite remote sensing imagery . Acta Geodaetica et Cartographica Sinica , 46 ( 10 ): 1492 - 1499

黄波 , 赵涌泉 . 2017 . 多源卫星遥感影像时空融合研究的现状及展望 . 测绘学报 , 46 ( 10 ): 1492 - 1499 [ DOI: 10.11947/j.AGCS.2017.20170376 http://dx.doi.org/10.11947/j.AGCS.2017.20170376 ]

Hwang T , Song C H , Bolstad P V and Band L E . 2011 . Downscaling real-time vegetation dynamics by fusing multi-temporal MODIS and Landsat NDVI in topographically complex terrain . Remote Sensing of Environment , 115 ( 10 ): 2499 - 2512 [ DOI: 10.1016/j.rse.2011.05.010 http://dx.doi.org/10.1016/j.rse.2011.05.010 ]

Jin M L , Dickinson R E and Zhang D . 2005 . The footprint of urban areas on global climate as characterized by MODIS . Journal of Climate 18 ( 10 ): 1551 - 1565 [ DOI: 10.1175/jcli3334.1 http://dx.doi.org/10.1175/jcli3334.1 ]

Karnieli A , Agam N , Pinker R T , Anderson M , Imhoff M L , Gutman G G , Panov N and Goldberg A . 2010 . Use of NDVI and land surface temperature for drought assessment: merits and Limitations . Journal of Climate , 23 ( 3 ): 618 - 633 [ DOI: 10.1175/2009jcli2900.1 http://dx.doi.org/10.1175/2009jcli2900.1 ]

Ke Y H , Im J , Park S and Gong H L . 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 ]

Kou X K , Jiang L M , Bo Y C , Yan S and Chai L N . 2016 . Estimation of land surface temperature through blending MODIS and AMSR-E data with the Bayesian maximum entropy method . Remote Sensing , 8 ( 2 ): 105 [ DOI: 10.3390/rs8020105 http://dx.doi.org/10.3390/rs8020105 ]

Kwan C , Budavari B , Gao F and Zhu X L . 2018 . A hybrid color mapping approach to fusing MODIS and Landsat images for forward prediction . Remote Sensing , 10 ( 4 ): 520 [ DOI: 10.3390/rs10040520 http://dx.doi.org/10.3390/rs10040520 ]

Lan T , Shao G , Tang L , Xu Z , Zhu W and Liu L . 2021 . Quantifying Spatiotemporal Changes in Human Activities Induced by COVID-19 Pandemic Using Daily Nighttime Light Data . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 14 : 2740 - 2753 [ DOI: 10.1109/JSTARS.2021.3060038 http://dx.doi.org/10.1109/JSTARS.2021.3060038 ]

Li A H , Bo Y C , Zhu Y X , Guo P , Bi J and He Y Q . 2013 . Blending multi-resolution satellite sea surface temperature (SST) products using Bayesian maximum entropy method . Remote Sensing of Environment , 135 : 52 - 63 [ DOI: 10.1016/j.rse.2013.03.021 http://dx.doi.org/10.1016/j.rse.2013.03.021 ]

Li J , Li Y F , He L , Chen J and Plaza A . 2020 . Spatio-temporal fusion for remote sensing data: an overview and new benchmark . Science China-Information Sciences , 63 ( 4 ): 140301 [ DOI: 10.1007/s11432-019-2785-y http://dx.doi.org/10.1007/s11432-019-2785-y ]

Li X D , Ling F , Foody G M , Ge Y , Zhang Y H and Du Y . 2017a . Generating a series of fine spatial and temporal resolution land cover maps by fusing coarse spatial resolution remotely sensed images and fine spatial resolution land cover maps . Remote Sensing of Environment , 196 : 293 - 311 [ DOI: 10.1016/j.rse.2017.05.011 http://dx.doi.org/10.1016/j.rse.2017.05.011 ]

