Abstract
Satellite time series with high spatial resolution is critical for monitoring land surface dynamics in heterogeneous landscapes. Although remote sensing technologies have experienced rapid development in recent years, data acquired from a single satellite sensor are often unable to satisfy our demand. As a result, integrated use of data from different sensors has become increasingly popular in the past decade. Many spatiotemporal data fusion methods have been developed to produce synthesized images with both high spatial and temporal resolutions from two types of satellite images, frequent coarse-resolution images, and sparse fine-resolution images. These methods were designed based on different principles and strategies, and therefore show different strengths and limitations. This diversity brings difficulties for users to choose an appropriate method for their specific applications and data sets. To this end, this review paper investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their potential applications, and proposes possible directions for future studies in this field.
Highlights
Dense time-series data from satellites are highly desired for studying dynamics of our Earth systems
In the early years, the names are quite inconsistent. Developers named their methods in different ways. This can be reflected by the words used in the titles (Figure 3a) and keywords of the 58 methodology papers (Figure 3b). “fusion”, “spatial”, and “temporal” are frequent words used in titles and keywords which exactly reflect the nature of spatiotemporal data fusion, i.e., technology to improve spatial and temporal resolutions of multi-source satellite images
The results indicate that the bias and root mean squared error (RMSE) of ET estimated from fused images are smaller than a satellite-based ET product, especially in maize and vegetable sites [88]
Summary
Dense time-series data from satellites are highly desired for studying dynamics of our Earth systems. 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]. 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). 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. This review investigates literature on current spatiotemporal data fusion methods, categorizes existing methods, discusses the principal laws underlying these methods, summarizes their applications, presents the key issues, and proposes possible directions of future studies on this topic
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