## 1 Introduction  
Coarse-resolution satellite data obtained, for example from the Advanced Very High Resolution Radiometer (AVHRR) [1], Systeme Pour l'Observation de la Terre (SPOT) Vegetation (VGT) [2], and from the Moderate Resolution Imaging Spectroradiometer (MODIS) [3], are widely used in areas such as land cover and land use mapping [4, 5], crop mapping and yield forecasting [6, 7], global change [8], vegetation trends and phenology estimations [9, 10], disaster monitoring [11, 12, 13, 14] and atmospheric environment [15, 16, 17] and water environment monitoring [18]. The return cycle of these satellites is one to two days, making them suitable for dynamic monitoring of land surface processes, particularly AVHRR data, which provides the longest time series among global satellite measurements [19]. However, the spatial resolutions of these data are lower than 250 m. When the size of land objects is smaller than the spatial resolution of images acquired by these sensors, the recorded signals are often a mixture of different land cover types. This makes these data difficult to apply to high spatial resolution surface process monitoring. Medium spatial resolution satellite data, such as data from Landsat and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), can also be used for dynamic monitoring of land surface processes. However, because of the long return cycles of these satellites (>16 days) and the influence of clouds, the rate at which these satellites can obtain useful data is very low [20], and it is difficult to use their data to monitor rapid changes in land surface processes. Therefore, these satellite data are often used only for annual dynamic analyses, including the spatiotemporal dynamic analysis of ecosystems such as wetlands [21, 22], forests [23, 24, 25], water [26], crops [27] and cities [28], for monitoring plant phenology [29] and for land management [30, 31]. There is a lack of satellite data with high enough spatial and temporal resolutions to monitor rapid changes in land surface processes. A solution to this problem is to combine coarse and medium spatial resolution satellite data to generate synthetic satellite data with high spatial and temporal resolutions. This method is called spatial and temporal data fusion technology and several such approaches have recently been proposed. [PERSON] _et al._[32] introduced the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) for blending MODIS and Landsat imagery. Several studies have applied STARFM, mainly in coniferous areas for urban environmental variable extraction, vegetated dry-land ecosystem monitoring, public health studies and daily land surface temperature generation [33, 34, 35, 36, 37]. [PERSON] _et al._[38] enhanced STARFM for complex heterogeneous regions. [PERSON] _et al._[39] assessed the accuracy of STARFM and ESTARFM (Enhanced STARFM) for two landscapes with contrasting spatial and temporal dynamics. [PERSON] _et al._[40] evaluated the accuracy of STARFM and ESTARFM to downscale MODIS indices to match the spatial resolution of Landsat. Scholars have also proposed methods based on linear mixed models [41, 42, 43]. [PERSON] _et al._[44] proposed a Spatial and Temporal Data Fusion Approach (STDFA) to calculate the real surface reflectance of fine-resolution pixels from the mean reflectance of each land cover class, disaggregated using unmixing methods. They also applied this method to the estimation of high spatial and temporal resolution Leaf Area Index [45] and land surface temperature [46] data. [PERSON] and [PERSON] [47] compared STARFM and an unmixing-based algorithm. STARFM and ESTARFM are more suitable for complex heterogeneous regions, while unmixing methods such as STDFA are more suitable for cases that downscale the spectral characteristics of medium-resolution input imagery [47]. The proposed spatial and temporal data fusion approaches mainly focus on the fusion of Landsat and MODIS data.