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纸质出版日期： 2019-9 ，
录用日期： 2018-8-17
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杨军明, 吴昱, 魏永霞, 王斌, 汝晨, 马瑛瑛, 张奕. 2019. 多源数据融合的高时空分辨率植被指数生成. 遥感学报, 23(5): 935–943
Yang J M, Wu Y, Wei Y X, Wang B, Ru C, Ma Y Y and Zhang Y. 2019. A model for the fusion of multi-source data to generate high temporal and spatial resolution VI data. Journal of Remote Sensing, 23(5): 935–943
高时空分辨率的植被指数VI(Vegetation Index)数据是农业和生态研究的重要基础数据集，目前常用的VI数据的时空分辨率存在不可调和矛盾。考虑VI时序变化对数据融合的影响，提出一种新的VI数据时空融合模型VISTFM(Vegetation Index Spatial and Temporal Fusion Model)，VISTFM采用模糊C聚类算法，对存量时序VI数据按土地利用类型划分为若干子类，从高低分辨率影像中随土地覆被类的变化规律提取子类，结合低分辨率影像提取的土地覆被类变化规律融合生成高时空分辨率的VI数据。用常用的Landsat和MODIS数据验证该算法，测试表明，VISTFM能够较好的捕获VI的中间变化过程，与常用的基于线性混合模型的模型和时空自适应反射率融合模型及其改进模型相比，利用VISTFM获得的植被指数数据集具有更高的时空分辨率。
Vegetation Index (VI) data with high spatial and temporal resolution are highly important in the use of remote sensing technology to observe the earth. Owing to technological limitations
obtaining VI data that exhibits both high spatial and temporal resolution is impossible. The arrival of multi-source remote sensing data spatial and temporal fusion model enables the retrieval of data with high spatial and temporal resolution. The commonly used model does not have the ability to capture the intermediate change process of pixel value
and a certain regularity occurs in the change of the VI of the landmark. This study proposes a new multi-source data fusion model (FCMVISTFM) based on Fuzzy C-Mean algorithm (FCM). Making pixels that group together have similar VI values and law of VI changes throughout the period. FCMVISTFM uses FCM to divide land-cover types into certain categories based on multi-phase VI data
which are defined as subclasses of each land-cover class. Each land-cover class average value is calculated by using the linear unmixed model
and subclass average value is calculated by the law between land-cover class and subclass. The VI data are fused by Landsat8 OLI and MODIS data based on the assumption that the average VI value of subclass S is the same as the VI value of pixels that belong to subclass S. Results show that FCMVISTFM can achieve relatively high accuracy. The average values of correlation coefficient (
R
)
RMSE
ERGAS
and variance are 0.9057
0.0674
1.9795
and 0.0045
respectively. With this level of accuracy
VI data can be used for vegetation research and observations of the earth. Commonly used line unmixed models
spatial and temporal adaptive reflectance fusion model (STARFM)
and its improved models have the problem of uncertain ability to capture the intermediate change process of VI. Thus
FCMVISTFM is more accurate compared with STDFA and ESTARFM. FCMVISTFM is developed for obtaining high spatial and temporal resolution VI data
making it easier to capture the intermediate changes of VI
which can be applied where high spatial and temporal resolution VI data are needed. In this study
the accuracy of the multi-source data fusion model can be increased by the following aspects. (1) The models based on line unmixed model
regardless of pixel classes or subset S average value calculations
are based on the entire image. However
in the STARFM model and improved models based on STARFM
the data fusion based on high and low resolutions pixels in a certain window
cloud cover only affects the calculation of pixels near its coverage area. The acquisition of multiphase cloudless images is especially difficult when the study area is large. In this case
the STARFM model and improved models based on STARFM have more application advantages. (2) All of the multi-source remote sensing data fusion models are based on certain assumptions
though these assumptions are based on a certain theoretical and have certain rationality. Errors are mainly caused by assumptions. A complete model assumption is proposed as the main way to improve the accuracy of the multi-source remote sensing data fusion model. (3) The time sequence laws of the VI of various landmark are not disordered
but a certain regularity
such as the specific laws of the crop’s VI
occurs. If the multi-source remote sensing data fusion model is established based on these laws
then it can also improve the accuracy of fusion results to some extent.
遥感植被指数数据融合时空分辨率模糊C聚类算法线性混合模型
remote sensingVegetation Index (VI)data fusiontemporal and spatial resolutionfuzzy c-means algorithmlinear unmixed model
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