Li Y , Huang C L , Hou J L , Gu J , Zhu G F and Li X . 2017b . Mapping daily evapotranspiration based on spatiotemporal fusion of ASTER and MODIS images over irrigated agricultural areas in the Heihe River basin, Northwest China . Agricultural and Forest Meteorology , 244 - 245 : 82 - 97 [ DOI: 10.1016/j.agrformet.2017.05.023 http://dx.doi.org/10.1016/j.agrformet.2017.05.023 ]

Liao C H , Wang J F , Pritchard I , Liu J G and Shang J L . 2017 . A spatio-temporal data fusion model for generating NDVI time series in heterogeneous regions . Remote Sensing , 9 ( 11 ): 1125 [ DOI: 10.3390/rs9111125 http://dx.doi.org/10.3390/rs9111125 ]

Liao L M , Song J L , Wang J D , Xiao Z Q and Wang J . 2016 . Bayesian method for building frequent Landsat-like NDVI datasets by integrating MODIS and Landsat NDVI . Remote Sensing , 8 ( 6 ): 452 [ DOI: 10.3390/rs8060452 http://dx.doi.org/10.3390/rs8060452 ]

Liu H and Weng Q H . 2012 . Enhancing temporal resolution of satellite imagery for public health studies: a case study of West Nile virus outbreak in Los Angeles in 2007 . Remote Sensing of Environment , 117 : 57 - 71 [ DOI: 10.1016/j.rse.2011.06.023 http://dx.doi.org/10.1016/j.rse.2011.06.023 ]

Liu J B , Ma Y , Wu Y T and 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 ]

Liu W J , Zeng Y N , Li S N and Huang W . 2020 . Spectral unmixing based spatiotemporal downscaling fusion approach . International Journal of Applied Earth Observation and Geoinformation , 88 : 102054 [ DOI: 10.1016/j.jag.2020.102054 http://dx.doi.org/10.1016/j.jag.2020.102054 ]

Liu X , Deng C W , Wang S G , Huang G B , Zhao B J and Lauren P . 2016 . Fast and accurate spatiotemporal fusion based upon extreme learning machine . IEEE Geoscience and Remote Sensing Letters , 13 ( 12 ): 2039 - 2043 [ DOI: 10.1109/lgrs.2016.2622726 http://dx.doi.org/10.1109/lgrs.2016.2622726 ]

Liu Z H . 2017 . Spatiotemporal Variation Characteristics of Urban Heat Islands in Hefei City, Anhui Province of China Based on Spatio-Temporal Fusion . Anhui University (刘紫涵 . 2017. 基于时空融合技术的合肥市热岛效应时空演变分析. 安徽大学)

Lu M , Chen J , Tang H J , Rao Y H , Yang P and Wu W B . 2016 . Land cover change detection by integrating object-based data blending model of Landsat and MODIS . Remote Sensing of Environment , 184 : 374 - 386 [ DOI: 10.1016/j.rse.2016.07.028 http://dx.doi.org/10.1016/j.rse.2016.07.028 ]

Luo Y N , Guan K Y and Peng J . 2018 . STAIR: a generic and fully-automated method to fuse multiple sources of optical satellite data to generate a high-resolution, daily and cloud-/gap-free surface reflectance product . Remote Sensing of Environment , 214 : 87 - 99 [ DOI: 10.1016/j.rse.2018.04.042 http://dx.doi.org/10.1016/j.rse.2018.04.042 ]

Maselli F and Rembold F . 2002 . Integration of LAC and GAC NDVI data to improve vegetation monitoring in semi-arid environments . International Journal of Remote Sensing , 23 ( 12 ): 2475 - 2488 [ DOI: 10.1080/01431160110104755 http://dx.doi.org/10.1080/01431160110104755 ]

Mizuochi H , Hiyama T , Ohta T , Fujioka Y , Kambatuku J R , Iijima M and Nasahara K N . 2017 . Development and evaluation of a lookup-table-based approach to data fusion for seasonal wetlands monitoring: an integrated use of AMSR series, MODIS, and Landsat . Remote Sensing of Environment , 199 : 370 - 388 [ DOI: 10.1016/j.rse.2017.07.026 http://dx.doi.org/10.1016/j.rse.2017.07.026 ]

