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Scale Issues in Remote Sensing: Analysis, Processing and Modeling

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing Image Processing".

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 8705

Special Issue Editors


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Guest Editor
Key Laboratory of Agri-Informatics, Ministry of Agriculture/Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: thermal infrared remote sensing; land surface temperature; land surface emissivity; evapotranspiration; scaling problem; hyperspectral analysis; radiative transfer modelling
Special Issues, Collections and Topics in MDPI journals
Institute of Geographic Sciences and Natural Resources Research, CAS, Beijing, China
Interests: thermal infrared remote sensing; scaling and validation of remote sensed products; retrieval of hydrothermal parameters from remote sensing data
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advance of remote sensing technology, which can quickly access a wide range of real-time land surface spatial information, has provided a powerful means to conduct regional and global measurements for resource surveys, environmental monitoring, and disaster prediction. However, the inconsistency between the spatial resolution of remotely sensed data and that required for remote sensing applications greatly limits the development of the latter. The scale represents the window of perception and the ability of observation and reflects the limitation of knowledge through which a phenomenon may be viewed or perceived. When only certain spatial scales are considered, this can inevitably lead to a geographical misunderstanding of the current status from in situ measurement data on the point, site, or regional scale.

Scale research is of great importance in remote sensing. Generally, most retrieval methods and algorithms are mainly derived at a small scale and assume that the land surface at this scale is homogeneous. However, when these methods and algorithms are used directly on remotely sensed data with different spatial resolutions, there may be inconsistencies among the corresponding products because of the heterogeneity of the land surface. These inconsistencies, which are also regarded as scaling effects, may have great impact on the quality of the products and make the products unreliable. For decades, scientists have been dedicated to answering two important questions about the scale effects and scaling in the quantitative remote sensing: 1) Does the basic laws of physics at the pixel scale still apply to other scales, and if not, how to correct them? 2) What are the laws and connections of geography target features on different resolution scales, and how to upscale and downscale these features?

In fact, there is no generalized scaling method considering both the heterogeneity of land surface and the nonlinearity of the retrieval methods. Research concerning scale is still in its initial stage. With the rapid development of remote sensing technology, this Special Issue focuses on Scale Issues in Remote Sensing: Analysis, Processing, and Modeling. Potential topics include, but are not limited to, the following:

  •  Scale effects of remotely sensed products
  • Upscaling and downscaling modelling approaches
  • Fusion of multi-temporal, multi-angle, and multi-sensor remotely sensed data
  • Strategies of multi-scale image processing
  • Validation of multi-scale quantitative remote sensing products by in situ measurements
  • Evaluation of uncertainty and applicability in upscaled or downscaled products
  • Comparison of scale drivers in different domain
  • Identification of relevant spatial scales and thresholds for remote sensing models

Dr. Zhao-Liang Li
Dr. Hua Wu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Scale effects
  • Scaling
  • Fusion
  • Upscaling and downscaling modelling
  • Multi-scale Validation
  • Uncertainty of Scaling

Published Papers (2 papers)

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Research

19 pages, 9838 KiB  
Article
Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product
by Shuting Zhang, Si-Bo Duan, Zhao-Liang Li, Cheng Huang, Hua Wu, Xiao-Jing Han, Pei Leng and Maofang Gao
Remote Sens. 2019, 11(24), 3025; https://doi.org/10.3390/rs11243025 - 15 Dec 2019
Cited by 17 | Viewed by 2966
Abstract
Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The [...] Read more.
Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0–6.5 g/cm2 were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces. Full article
(This article belongs to the Special Issue Scale Issues in Remote Sensing: Analysis, Processing and Modeling)
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21 pages, 3288 KiB  
Article
An Efficient Approach to Remove Thick Cloud in VNIR Bands of Multi-Temporal Remote Sensing Images
by Wenhui Du, Zhihao Qin, Jinlong Fan, Maofang Gao, Fei Wang and Bilawal Abbasi
Remote Sens. 2019, 11(11), 1284; https://doi.org/10.3390/rs11111284 - 29 May 2019
Cited by 17 | Viewed by 5157
Abstract
Cloud-free remote sensing images are required for many applications, such as land cover classification, land surface temperature retrieval and agricultural-drought monitoring. Cloud cover in remote sensing images can be pervasive, dynamic and often unavoidable. Current techniques of cloud removal for the VNIR (visible [...] Read more.
Cloud-free remote sensing images are required for many applications, such as land cover classification, land surface temperature retrieval and agricultural-drought monitoring. Cloud cover in remote sensing images can be pervasive, dynamic and often unavoidable. Current techniques of cloud removal for the VNIR (visible and near-infrared) bands still encounters the problem of pixel values estimated for the cloudy area incomparable and inconsistent with the cloud-free region in the target image. In this paper, we proposed an efficient approach to remove thick clouds and their shadows in VNIR bands using multi-temporal images with good maintenance of DN (digital number) value consistency. We constructed the spectral similarity between the target image and reference one for DN value estimation of the cloudy pixels. The information reconstruction was done with 10 neighboring cloud-free pair-pixels with the highest similarity over a small window centering the cloudy pixel between target and reference images. Four Landsat5 TM images around Nanjing city of Jiangsu Province in Eastern China were used to validate the approach over four representative surface patterns (mountain, plain, water and city) for diverse sizes of cloud cover. Comparison with the conventional approaches indicates high accuracy of the approach in cloud removal for the VNIR bands. The approach was applied to the Landsat8 OLI (Operational Land Imager) image on 29 April 2016 in Nanjing area using two reference images. Very good consistency was achieved in the resulted images, which confirms that the proposed approach could be served as an alternative for cloud removal in the VNIR bands using multi-temporal images. Full article
(This article belongs to the Special Issue Scale Issues in Remote Sensing: Analysis, Processing and Modeling)
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