Special Issue "Remote Sensing Image Downscaling"

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 (30 July 2018).

Special Issue Editor

Prof. Peter M. Atkinson
Website
Guest Editor
Lancaster Environment Centre, Lancaster University, UK
Interests: Remote sensing; Geostatistics; environmental modelling; Spatial and space-time sampling effects; Disease transmission systems; Global vegetation and land cover changes; Natural hazard impacts and risks
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Special Issue Information

Dear Colleagues,

In the early 1970s, one of the first applications of remote sensing was to determine “what is there”, that is, to classify the cover of the land. In the 1980s, some researchers realised that the pixel is a problematic concept in relation to land cover, because commonly a pixel covers more than one class. This led to the so-called “mixed pixel” approaches that estimated the proportion of each land cover class in each pixel instead of allocating each pixel to one class only. However, in the 1990s, it was realised that the allocation of proportions is only a partial solution (and a frustrating point at which to stop) because in reality those land cover proportions represent hard classes which have a spatial position within the pixel. Moreover, where the number of classes is large, it is very difficult to visualise such proportion maps, that is, to compress many class proportions into a single visual map. This led to methods for image downscaling (also termed super-resolution mapping and sub-pixel mapping) which produce a single thematic class map at a finer spatial resolution than the original data. Over the last two decades, many advances have been made. At the same time, the goal of downscaling was extended to continua, that is, increasing the spatial resolution of images of reflectance using change-of-support geostatistics and related techniques.

The advent of long historical time-series of remotely sensed images from sensors, such as AVHRR, MODIS and MERIS, has meant that the focus of remote sensing image downscaling has shifted from handling one-time image sets to extensive time-series of images. The requirement for downscaling solutions for time-series of images has been amplified by the Copernicus programme and especially the Sentinel series of satellites. The objective is to find the most suitable method of utilizing the available temporal information (e.g., temporal covariance structure) in such time-series and integrate this with the available spatial information and covariate information. This is problematic because the time-series of a given pixel, or set of pixels, may involve abrupt land cover changes, which requires a non-stationary model.

This Special Issue aims to showcase a wide range of new developments in remote sensing image downscaling. The scope includes both image downscaling for land cover classification and downscaling of continua. Contributions which provide new downscaling solutions for extensive time-series of remotely sensed images are particularly encouraged. Contributions may focus on, but are not limited to:

  1. New methods for the creation of downscaled image time-series and fine resolution change detection;
  2. New (e.g., Bayesian, machine learning, and computationally efficient) approaches to spatial downscaling;
  3. Image downscaling in operational applications (e.g., agricultural monitoring and crop yield forecasting, deforestation, urbanization, vegetation phenology, spatial epidemiology);
  4. Assessment of the information content and uncertainty in downscaled products.

Prof. Peter M. Atkinson
Guest Editor

Manuscript Submission Information

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Keywords

  • image downscaling
  • super-resolution mapping
  • sub-pixel mapping
  • change-of-support
  • spatial resolution
  • land cover classification
  • geostatistics
  • multiple-point statistics
  • change detection
  • time-series

Published Papers (10 papers)