Moosavi V , Talebi A , Mokhtari M H , Shamsi S R F and Niazi Y . 2015 . A wavelet-artificial intelligence fusion approach (WAIFA) for blending Landsat and MODIS surface temperature . Remote Sensing of Environment , 169 : 243 - 254 [ DOI: 10.1016/j.rse.2015.08.015 http://dx.doi.org/10.1016/j.rse.2015.08.015 ]

Moreno-Martínez Á , Izquierdo-Verdiguier E , Maneta M P , Camps-Valls G , Robinson N , Muñoz-Marí J , Sedano F , Clinton N and Running S W . 2020 . Multispectral high resolution sensor fusion for smoothing and gap-filling in the cloud . Remote Sensing of Environment , 247 : 111901 [ DOI: 10.1016/j.rse.2020.111901 http://dx.doi.org/10.1016/j.rse.2020.111901 ]

Nirmala D E and Vaidehi V . 2015 . Comparison of pixel-level and feature level image fusion methods // Proceedings of the 2015 2nd International Conference on Computing for Sustainable Global Development . New Delhi : IEEE: 743 - 748 .

Peng S , Tang B H , Li Z L , Wu H and Tang R L . 2016 . Study of the relationship between thermal infrared directional and hemispherical radiative temperatures . Journal of Geo-Information Science , 18 ( 1 ): 106 - 116

彭硕 , 唐伯惠 , 李召良 , 吴骅 , 唐荣林 . 2016 . 热红外地表方向性辐射温度与半球辐射温度关系研究 . 地球信息科学学报 , 18 ( 1 ): 106 - 116 [ DOI: 10.3724/SP.J.1047.2016.00106 http://dx.doi.org/10.3724/SP.J.1047.2016.00106 ]

Quan J L , Zhan W F , Ma T , Du Y Y , Guo Z and Qin B Y . 2018 . An integrated model for generating hourly Landsat-like land surface temperatures over heterogeneous landscapes . Remote Sensing of Environment , 206 : 403 - 423 [ DOI: 10.1016/j.rse.2017.12.003 http://dx.doi.org/10.1016/j.rse.2017.12.003 ]

Rao C V , Rao J M , Kumar A S and Dadhwal V K . 2014 . Fast spatiotemporal data fusion: merging LISS III with AWiFS sensor data . International Journal of Remote Sensing , 35 ( 24 ): 8323 - 8344 [ DOI: 10.1080/01431161.2014.985396 http://dx.doi.org/10.1080/01431161.2014.985396 ]

Rao J M , Rao C V , Kumar A S , Lakshmi B and Dadhwal V K . 2015a . Spatiotemporal data fusion using temporal high-pass modulation and edge primitives . IEEE Transactions on Geoscience and Remote Sensing , 53 ( 11 ): 5853 - 5860 [ DOI: 10.1109/tgrs.2015.2422712 http://dx.doi.org/10.1109/tgrs.2015.2422712 ]

Rao Y H , Zhu X L , Chen J and Wang J M . 2015b . An improved method for producing high spatial-resolution NDVI time series datasets with multi-temporal MODIS NDVI data and Landsat TM/ETM+ images . Remote Sensing , 7 ( 6 ): 7865 - 7991 [ DOI: 10.3390/rs70607865 http://dx.doi.org/10.3390/rs70607865 ]

Roy D P , Ju J C , Lewis P , Schaaf C , Gao F , Hansen M and Lindquist E . 2008 . Multi-temporal MODIS-Landsat data fusion for relative radiometric normalization, gap filling, and prediction of Landsat data . Remote Sensing of Environment , 112 ( 6 ): 3112 - 3130 [ DOI: 10.1016/j.rse.2008.03.009 http://dx.doi.org/10.1016/j.rse.2008.03.009 ]

Sakellariou S , Cabral P , Caetano M , Pla F , Painho M , Christopoulou O , Sfougaris A , Dalezios N and Vasilakos C . 2020 . Remotely sensed data fusion for spatiotemporal geostatistical analysis of forest fire hazard . Sensors 20 ( 17 ): 5014 [ DOI: 10.3390/s20175014 http://dx.doi.org/10.3390/s20175014 ]

