Spatiotemporal Fusion of Multisource Remote Sensing Data: Literature Survey, Taxonomy, Principles, Applications, and Future Directions

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.

How can Big Data and machine learning benefit environment and water management: a survey of methods, applications, and future directions

“…Fusion of spatial and temporal data has been extensively studied in the EWM literature [161][162][163]. Significant interests now exist in using DL methods to provide automated processing of large sensing datasets.…”

A Novel Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions Using a Two-Stream Convolutional Neural Network

“…Due to its relatively simple implementation, linear spatiotemporal fusion methods have been utilized in various applications, such as land-cover classification [15,16], wetland monitoring [17], land surface temperature monitoring [18,19], leaf area index monitoring [20,21], and evapotranspiration monitoring [22,23]. However, this type of method has some major limitations: (1) linear theoretical assumptions are implausible in the case of land-cover change, resulting in poor fusion performance in land-cover change prediction; and (2) the effectiveness of linear spatiotemporal fusion methods depends on the selection of the weighting function, which is empirical with limited generalization [24].…”

An Enhanced Deep Convolutional Model for Spatiotemporal Image Fusion

“…Remote sensing images with simultaneous high spatial and high temporal resolution play a critical role in land surface dynamics research [1], such as crop and forest monitoring [2,3], and land-use and land-cover changes detection [4]. These applications require dense time-series data to capture ground changes and also fine-spatial-resolution surface details, such as textures and structures of ground objects, to perform accurate classification and identification or some advanced quantitative calculation.…”

“…Fortunately, this problem that it is not easy to directly acquire high spatiotemporal resolution images from existing satellite observation systems can be partly alleviated by some data post-processing processes [7][8][9]. Among them, spatiotemporal remote sensing image fusion is a class of techniques used to synthesize dense-time images with high spatial resolution from at least two different data sources [1,5,10]. In most cases, one is the coarse-spatial-resolution image with high temporal but low spatial resolution (HTLS), while the other is the fine-spatial-resolution image with low temporal but high spatial resolution (LTHS).…”