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Research

Open AccessArticle
“Regression-then-Fusion” or “Fusion-then-Regression”? A Theoretical Analysis for Generating High Spatiotemporal Resolution Land Surface Temperatures
Remote Sens. 2018, 10(9), 1382; https://doi.org/10.3390/rs10091382 - 30 Aug 2018
Cited by 3
Abstract
The trade-off between spatial and temporal resolutions in satellite sensors has inspired the development of numerous thermal sharpening methods. Specifically, regression and spatiotemporal fusion are the two main strategies used to generate high-resolution land surface temperatures (LSTs). The regression method statically downscales coarse-resolution [...] Read more.
The trade-off between spatial and temporal resolutions in satellite sensors has inspired the development of numerous thermal sharpening methods. Specifically, regression and spatiotemporal fusion are the two main strategies used to generate high-resolution land surface temperatures (LSTs). The regression method statically downscales coarse-resolution LSTs, whereas the spatiotemporal fusion method can dynamically downscale LSTs; however, the resolution of downscaled LSTs is limited by the availability of the fine-resolution LSTs. Few studies have combined these two methods to generate high spatiotemporal resolution LSTs. This study proposes two strategies for combining regression and fusion methods to generate high spatiotemporal resolution LSTs, namely, the “regression-then-fusion” (R-F) and “fusion-then-regression” (F-R) methods, and discusses the criteria used to determine which strategy is better. The R-F and F-R have several advantages: (1) they fully exploit the information in the available data on the visible and near infrared (VNIR) and thermal infrared (TIR) bands; (2) they downscale the LST time series to a finer resolution corresponding to that of VNIR data; and (3) they inherit high spatial reconstructions from the regression method and dynamic temporal reconveyance from the fusion method. The R-F and F-R were tested with different start times and target times using Landsat 8 and Advanced Spaceborne Thermal Emission and Reflection Radiometer data. The results showed that the R-F performed better than the F-R when the regression error at the start time was smaller than that at the target time, and vice versa. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle
Spatio-Temporal Super-Resolution Land Cover Mapping Based on Fuzzy C-Means Clustering
Remote Sens. 2018, 10(8), 1212; https://doi.org/10.3390/rs10081212 - 02 Aug 2018
Cited by 3
Abstract
Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often [...] Read more.
Super-resolution land cover mapping (SRM) is a method that aims to generate land cover maps with fine spatial resolutions from the original coarse spatial resolution remotely sensed image. The accuracy of the resultant land cover map produced by existing SRM methods is often limited by the errors of fraction images and the uncertainty of spatial pattern models. To address these limitations in this study, we proposed a fuzzy c-means clustering (FCM)-based spatio-temporal SRM (FCM_STSRM) model that combines the spectral, spatial, and temporal information into a single objective function. The spectral term is constructed with the FCM criterion, the spatial term is constructed with the maximal spatial dependence principle, and the temporal term is characterized by the land cover transition probabilities in the bitemporal land cover maps. The performance of the proposed FCM_STSRM method is assessed using data simulated from the National Land Cover Database dataset and real Landsat images. Results of the two experiments show that the proposed FCM_STSRM method can decrease the influence of fraction errors by directly using the original images as the input and the spatial pattern uncertainty by inheriting land cover information from the existing fine resolution land cover map. Compared with the hard classification and FCM_SRM method applied to mono-temporal images, the proposed FCM_STSRM method produced fine resolution land cover maps with high accuracy, thus showing the efficiency and potential of the novel approach for producing fine spatial resolution maps from coarse resolution remotely sensed images. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle
A Conservative Downscaling of Satellite-Detected Chemical Compositions: NO2 Column Densities of OMI, GOME-2, and CMAQ
Remote Sens. 2018, 10(7), 1001; https://doi.org/10.3390/rs10071001 - 23 Jun 2018
Cited by 6
Abstract
A conservative downscaling technique was applied when comparing nitrogen dioxide (NO2) column densities from space-borne observations and a fine-scale regional model. The conservative downscaling was designed to enhance the spatial resolution of satellite measurements by applying the fine-scale spatial structure from [...] Read more.