Shen H F , Meng X C and Zhang L P . 2016 . An integrated framework for the spatio-temporal-spectral fusion of remote sensing images . IEEE Transactions on Geoscience and Remote Sensing , 54 ( 12 ): 7135 - 7148 [ DOI: 10.1109/tgrs.2016.2596290 http://dx.doi.org/10.1109/tgrs.2016.2596290 ]

Shen H F , Wu P H , Liu Y L , Ai T H , Wang Y and Liu X P . 2013 . A spatial and temporal reflectance fusion model considering sensor observation differences . International Journal of Remote Sensing , 34 ( 12 ): 4367 - 4383 [ DOI: 10.1080/01431161.2013.777488 http://dx.doi.org/10.1080/01431161.2013.777488 ]

Shi C L , Wang X H , Zhang M , Liang X J , Niu L Z , Han H Q and Zhu X M . 2019 . A comprehensive and automated fusion method: the enhanced flexible spatiotemporal data fusion model for monitoring dynamic changes of land surface . Applied Sciences , 9 ( 18 ): 3693 [ DOI: 10.3390/app9183693 http://dx.doi.org/10.3390/app9183693 ]

Song H H and 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 H , Liu Q S , Wang G J , Hang R L and 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 ): 821 - 829 [ DOI: 10.1109/jstars.2018.2797894 http://dx.doi.org/10.1109/jstars.2018.2797894 ]

Sun C H . 2015 . Research and Verification of Generating High Spatial and Temporal Resolution Surface Temperature Based on Fusing Multi-Source Remote Sensing Data . Xi’an University of Science and Technology (孙晨红 . 2015. 多源遥感数据融合生成高时空分辨率地表温度研究与验证. 西安科技大学)

Tan J C , NourEldeen N , Mao K B , Shi J C , Li Z L , Xu T R and Yuan Z J . 2019a . Deep learning convolutional neural network for the retrieval of land surface temperature from AMSR2 data in China . Sensors , 19 ( 13 ): 2987 [ DOI: 10.3390/s19132987 http://dx.doi.org/10.3390/s19132987 ]

Tan Z Y , Di L P , Zhang M D , Guo L Y and Gao M L . 2019b . An enhanced deep convolutional model for spatiotemporal image fusion . Remote Sensing , 11 ( 24 ): 2898 [ DOI: 10.3390/rs11242898 http://dx.doi.org/10.3390/rs11242898 ]

Tan Z Y , Yue P , Di L P and Tang J M . 2018 . Deriving high spatiotemporal remote sensing images using deep convolutional network . Remote Sensing , 10 ( 7 ): 1066 [ DOI: 10.3390/rs10071066 http://dx.doi.org/10.3390/rs10071066 ]

Tao X , Liang S L , Wang D D , He T and Huang C Q . 2018 . Improving satellite estimates of the fraction of absorbed photosynthetically active radiation through data integration: methodology and validation . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 4 ): 2107 - 2118 [ DOI: 10.1109/tgrs.2017.2775103 http://dx.doi.org/10.1109/tgrs.2017.2775103 ]

Tong X-Y , Xia G-S , Lu Q , Shen H , Li S , You S and Zhang L . 2020 . Land-cover classification with high-resolution remote sensing images using transferable deep models . Remote Sensing of Environment , 237 : 111322 [ DOI: 10.1016/j.rse.2019.111322 http://dx.doi.org/10.1016/j.rse.2019.111322 ]

Wang J and Huang B . 2017 . A rigorously-weighted spatiotemporal fusion model with uncertainty analysis . Remote Sensing , 9 ( 10 ): 990 [ DOI: 10.3390/rs9100990 http://dx.doi.org/10.3390/rs9100990 ]

