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Remote Sensing for Land & Resources    2018, Vol. 30 Issue (2) : 1-11     DOI: 10.6046/gtzyyg.2018.02.01
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Review of spatiotemporal fusion model of remote sensing data
Wenquan DONG1,2(), Jihua MENG1()
1. Key Laboratory of Digital Earth Science, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101,China
2. University of Chinese Academy of Sciences, Beijing 100049, China
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Abstract  

Taking the interaction between spatial and temporal resolution of remote sensing data into consideration, the authors hold that there is no satellite sensor that can produce images with both high spatial and temporal resolution, and spatiotemporal fusion of remote sensing data is an effective method to solve this problem. This paper introduces main research achievements of spatiotemporal fusion model obtained both in China and abroad. Based on the comparative analysis of the mainstream fusion models, these models can be divided into two categories, i.e., the transformation-based model and the pixel-reconstruction-based model. Furthermore, the authors divide the pixel-reconstruction-based model into mixed linear model and spatial and temporal adaptive reflectance model, and then introduce the basic principles, methods of these models. This paper makes a comparative analysis of the advantages and disadvantages of various aspects of the model. At last, the data, application and scale prospect of spatiotemporal fusion models are put forward.
Keywords remote sensing data      spatiotemporal fusion      model      comparison      prospect     
:  TP751.1  
Corresponding Authors: Jihua MENG     E-mail: dongwq@radi.ac.cn;mengjh@radi.ac.cn
Issue Date: 30 May 2018
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Cite this article:   
Wenquan DONG,Jihua MENG. Review of spatiotemporal fusion model of remote sensing data[J]. Remote Sensing for Land & Resources, 2018, 30(2): 1-11.
URL:  
https://www.gtzyyg.com/EN/10.6046/gtzyyg.2018.02.01     OR     https://www.gtzyyg.com/EN/Y2018/V30/I2/1
算法
类型		 二级分类 名称 参考文献 至少所需中
高空间分辨率
数据个数/期		 实验所
用数据		 适用尺度 异质性较强
区域适用性		 算法特点
基
于
变
换
的
模
型		 基于
小波
变换
的模
型		 小波变换 顾晓鹤等[9] 1 MODIS归一化植
被指数(normalized
difference vegetation
index,NDVI),
TM NDVI		 中、
大尺度		 较差 所用MODIS NDVI数据为16 d产品,物候差异特征不够明显; 融合数据存在混合像元问题
小波变换 Acerbi-Junior等[10] 1 MODIS,
TM		 中、
大尺度		 较差 有效地提高了MODIS数据的空间分辨率,为最小失真情况下提高源图像的空间分辨率提供了一个概念框架
小波变换 Wu等[11] 1 MODIS,
TM		 中、
大尺度		 较差 评价了小波变换在时空融合中的潜力,研究发现选择合适的小波函数和融合方法是小波变换的关键
基于主
成分分析
的模型		 主成分
分析		 Shevyrnogov等[12] 1 NOAA NDVI,
MSS		 中、
大尺度		 较差 通过融合MSS亮度分量和NOAA NDVI数据得到高时空分辨率NDVI数据
基
于
像
元
重
构
的
模
型		 基于
线性
混合
模型		 线性回归
和决策树		 Hansen等[13] 1 MODIS,
ETM+		 尤其
大尺度		 差 适用于地物单一且反射率呈线性变换的区域,大大减少时空融合所需时间,算法可移植
线性回归 Zhukov等[14] 1 AVHRR,
TM		 中、
大尺度		 差 考虑了像元反射率空间可变性的问题,引入窗口技术,为后续研究所采用
线性回归 Maselli[15] 1 AVHRR NDVI,
TM NDVI		 中、
大尺度		 较差 提出了距离权重的概念,即认为距离目标像元越近,对目标像元的影响越大
线性回归 Busetto等[16] 1 MODIS,
TM		 中、
大尺度		 较好 提出了光谱权重的概念,主要解决线性混合模型解算过程中像元反射率的空间可变性问题
基于
时空
自适
应融
合模
型		 时空自适应
性反射率
融合模型		 Gao等[17] 1 MODIS,
ETM+		 中、
小尺度		 较好 不仅考虑与目标像元的空间距离和光谱相似性,还考虑了时间上的差异,并且利用邻近光谱相似像元计算中心像元,大大提高了结果精度
针对反射率变
化的时空自适
应融合模型		 Hilker等[18] 2 MODIS,
TM,ETM+		 中、
小尺度		 好 能够捕获比较短暂的地表变化
改进型时空自
适应融合模型		 Zhu等[19] 2 MODIS,
TM		 中、
小尺度		 好 根据空间和光谱相似性来估计中心像元,适用于异质性较强的非植被覆盖地区
不同时空分辨
率NDVI的时
空融合模型		 蒙继华等[20,21] 1 MODIS,
TM,HJ-1 CCD		 中、
小尺度		 好 考虑了物候的影响,直接将算法用于植被指数
Tab.1  Summary of spatiotemporal fusion models
Fig.1  Spatiotemporal fusion of multi-source remote sensing data
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