A conservative downscaling technique was applied when comparing nitrogen dioxide (NO2) column densities from space-borne observations and a fine-scale regional model. The conservative downscaling was designed to enhance the spatial resolution of satellite measurements by applying the fine-scale spatial structure from the model, with strict mass conservation at each satellite footprint pixel level. With the downscaling approach, NO2 column densities from the Ozone Monitoring Instrument (OMI; 13 × 24 km nadir footprint resolution) and the Global Ozone Monitoring Experiment-2 (GOME-2; 40 × 80 km) show excellent agreement with the Community Multiscale Air Quality (CMAQ; 4 × 4 km) NO2 column densities, with R = 0.96 for OMI and R = 0.97 for GOME-2. We further introduce an approach to reconstruct surface NO2 concentrations by combining satellite column densities and simulated surface-to-column ratios from the model. Compared with the Environmental Protection Agency’s (EPA) Air Quality System (AQS) surface observations, the reconstructed surface concentrations show a good agreement; R = 0.86 for both OMI and GOME-2. This study demonstrates that the conservative downscaling approach is a useful tool to compare coarse-scale satellites with fine-scale models or observations in urban areas for air quality and emissions studies. The reconstructed fine-scale surface concentration field could be used for future epidemiology and urbanization studies. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle
Utilizing Pansharpening Technique to Produce Sub-Pixel Resolution Thematic Map from Coarse Remote Sensing Image
Remote Sens. 2018, 10(6), 884; https://doi.org/10.3390/rs10060884 - 06 Jun 2018
Cited by 14
Abstract
Super-resolution mapping (SRM) is a technique to obtain sub-pixel resolution thematic map (SRTM). Soft-then-hard SRM (STHSRM) is an important SRM algorithm due to its simple physical meaning. The soft classification errors may affect the SRTM derived by STHSRM. To overcome this problem, the [...] Read more.
Super-resolution mapping (SRM) is a technique to obtain sub-pixel resolution thematic map (SRTM). Soft-then-hard SRM (STHSRM) is an important SRM algorithm due to its simple physical meaning. The soft classification errors may affect the SRTM derived by STHSRM. To overcome this problem, the maximum a posteriori probability (MAP) super-resolution then hard classification (MTC) algorithm has been proposed. However, the prior information of the original image is difficult to utilize in MTC. To solve this issue, a novel method based on pansharpening then hard classification (PTC) is proposed to improve SRTM. The pansharpening technique is applied to the original coarse image to obtain the improved resolution image by suppling more prior information. The SRTM is then derived from the improved resolution image by hard classification. Not only does PTC inherit the advantages of MTC that avoids soft classification errors, but it can also incorporate more prior information from the original image into the process. Experiments based on real remote sensing images show that the proposed method can produce higher mapping accuracy than the STHSRM and MTC. It is shown that the PTC has the percentage correctly classified (PCC) in the range from 89.62% to 95.92% for the experimental dataset. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle
A Geostatistical Approach for Modeling Soybean Crop Area and Yield Based on Census and Remote Sensing Data
Remote Sens. 2018, 10(5), 680; https://doi.org/10.3390/rs10050680 - 27 Apr 2018
Cited by 2
Abstract
Advances in satellite imagery and remote sensing have enabled the acquisition of spatial data at several different resolutions. Geographic information systems (GIS) and geostatistics can be used to link geographic data from different sources. This article discusses the need to improve soybean crop [...] Read more.
Advances in satellite imagery and remote sensing have enabled the acquisition of spatial data at several different resolutions. Geographic information systems (GIS) and geostatistics can be used to link geographic data from different sources. This article discusses the need to improve soybean crop detection and yield prediction by linking census data, GIS, remote sensing, and geostatistics. The proposed approach combines Brazilian Institute of Geography and Statistics (IBGE) census data with an eight-day enhanced vegetation index (EVI) time series derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data to monitor soybean areas and yields in Mato Grosso State, Brazil. In situ data from farms were used to validate the obtained results. Binomial areal kriging was used to generate maps of soybean occurrence over the years, and Gaussian areal kriging was used to predict soybean crop yield census data inside detected soybean areas, which had a downscaling effect on the results. The global accuracy and the Kappa index for the soybean crop detection were 92.1% and 0.84%, respectively. The yield prediction presented 95.09% accuracy considering the standard deviation and probable error. Soybean crop detection and yield monitoring can be improved by this approach. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle
Geographically Weighted Area-to-Point Regression Kriging for Spatial Downscaling in Remote Sensing
Remote Sens. 2018, 10(4), 579; https://doi.org/10.3390/rs10040579 - 09 Apr 2018
Cited by 7
Abstract
Spatial downscaling of remotely sensed products is one of the main ways to obtain earth observations at fine resolution. Area-to-point (ATP) geostatistical techniques, in which regular fine grids of remote sensing products are regarded as points, have been applied widely for spatial downscaling. [...] Read more.