Wang M M , Zhang Z J , Hu T and Liu X G . 2019 . A practical single-channel algorithm for land surface temperature retrieval: application to landsat series data . Journal of Geophysical Research: Atmospheres , 124 ( 1 ): 299 - 316 [ DOI: 10.1029/2018jd029330 http://dx.doi.org/10.1029/2018jd029330 ]

Wang P J , Gao F and Masek J G . 2014 . Operational data fusion framework for building frequent Landsat-like imagery . IEEE Transactions on Geoscience and Remote Sensing , 52 ( 11 ): 7353 - 7365 [ DOI: 10.1109/tgrs.2014.2311445 http://dx.doi.org/10.1109/tgrs.2014.2311445 ]

Wang Q M and 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 M , Blackburn G A , Onojeghuo A O , Dash J , Zhou L Q , Zhang Y H and Atkinson P M . 2017a . Fusion of Landsat 8 OLI and Sentinel-2 MSI data . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 7 ): 3885 - 3899 [ DOI: 10.1109/tgrs.2017.2683444 http://dx.doi.org/10.1109/tgrs.2017.2683444 ]

Wang Q M , Zhang Y H , Onojeghuo A O , Zhu X L and Atkinson P M . 2017b . Enhancing spatio-temporal fusion of MODIS and Landsat data by incorporating 250 m MODIS data . IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing , 10 ( 9 ): 1 - 8 [ DOI: 10.1109/jstars.2017.2701643 http://dx.doi.org/10.1109/jstars.2017.2701643 ]

Wang X F and Wang X Y . 2020 . Spatiotemporal fusion of remote sensing image based on deep learning . Journal of Sensors , 2020 ( 6 ): 1 - 11 . [ DOI: 10.1155/2020/8873079 http://dx.doi.org/10.1155/2020/8873079 ]

Wei J B , Wang L Z , Liu P , Chen X D , Li W and Zomaya A Y . 2017a . Spatiotemporal fusion of MODIS and Landsat-7 reflectance images via compressed sensing . IEEE Transactions on Geoscience and Remote Sensing , 55 ( 12 ): 7126 - 7139 [ DOI: 10.1109/tgrs.2017.2742529 http://dx.doi.org/10.1109/tgrs.2017.2742529 ]

Wei J B , Wang L Z , Liu P and Song W J . 2017b . Spatiotemporal fusion of remote sensing images with structural sparsity and semi-coupled dictionary learning . Remote Sensing , 9 ( 1 ): 21 [ DOI: 10.3390/rs9010021 http://dx.doi.org/10.3390/rs9010021 ]

Wei R . 2016 . Method and Application Research on Spatial and Temporal Fusion with Muiti-Source Remote Sensing of Land Surface Temperature Data . Wuhan University (魏然 . 2016. 多源遥感地表温度数据时空融合研究及应用. 武汉大学)

Weng Q H . 2009 . Thermal infrared remote sensing for urban climate and environmental studies: methods, applications, and trends . ISPRS Journal of Photogrammetry and Remote Sensing , 64 ( 4 ): 335 - 344 [ DOI: 10.1016/j.isprsjprs.2009.03.007 http://dx.doi.org/10.1016/j.isprsjprs.2009.03.007 ]

Weng Q H , Fu P and Gao F . 2014 . Generating daily land surface temperature at Landsat resolution by fusing Landsat and MODIS data . Remote Sensing of Environment , 145 : 55 - 67 [ DOI: 10.1016/j.rse.2014.02.003 http://dx.doi.org/10.1016/j.rse.2014.02.003 ]

Wu B , Huang B , Cao K and Zhuo G H . 2017 . Improving spatiotemporal reflectance fusion using image inpainting and steering kernel regression techniques . International Journal of Remote Sensing , 38 ( 3 ): 706 - 727 [ DOI: 10.1080/01431161.2016.1271471 http://dx.doi.org/10.1080/01431161.2016.1271471 ]

Wu B , Huang B and Zhang L P . 2015a . An error-bound-regularized sparse coding for spatiotemporal reflectance fusion . IEEE Transactions on Geoscience and Remote Sensing , 53 ( 12 ): 6791 - 6803 [ DOI: 10.1109/tgrs.2015.2448100 http://dx.doi.org/10.1109/tgrs.2015.2448100 ]

