Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures

Xia, Hao; Chen, Yunhao; Liu, Ying; Quan, Jinling

doi:10.1016/j.rse.2019.02.006

Cited by 62 publications

(47 citation statements)

References 50 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Wavelets Support Vector Regression, adaptive network-based fuzzy inference system (ANFIS) and neural networks are the artificial intelligence methods tested in this problem. In [26], a problem of high-spatiotemporal-resolution land surface temperature reconstruction is tackled, by applying a weighted combination kernel-based and fusion methods, in order to improve the spatial and temporal resolutions of satellite images. This method can be classified as a sub-feature level approach.…”

Section: Surface Temperaturementioning

confidence: 99%

Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources

et al. 2020

View full text Add to dashboard Cite

This paper reviews the most important information fusion data-driven algorithms based on Machine Learning (ML) techniques for problems in Earth observation. Nowadays we observe and model the Earth with a wealth of observations, from a plethora of different sensors, measuring states, fluxes, processes and variables, at unprecedented spatial and temporal resolutions. Earth observation is well equipped with remote sensing systems, mounted on satellites and airborne platforms, but it also involves in-situ observations, numerical models and social media data streams, among other data sources. Data-driven approaches, and ML techniques in particular, are the natural choice to extract significant information from this data deluge. This paper produces a thorough review of the latest work on information fusion for Earth observation, with a practical intention, not only focusing on describing the most relevant previous works in the field, but also the most important Earth observation applications where ML information fusion has obtained significant results. We also review some of the most currently used data sets, models and sources for Earth observation problems, describing their importance and how to obtain the data whether needed. Finally, we illustrate the application of ML data fusion with a representative set of case studies, as well as we discuss and outlook the near future of the field.

show abstract

Section: Surface Temperaturementioning

confidence: 99%

Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources

et al. 2020

View full text Add to dashboard Cite

show abstract

“…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].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2020

View full text Add to dashboard Cite

Spatiotemporal fusion is considered a feasible and cost-effective way to solve the trade-off between the spatial and temporal resolution of satellite sensors. Recently proposed learning-based spatiotemporal fusion methods can address the prediction of both phenological and land-cover change. In this paper, we propose a novel deep learning-based spatiotemporal data fusion method that uses a two-stream convolutional neural network. The method combines both forward and backward prediction to generate a target fine image, where temporal change-based and a spatial information-based mapping are simultaneously formed, addressing the prediction of both phenological and land-cover changes with better generalization ability and robustness. Comparative experimental results for the test datasets with phenological and land-cover changes verified the effectiveness of our method. Compared to existing learning-based spatiotemporal fusion methods, our method is more effective in predicting phenological change and directly reconstructing the prediction with complete spatial details without the need for auxiliary modulation.

show abstract

“…Both MODIS and Landsat data have their strengths and limitations. To obtain simultaneously the higher spatiotemporal resolution data, data fusion methods have been used to deal with these spatiotemporal resolution problems by generating the fusion LST data of Landsat and MODIS, satisfying the need of long-term and fine-scale regional thermal environment research [41][42][43][44]. For example, by utilizing the Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Shen et al [45] obtained "Landsat-like" LST datasets from 1988 to 2013 and analyzed the thermal mechanism of the Wuhan city urban heat island.…”

Section: Introductionmentioning

confidence: 99%

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Yang

Zhao

et al. 2020

Remote Sensing

View full text Add to dashboard Cite

Satellite-based remote sensing technologies are utilized extensively to investigate urban thermal environment under rapid urban expansion. Current Moderate Resolution Imaging Spectroradiometer (MODIS) data are, however, unable to adequately represent the spatially detailed information because of its relatively coarser spatial resolution, while Landsat data cannot explore the temporally continued analysis due to the lower temporal resolution. Combining MODIS and Landsat data, “Landsat-like” data were generated by using the Flexible Spatiotemporal Data Fusion method (FSDAF) to measure land surface temperature (LST) variations, and Landsat-like data including Normalized Difference Vegetation Index (NDVI) and Normalized Difference Built Index (NDBI) were generated to analyze LST dynamic driving forces. Results show that (1) the estimated “Landsat-like” data are capable of measuring the LST variations; (2) with the urban expansion from 2013 to 2016, LST increases ranging from 1.80 °C to 3.92 °C were detected in areas where the impervious surface area (ISA) increased, while LST decreases ranging from −3.52 °C to −0.70 °C were detected in areas where ISA decreased; (3) LST has a significant negative correlation with the NDVI and a strong positive correlation with NDBI in summer. Our findings can provide information useful for mitigating undesirable thermal conditions and for long-term urban thermal environmental management.

show abstract

Combining kernel-driven and fusion-based methods to generate daily high-spatial-resolution land surface temperatures

Cited by 62 publications

References 50 publications

Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources

Machine learning information fusion in Earth observation: A comprehensive review of methods, applications and data sources

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

Measuring the Urban Land Surface Temperature Variations Under Zhengzhou City Expansion Using Landsat-Like Data

Contact Info

Product

Resources

About