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

Yang, Haibo; Xi, Chaofan; Zhao, Xincan; Mao, Penglei; Wang, Zongmin; Shi, Yong; He, Tao; Li, Zhenhong

doi:10.3390/rs12050801

Cited by 35 publications

(18 citation statements)

References 76 publications

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“…Lin et al found that the area of BYD lake in flood season can reach 290 km 2[66], and it is difficult to achieve remote sensing inversion with a large range, a high frequency, and a high spatial resolution. Yang et al used STF for the inversion of surface temperature in Zhengzhou City[54], and we applied STF to the inversion of Chl-a with the same good results. A similar study was conducted by Liu et al who used Sentinel-2, Landsat-8, and Modis for spatio-temporal fusion and SPM (suspended particulate matter) inversion[67].…”

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confidence: 84%

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Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

Yang

Zhao

et al. 2022

Remote Sensing

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The combination of remote sensing technology and traditional field sampling provides a convenient way to monitor inland water. However, limited by the resolution of remote sensing images and cloud contamination, the current water quality inversion products do not provide both high temporal resolution and high spatial resolution. By using the spatio-temporal fusion (STF) method, high spatial resolution and temporal fusion images were generated with Landsat, Sentinel-2, and GaoFen-2 data. Then, a Chl-a inversion model was designed based on a convolutional neural network (CNN) with the structure of 4-(136-236-340)-1-1. Finally, the results of the Chl-a concentrations were corrected using a pixel correction algorithm. The images generated from STF can maintain the spectral characteristics of the low-resolution images with the R2 between 0.7 and 0.9. The Chl-a inversion results based on the spatio-temporal fused images and CNN were verified with measured data (R2 = 0.803), and then the results were improved (R2 = 0.879) after further combining them with the pixel correction algorithm. The correlation R2 between the Chl-a results of GF2-like and Sentinel-2 were both greater than 0.8. The differences in the spatial distribution of Chl-a concentrations in the BYD lake gradually increased from July to August. Remote sensing water quality inversion based on STF and CNN can effectively achieve high frequency in time and fine resolution in space, which provide a stronger scientific basis for rapid diagnosis of eutrophication in inland lakes.

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confidence: 84%

“…Finally, the GF2-like image at time T2 is fused and generated. The calculation process for step 5 is as follows [54]:…”

Section: Spatial and Temporal Fusion Methodsmentioning

confidence: 99%

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Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

Yang

Zhao

et al. 2022

Remote Sensing

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“…Note that although revisions of FSDAF, i.e., IFSDAF, SFSDAF, and FSDAF 2.0 have been proposed, the framework of these hybrid STF methods is basically consistent with the original FSDAF. Moreover, FSDAF has been widely used [63][64][65][66], suggesting its effectiveness and representativeness. We thus used FSDAF as the representative method of the hybrid STF methods.…”

Section: Comparison and Evaluation Strategymentioning

confidence: 99%

A Hybrid Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions

et al. 2021

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Spatiotemporal fusion (STF) is considered a feasible and cost-effective way to deal with the trade-off between the spatial and temporal resolution of satellite sensors, and to generate satellite images with high spatial and high temporal resolutions. This is achieved by fusing two types of satellite images, i.e., images with fine temporal but rough spatial resolution, and images with fine spatial but rough temporal resolution. Numerous STF methods have been proposed, however, it is still a challenge to predict both abrupt landcover change, and phenological change, accurately. Meanwhile, robustness to radiation differences between multi-source satellite images is crucial for the effective application of STF methods. Aiming to solve the abovementioned problems, in this paper we propose a hybrid deep learning-based STF method (HDLSFM). The method formulates a hybrid framework for robust fusion with phenological and landcover change information with minimal input requirements, and in which a nonlinear deep learning-based relative radiometric normalization, a deep learning-based superresolution, and a linear-based fusion are combined to address radiation differences between different types of satellite images, landcover, and phenological change prediction. Four comparative experiments using three popular STF methods, i.e., spatial and temporal adaptive reflectance fusion model (STARFM), flexible spatiotemporal data fusion (FSDAF), and Fit-FC, as benchmarks demonstrated the effectiveness of the HDLSFM in predicting phenological and landcover change. Meanwhile, HDLSFM is robust for radiation differences between different types of satellite images and the time interval between the prediction and base dates, which ensures its effectiveness in the generation of fused time-series data.

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“…FSDAF is suitable for heterogeneous landscapes and can effectively capture land cover changes. In recent years, FSDAF has been used in a number of applications, such as monitoring dynamics of impervious surface [38], wetland [39], land surface temperature [40], and vegetation [41]. Moreover, FSDAF provides a framework for addressing both gradual and abrupt land-cover changes during the spatiotemporal fusion process.…”

Section: Introductionmentioning

confidence: 99%

cuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units

Gao

Zhu

Guan

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Spatiotemporal data fusion is a cost-effective way to produce remote sensing images with high spatial and temporal resolutions using multi-source images. Using spectral unmixing analysis and spatial interpolation, the Flexible Spatiotemporal DAta Fusion (FSDAF) algorithm is suitable for heterogeneous landscapes and capable of capturing abrupt land-cover changes. However, the extensive computational complexity of FSDAF prevents its use in large-scale applications and mass production. Besides, the domain decomposition strategy of FSDAF causes accuracy loss at the edges of sub-domains due to the insufficient consideration of edge effects. In this study, an enhanced FSDAF (cuFSDAF) is proposed to address these problems, and includes three main improvements: (1) The TPS interpolator is replaced by an accelerated inverse distance weighted interpolator to reduce computational complexity. (2) The algorithm is parallelized based on the Compute Unified Device Architecture (CUDA), a widely used parallel computing framework for graphics processing units (GPUs). (3) An adaptive domain decomposition method is proposed to improve the fusion accuracy at the edges of subdomains, and to enable GPUs with varying computing capacities to deal with datasets of any size. Experiments showed while obtaining similar accuracies to FSDAF and an up-to-date deeplearning-based method, cuFSDAF reduced the computing time significantly and achieved speed-ups of 140.3-182.2 over the original FSDAF program. cuFSDAF is capable of efficiently producing fused images with both high spatial and temporal resolutions to support applications for large-scale and long-term land surface dynamics. Source code and test data available at https://github.com/HPSCIL/cuFSDAF.

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

Cited by 35 publications

References 76 publications

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

Water Quality Chl-a Inversion Based on Spatio-Temporal Fusion and Convolutional Neural Network

A Hybrid Deep Learning-Based Spatiotemporal Fusion Method for Combining Satellite Images with Different Resolutions

cuFSDAF: An Enhanced Flexible Spatiotemporal Data Fusion Algorithm Parallelized Using Graphics Processing Units

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