Wu M Q , Huang W J , Niu Z and Wang C Y . 2015b . Generating daily synthetic landsat imagery by combining landsat and MODIS data . Sensors , 15 ( 9 ): 24002 - 24025 [ DOI: 10.3390/s150924002 http://dx.doi.org/10.3390/s150924002 ]

Wu M Q , Li H , Huang W J , Niu Z and Wang C Y . 2015c . Generating daily high spatial land surface temperatures by combining ASTER and MODIS land surface temperature products for environmental process monitoring . Environmental Science : Processes and Impacts , 17 ( 8 ): 1396 - 1404 [ DOI: 10.1039/c5em00254k http://dx.doi.org/10.1039/c5em00254k ]

Wu M Q , Niu Z , Wang C Y , Wu C Y and 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 ]

Wu P H , Shen H F , Zhang L P and Goettsche F M . 2015d . Integrated fusion of multi-scale polar-orbiting and geostationary satellite observations for the mapping of high spatial and temporal resolution land surface temperature . Remote Sensing of Environment , 156 : 169 - 181 [ DOI: 10.1016/j.rse.2014.09.013 http://dx.doi.org/10.1016/j.rse.2014.09.013 ]

Xie D F , Zhang J S , Zhu X F , Pan Y Z , Liu H L , Yuan Z M Q and Yun Y . 2016 . An improved STARFM with help of an unmixing-based method to generate high spatial and temporal resolution remote sensing data in complex heterogeneous regions . Sensors , 16 ( 2 ): 207 [ DOI: 10.3390/s16020207 http://dx.doi.org/10.3390/s16020207 ]

Xu C Y , Qu J J , Hao X J , Cosh M H , Prueger J H , Zhu Z L and Gutenberg L . 2018 . Downscaling of surface soil moisture retrieval by combining MODIS/Landsat and in situ measurements . Remote Sensing , 10 ( 2 ): 210 [ DOI: 10.3390/rs10020210 http://dx.doi.org/10.3390/rs10020210 ]

Xu F , Hu C , Li J , Plaza A and Datcu M . 2020 . Special focus on deep learning in remote sensing image processing . Science China Information Sciences , 63 ( 4 ): 140300 [ DOI: 10.1007/s11432-020-2810-x http://dx.doi.org/10.1007/s11432-020-2810-x ]

Xu Y , Huang B , Xu Y Y , Cao K , Guo C L and Meng D Y . 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 ]

Xue J , Leung Y and Fung T . 2017 . A Bayesian data fusion approach to spatio-temporal fusion of remotely sensed images . Remote Sensing , 9 ( 12 ): 1310 [ DOI: 10.3390/rs9121310 http://dx.doi.org/10.3390/rs9121310 ]

Yang G J , Sun C H and Li H . 2015 . Verification of high-resolution land surface temperature by blending ASTER and MODIS data in Heihe River basin . Transactions of the Chinese Society of Agricultural Engineering , 31 ( 6 ): 193 - 200

杨贵军 , 孙晨红 , 历华 . 2015 . 黑河流域ASTER与MODIS融合生成高分辨率地表温度的验证 . 农业工程学报 , 31 ( 6 ): 193 - 200 [ DOI: 10.3969/j.issn.1002-6819.2015.06.026 http://dx.doi.org/10.3969/j.issn.1002-6819.2015.06.026 ]

Yang M . 2017 . Comparison and Application of Downscale Spatial and Temporal Fusion Method for Land Surface Temperature . Xi’an University of Science and Technology (杨敏 . 2017. 地表温度降尺度时空融合方法对比及其应用. 西安科技大学)

Yang M , Yang G J , Chen X N , Zhang Y F and You J N . 2018 . Generation of land surface temperature with high spatial and temporal resolution based on FSDAF method . Remote Sensing for Land and Resources , 30 ( 1 ): 54 - 62