Spatial downscaling of remotely sensed products is one of the main ways to obtain earth observations at fine resolution. Area-to-point (ATP) geostatistical techniques, in which regular fine grids of remote sensing products are regarded as points, have been applied widely for spatial downscaling. In spatial downscaling, it is common to use auxiliary information to explain some of the unknown spatial variation of the target geographic variable. Because of the ubiquitously spatial heterogeneities, the observed variables always exhibit uncontrolled variance. To overcome problems caused by local heterogeneity that cannot meet the stationarity requirement in ATP regression kriging, this paper proposes a hybrid spatial statistical method which incorporates geographically weighted regression and ATP kriging for spatial downscaling. The proposed geographically weighted ATP regression kriging (GWATPRK) combines fine spatial resolution auxiliary information and allows for non-stationarity in a downscaling model. The approach was verified using eight groups of four different 25 km-resolution surface soil moisture (SSM) remote sensing products to obtain 1 km SSM predictions in two experimental regions, in conjunction with the implementation of three benchmark methods. Analyses and comparisons of the different downscaled results showed GWATPRK obtained downscaled fine spatial resolution images with greater quality and an average loss with a root mean square error value of 17.5%. The analysis indicated the proposed method has high potential for spatial downscaling in remote sensing applications. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle
Super-Resolution Mapping of Impervious Surfaces from Remotely Sensed Imagery with Points-of-Interest
Remote Sens. 2018, 10(2), 242; https://doi.org/10.3390/rs10020242 - 06 Feb 2018
Cited by 11
Abstract
The accurate mapping of impervious surfaces is of key significance for various urban applications. Usually, traditional methods extract the proportion image of impervious surfaces from remote sensing images; however, the proportion image cannot specify where the impervious surfaces spatially distribute within a pixel. [...] Read more.
The accurate mapping of impervious surfaces is of key significance for various urban applications. Usually, traditional methods extract the proportion image of impervious surfaces from remote sensing images; however, the proportion image cannot specify where the impervious surfaces spatially distribute within a pixel. Meanwhile, impervious surfaces often locate urban areas and have a strong correlation with the relatively new big (geo)data points of interest (POIs). This study, therefore, proposed a novel impervious surfaces mapping method (super-resolution mapping of impervious surfaces, SRMIS) by combining a super-resolution mapping technique and POIs to increase the spatial resolution of impervious surfaces in proportion images and determine the accurate spatial location of impervious surfaces within each pixel. SRMIS was evaluated using a 10-m Sentinel-2 image and a 30-m Landsat 8 Operational Land Imager (OLI) image of Nanjing city, China. The experimental results show that SRMIS generated satisfactory impervious surface maps with better-classified image quality and greater accuracy than a traditional hard classifier, the two existing super-resolution mapping (SRM) methods of the subpixel-swapping algorithm, or the method using both pixel-level and subpixel-level spatial dependence. The experimental results show that the overall accuracy increase of SRMIS was from 2.34% to 5.59% compared with the hard classification method and the two SRM methods in the first experiment, while the overall accuracy of SRMIS was 1.34–3.09% greater than that of the compared methods in the second experiment. Hence, this study provides a useful solution to combining SRM techniques and the relatively new big (geo)data (i.e., POIs) to extract impervious surface maps with a higher spatial resolution than that of the input remote sensing images, and thereby supports urban research. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle
Performance Evaluation of Downscaling Sentinel-2 Imagery for Land Use and Land Cover Classification by Spectral-Spatial Features
Remote Sens. 2017, 9(12), 1274; https://doi.org/10.3390/rs9121274 - 07 Dec 2017
Cited by 19
Abstract
Land Use and Land Cover (LULC) classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution [...] Read more.
Land Use and Land Cover (LULC) classification is vital for environmental and ecological applications. Sentinel-2 is a new generation land monitoring satellite with the advantages of novel spectral capabilities, wide coverage and fine spatial and temporal resolutions. The effects of different spatial resolution unification schemes and methods on LULC classification have been scarcely investigated for Sentinel-2. This paper bridged this gap by comparing the differences between upscaling and downscaling as well as different downscaling algorithms from the point of view of LULC classification accuracy. The studied downscaling algorithms include nearest neighbor resampling and five popular pansharpening methods, namely, Gram-Schmidt (GS), nearest neighbor diffusion (NNDiffusion), PANSHARP algorithm proposed by Y. Zhang, wavelet transformation fusion (WTF) and high-pass filter fusion (HPF). Two spatial features, textural metrics derived from Grey-Level-Co-occurrence Matrix (GLCM) and extended attribute profiles (EAPs), are investigated to make up for the shortcoming of pixel-based spectral classification. Random forest (RF) is adopted as the classifier. The experiment was conducted in Xitiaoxi watershed, China. The