## 1 Introduction  
Dense time-series data from satellites are highly desired for studying dynamics of our Earth systems. For instance, dense time-series data contain the temporal information of objects on the land surface, which are extremely helpful for discriminating different land cover types [1], monitoring vegetation seasonality [2], modeling the carbon sequestration [3], estimating crop yields [4], exploring the human-nature interactions [5], and revealing the ecosystem-climate feedback [6]. Time series-based studies have become extremely popular in this decade because the number of free satellite images available to the public are increasing. For example, all Landsat data have been available at no charge since 2008 and now data from a new satellite, Sentinel-2, are also free of charge. Additionally, a free cloud computing platform, Google Earth Engine (GEE), currently promotes the use of time-series satellite data for large area monitoring of land and water dynamics because it has super ability to process massive satellite images [7]. However, these available satellite images still cannot satisfy our needs for studying high-frequency change in heterogeneous landscapes, such as monitoring the progress of construction work in cities and producing real-time map of urban hazards (e.g., landslides and floods). These studies need satellite images with high frequency as well as high spatial resolution. Due to the tradeoff between scanning swath and pixel size, existing satellites are hard to obtain images with both high temporal resolution (or temporal frequency) and high spatial resolution [8]. Table 1 lists most of the existing multispectral satellite sensors with spatial resolution larger than 10 m. The commercial satellites with very high spatial resolutions, e.g., meter to sub-meter, are not included in Table 1 since they can be costly to composite time-series data. From Table 1, we can see that high-frequency data have coarse spatial resolutions, such as AVHRR, MODIS and SeaWiFS which collects daily images, but their spatial resolutions range from 250 m to 1000 m. On the other hand, the sub 100 m sensors, such as ASTER, Landsat (TM, ETM+, OLI), and Sentinel-2 MSI, have a relatively long revisit cycle, e.g., longer than 10 days. In addition, optical satellite images are contaminated by clouds, cloud shadows, and other atmospheric conditions. These contaminations further hinder the acquisition of dense time series data from sub 100 m sensors, especially in cloudy regions such as tropical and subtropical areas [9]. Although new technologies developed in recent years, such as Unmanned Aerial Vehicles (UAVs), provide a valuable supplement to traditional satellites, scientists still face challenges when studying land surface dynamics of the complex environment over a long period (e.g., several decades) because only satellite images documented the early years. For example, Landsat images are the only data source appropriate for studying any land cover changes in the past 40 years because of their relatively high spatial resolution and long-term archives since 1972. Therefore, how to integrate images from multiple satellites to generate high-quality dense time-series data becomes an urgent task for studies which require observations with high frequency and high spatial resolution. Spatiotemporal data fusion is a feasible solution for the aforementioned problem. Spatiotemporal data fusion is a type of methodology for fusing satellite images from two sensors, sensor one with very high frequency but coarse spatial resolution such as MODIS and AVHRR, and sensor two with very high spatial resolution but lower frequency such as Landsat and Sentinel-2. The output of spatiotemporal data fusion is the synthesized images with temporal frequency of sensor one and spatial resolution of sensor two (Figure 1). It can also integrate two sensors with similar spatial and temporal resolutions to generate consistent observations, such as harmonizing Landsat and Sentinel-2 images [10].