杨敏 , 杨贵军 , 陈晓宁 , 张勇峰 , 尤静妮 . 2018 . 基于FSDAF方法融合生成高时空分辨率地表温度 . 国土资源遥感 , 30 ( 1 ): 54 - 62 [ DOI: 10.6046/gtzyyg.2018.01.08 http://dx.doi.org/10.6046/gtzyyg.2018.01.08 ]

Zhai H , Huang F and Qi H . 2020 . Generating high resolution LAI based on a modified FSDAF model . Remote Sensing , 12 ( 1 ): 150 [ DOI: 10.3390/rs12010150 http://dx.doi.org/10.3390/rs12010150 ]

Zhan W F , Chen Y H , Zhou J , Wang J F , Liu W Y , Voogt J , Zhu X L , Quan J L and Li J . 2013 . Disaggregation of remotely sensed land surface temperature: literature survey, taxonomy, issues, and caveats . Remote Sensing of Environment , 131 : 119 - 139 [ DOI: 10.1016/j.rse.2012.12.014 http://dx.doi.org/10.1016/j.rse.2012.12.014 ]

Zhang L F , Peng M Y , Sun X J , Cen Y and Tong Q X . 2019 . Progress and bibliometric analysis of remote sensing data fusion methods ( 1992 — 2018 ). Journal of Remote Sensing , 23 ( 4 ): 603 - 619

张立福 , 彭明媛 , 孙雪剑 , 岑奕 , 童庆禧 . 2019 . 遥感数据融合研究进展与文献定量分析 ( 1992 — 2018 ). 遥感学报 , 23 ( 4 ): 603 - 619 [ DOI: 10.11834/jrs.20199073 http://dx.doi.org/10.11834/jrs.20199073 ]

Zhang L P and Shen H F . 2016 . Progress and future of remote sensing data fusion . Journal of Remote Sensing , 20 ( 5 ): 1050 - 1061

张良培 , 沈焕锋 . 2016 . 遥感数据融合的进展与前瞻 . 遥感学报 , 20 ( 5 ): 1050 - 1061 [ DOI: 10.11834/jrs.20166243 http://dx.doi.org/10.11834/jrs.20166243 ]

Zhang W , Li A N , Jin H A , Bian J H , Zhang Z J , Lei G B , Qin Z H and Huang C Q . 2013 . An enhanced spatial and temporal data fusion model for fusing Landsat and MODIS surface reflectance to generate high temporal Landsat-like data . Remote Sensing , 5 ( 10 ): 5346 - 5368 [ DOI: 10.3390/rs5105346 http://dx.doi.org/10.3390/rs5105346 ]

Zhang Y H , Foody G M , Ling F , Li X D , Ge Y , Du Y and Atkinson P M . 2018 . Spatial-temporal fraction map fusion with multi-scale remotely sensed images . Remote Sensing of Environment , 213 : 162 - 181 [ DOI: 10.1016/j.rse.2018.05.010 http://dx.doi.org/10.1016/j.rse.2018.05.010 ]

Zhang Y J . 2019 . Research on Landsat Surface Temperture Data Reconstruction Strategy Based on Spatio-Temporal Fusion Technology . Taiyuan University of Technology

张亚军 . 2019 . 基于时空融合技术的Landsat地表温度数据重建策略研究 . 太原理工大学

Zhao C Y , Gao X B , Emery W J , Wang Y and Li J . 2018a . An integrated spatio-spectral-temporal sparse representation method for fusing remote-sensing images with different resolutions . IEEE Transactions on Geoscience and Remote Sensing , 56 ( 6 ): 3358 - 3370 [ DOI: 10.1109/tgrs.2018.2798663 http://dx.doi.org/10.1109/tgrs.2018.2798663 ]

Zhao G H , Zhang Y N , Tan J L , Li C and Ren Y R . 2020a . A data fusion modeling framework for retrieval of land surface temperature from Landsat-8 and MODIS data . Sensors , 20 ( 15 ): 4337 [ DOI: 10.3390/s20154337 http://dx.doi.org/10.3390/s20154337 ]