results demonstrated that downscaling obviously outperforms upscaling in terms of classification accuracy. For downscaling, image sharpening has no obvious advantages than spatial interpolation. Different image sharpening algorithms have distinct effects. Two multiresolution analysis (MRA)-based methods, i.e., WTF and HFP, achieve the best performance. GS achieved a similar accuracy with NNDiffusion and PANSHARP. Compared to image sharpening, the introduction of spatial features, both GLCM and EAPs can greatly improve the classification accuracy for Sentinel-2 imagery. Their effects on overall accuracy are similar but differ significantly to specific classes. In general, using the spectral bands downscaled by nearest neighbor interpolation can meet the requirements of regional LULC applications, and the GLCM and EAPs spatial features can be used to obtain more precise classification maps. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle
Improved DisTrad for Downscaling Thermal MODIS Imagery over Urban Areas
Remote Sens. 2017, 9(12), 1243; https://doi.org/10.3390/rs9121243 - 01 Dec 2017
Cited by 15
Abstract
Spaceborne thermal sensors provide important physical parameters for urban studies. However, due to technical constraints, spaceborne thermal sensors yield a trade-off between their spatial and temporal resolution. The aims of this study are (1) to downscale the three originally low spatial resolution (960 [...] Read more.
Spaceborne thermal sensors provide important physical parameters for urban studies. However, due to technical constraints, spaceborne thermal sensors yield a trade-off between their spatial and temporal resolution. The aims of this study are (1) to downscale the three originally low spatial resolution (960 m) Moderate Resolution Imaging Spectroradiometer (MODIS/Terra) land surface temperature image products (MOD11_L2, MOD11A1 and MOD11A2) to resolutions of 60, 90, 120, 240 and 480 m; and (2) to propose an improved version of the DisTrad method for downscaling the MODIS/Terra land surface temperature products over urban areas. The proposed improved DisTrad is based on a better parameterization of the original DisTrad residuals in urban areas. The improved resampling technique is based on a regression relationship between the residuals of the temperature estimation and the impervious percentage index. Validation of the improved DisTrad, the original DisTrad, and the uniformly disaggregated MODIS land surface temperature images (UniTrad) are performed by comparative analysis with a time-coincident Landsat 7 ETM+ thermal image. Statistical results indicate that the improved DisTrad method shows a higher correlation (R2 = 0.48) with the observed temperatures than the original DisTrad (R2 = 0.43) and a lower mean absolute error (MAE = 1.88 °C) than the original DisTrad (MAE = 2.07 °C). It is concluded that the improved DisTrad method has a stronger capability to downscale land surface temperatures in urban areas than the original DisTrad. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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Open AccessArticle
A Rigorously-Weighted Spatiotemporal Fusion Model with Uncertainty Analysis
Remote Sens. 2017, 9(10), 990; https://doi.org/10.3390/rs9100990 - 25 Sep 2017
Cited by 13
Abstract
Interest has been growing with regard to the use of remote sensing data characterized by a fine spatial resolution and frequent coverage for the monitoring of land surface dynamics. However, current satellite sensors are fundamentally limited by a trade-off between their spatial and [...] Read more.
Interest has been growing with regard to the use of remote sensing data characterized by a fine spatial resolution and frequent coverage for the monitoring of land surface dynamics. However, current satellite sensors are fundamentally limited by a trade-off between their spatial and temporal resolutions. Spatiotemporal fusion thus provides a feasible solution to overcome this limitation, and many blending algorithms have been developed. Among them, the popular spatial and temporal adaptive reflectance fusion model (STARFM) is based on a weighted function; however, it uses an ad hoc approach to estimate the weights of surrounding similar pixels. Additionally, an uncertainty analysis of the predicted result is not provided in the STARFM or any other fusion algorithm. This paper proposes a rigorously-weighted spatiotemporal fusion model (RWSTFM) based on geostatistics to blend the surface reflectances of Moderate Resolution Imaging Spectroradiometer (MODIS) and Landsat-5 Thematic Mapper (TM) imagery. The RWSTFM, which is based on ordinary kriging, derives the weights in terms of a fitted semivariance-distance relationship and calculates the estimation variance, which is a measure of the prediction uncertainty. The RWSTFM was tested using three datasets and compared with two commonly-used spatiotemporal reflectance fusion algorithms: the STARFM and the flexible spatiotemporal data fusion (FSDAF) method. The fusion results show that the proposed RWSTFM consistently outperformed the other algorithms both visually and quantitatively. Additionally, more than 70% of the squared error was accounted for by the estimation variance of the RWSTFM for all three of the datasets. Full article
(This article belongs to the Special Issue Remote Sensing Image Downscaling)
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