Zhao H , Gallo O , Frosio I and Kautz J . 2017 . Loss functions for image restoration with neural networks . IEEE Transactions on Computational Imaging , 3 ( 1 ): 47 - 57 [ DOI: 10.1109/tci.2016.2644865 http://dx.doi.org/10.1109/tci.2016.2644865 ]

Zhao W , Duan S B , Li A N and Yin G F . 2019 . A practical method for reducing terrain effect on land surface temperature using random forest regression . Remote Sensing of Environment , 221 : 635 - 649 [ DOI: 10.1016/j.rse.2018.12.008 http://dx.doi.org/10.1016/j.rse.2018.12.008 ]

Zhao W , Guo Z , Yue J , Zhang X and Luo L . 2015 . On combining multiscale deep learning features for the classification of hyperspectral remote sensing imagery . International Journal of Remote Sensing , 36 : 3368 - 3379 . [ DOI: 10.1080/2150704X.2015.1062157 http://dx.doi.org/10.1080/2150704X.2015.1062157 ]

Zhao Y , Liu D and Wei X . 2020b . Monitoring cyanobacterial harmful algal blooms at high spatiotemporal resolution by fusing Landsat and MODIS imagery . Environmental Advances 2 : 100008 . [ DOI: 10.1016/j.envadv.2020.100008 http://dx.doi.org/10.1016/j.envadv.2020.100008 ]

Zhao Y Q , Huang B and Song H H . 2018b . A robust adaptive spatial and temporal image fusion model for complex land surface changes . Remote Sensing of Environment , 208 : 42 - 62 [ DOI: 10.1016/j.rse.2018.02.009 http://dx.doi.org/10.1016/j.rse.2018.02.009 ]

Zhong D T and Zhou F Q . 2018 . A prediction smooth method for blending Landsat and moderate resolution imagine spectroradiometer images . Remote Sensing , 10 ( 9 ): 1371 [ DOI: 10.3390/rs10091371 http://dx.doi.org/10.3390/rs10091371 ]

Zhou D C , Xiao J F , Bonafoni S , Berger C , Deilami K , Zhou Y Y , Frolking S , Yao R , Qiao Z and Sobrino J A . 2019a . Satellite remote sensing of surface urban heat islands: progress, challenges, and perspectives . Remote Sensing , 11 ( 1 ): 48 [ DOI: 10.3390/rs11010048 http://dx.doi.org/10.3390/rs11010048 ]

Zhou F C , Li Z L , Wu H , Duan S B , Song X N and Yan G J . 2019b . A remote sensing method for retrieving land surface emissivity and temperature in cloudy areas: a case study over South China . International Journal of Remote Sensing , 40 ( 5/6 ): 1724 - 1735 [ DOI: 10.1080/01431161.2018.1519288 http://dx.doi.org/10.1080/01431161.2018.1519288 ]

Zhou F Q and Zhong D T . 2020 . Kalman filter method for generating time-series synthetic landsat images and their uncertainty from Landsat and MODIS observations . Remote Sensing of Environment , 239 : 111628 [ DOI: 10.1016/j.rse.2019.111628 http://dx.doi.org/10.1016/j.rse.2019.111628 ]

Zhu X L , Cai F Y , Tian J Q and 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 L , Chen J , Gao F , Chen X H and 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 L , Helmer E H , Gao F , Liu D S , Chen J and 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 and 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 , Clevers J G P W and Schaepman M E . 2008 . Unmixing-based Landsat TM and MERIS FR data fusion . IEEE Geoscience and Remote Sensing Letters , 5 ( 3 ): 453 - 457 [ DOI: 10.1109/lgrs.2008.919685 http://dx.doi.org/10.1109/lgrs.2008.919685 ]

Zurita-Milla R , Kaiser G , Clevers J G P W , Schneider W and Schaepman M E . 2009 . Downscaling time series of MERIS full resolution data to monitor vegetation seasonal dynamics . Remote Sensing of Environment , 113 ( 9 ): 1874 - 1885 [ DOI: 10.1016/j.rse.2009.04.011 http://dx.doi.org/10.1016/j.rse.2009.04.011 ]

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