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Special Issue "Remote Sensors for Applications at Multi-Acquisition Levels and Resolutions"

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: closed (31 August 2019).

Special Issue Editors

Dr. Luiz E. O. C. Aragão
Website
Guest Editor
Instituto Nacional de Pesquisas Espaciais, Remote Sensing Division, Sao Jose dos Campos, SP, Brazil
Interests: remote sensing; Amazon; carbon cycle; degradation, fire, forest ecology
Dr. Ieda Del’Arco Sanches
Website
Guest Editor
Instituto Nacional de Pesquisas Espaciais, Remote Sensing Division, Sao Jose dos Campos, SP, Brazil
Interests: agricultural remote sensing; spectral behaviour of vegetation; hyperspectral remote sensing and agricultural statistics
Dr. Douglas F. M. Gherardi
Website
Guest Editor
Instituto Nacional de Pesquisas Espaciais, Remote Sensing Division, Sao Jose dos Campos, SP, Brazil
Interests: oceanography; remote sensing; biophysical models; fisheries, marine conservation

Special Issue Information

Dear Colleagues, 

This Special Issue is dedicated to the XIX Brazilian Symposium on Remote Sensing (XIX SBSR), 14–17April 2019, taking place in Santos, Brazil. It aims to accept manuscripts (review and original research articles) that highlight the latest achievements in the use of remote sensors for applications at multi-acquisition levels and resolutions. This is an open call for contributions. Despite the focus on the XIX SBSR, submissions are not limited to papers presented at the symposium. We seek original and innovative contributions that involve (but are not restricted to) sensor applications, sensing systems and principles, remote sensors, machine learning and modelling approaches, multispectral imaging, hyperspectral imaging, signal processing, data fusion and deep learning, and integrated sensor or internet-of-things technologies, among others. Papers that tackle complex Earth Observation issues by integrating traditional areas of application (e.g., agriculture, geology, oceanography) are also encouraged.

Dr. Luiz E. O. C. Aragão
Dr. Ieda Del’Arco Sanches
Dr. Douglas F. M. Gherardi
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 papers will be 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. Sensors 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 2000 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

  • multi-level remote sensing
  • multi-sensor remote sensing 
  • image processing 
  • internet of things 
  • data fusion and deep learning 
  • tropical environments 
  • sensing systems

Published Papers (3 papers)

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Open AccessArticle
An Improved Spatiotemporal Fusion Approach Based on Multiple Endmember Spectral Mixture Analysis
Sensors 2019, 19(11), 2443; https://doi.org/10.3390/s19112443 - 29 May 2019
Cited by 1
Abstract
High spatial and temporal resolution remotely sensed data is of great significance for the extraction of land use/cover information and the quantitative inversion of biophysical parameters. However, due to the limitation of sensor performance and the influence of rain cloud weather, it is [...] Read more.
High spatial and temporal resolution remotely sensed data is of great significance for the extraction of land use/cover information and the quantitative inversion of biophysical parameters. However, due to the limitation of sensor performance and the influence of rain cloud weather, it is difficult to obtain remote sensing images with both high spatial and temporal resolution. The spatiotemporal fusion model is a crucial method to solve this problem. The spatial and temporal adaptive reflectivity fusion model (STARFM) and its improved models are the most widely used spatiotemporal adaptive fusion models. However, the existing spatiotemporal adaptive reflectivity fusion model and its improved models have great uncertainty in selecting neighboring similar pixels, especially in spatially heterogeneous areas. Therefore, it is difficult to effectively search and determine neighboring spectrally similar pixels in STARFM-like models, resulting in a decrease of imagery fusion accuracy. In this research, we modify the procedure of neighboring similar pixel selection of ESTARFM method and propose an improved ESTARFM method (I-ESTARFM). Based on the land cover endmember types and its fraction values obtained by spectral mixing analysis, the neighboring similar pixels can be effectively selected. The experimental results indicate that the I-ESTARFM method selects neighboring spectrally similar pixels more accurately than STARFM and ESTARFM models. Compared with the STARFM and ESTARFM, the correlation coefficients of the image fused by the I-ESTARFM with that of the actual image are increased and the mean square error is decreased, especially in spatially heterogeneous areas. The uncertainty of spectral similar neighborhood pixel selection is reduced and the precision of spatial-temporal fusion is improved. Full article
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Open AccessArticle
On Optimal Imaging Angles in Multi-Angle Ocean Sun Glitter Remote-Sensing Platforms to Observe Sea Surface Roughness
Sensors 2019, 19(10), 2268; https://doi.org/10.3390/s19102268 - 16 May 2019
Abstract
Sea surface roughness (SSR) is a key physical parameter in studies of air–sea interactions and the ocean dynamics process. The SSR quantitative inversion model based on multi-angle sun glitter (SG) images has been proposed recently, which will significantly promote SSR observations through multi-angle [...] Read more.
Sea surface roughness (SSR) is a key physical parameter in studies of air–sea interactions and the ocean dynamics process. The SSR quantitative inversion model based on multi-angle sun glitter (SG) images has been proposed recently, which will significantly promote SSR observations through multi-angle remote-sensing platforms. However, due to the sensitivity of the sensor view angle (SVA) to SG, it is necessary to determine the optimal imaging angle and their combinations. In this study, considering the design optimization of imaging geometry for multi-angle remote-sensing platforms, we have developed an error transfer simulation model based on the multi-angle SG remote-sensing radiation transmission and SSR estimation models. We simulate SSR estimation errors at different imaging geometry combinations to evaluate the optimal observation geometry combination. The results show that increased SSR inversion accuracy can be obtained with SVA combinations of 0° and 20° for nadir- and backward-looking SVA compared with current combinations of 0° and 27.6°. We found that SSR inversion prediction error using the proposed model and actual SSR inversion error from field buoy data are correlated. These results can provide support for the design optimization of imaging geometry for multi-angle ocean remote-sensing platforms. Full article
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Other

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Open AccessLetter
Determining a Threshold to Delimit the Amazonian Forests from the Tree Canopy Cover 2000 GFC Data
Sensors 2019, 19(22), 5020; https://doi.org/10.3390/s19225020 - 18 Nov 2019
Abstract
Open global forest cover data can be a critical component for Reducing Emissions from Deforestation and Forest Degradation (REDD+) policies. In this work, we determine the best threshold, compatible with the official Brazilian dataset, for establishing a forest mask cover within the Amazon [...] Read more.
Open global forest cover data can be a critical component for Reducing Emissions from Deforestation and Forest Degradation (REDD+) policies. In this work, we determine the best threshold, compatible with the official Brazilian dataset, for establishing a forest mask cover within the Amazon basin for the year 2000 using the Tree Canopy Cover 2000 GFC product. We compared forest cover maps produced using several thresholds (10%, 30%, 50%, 80%, 85%, 90%, and 95%) with a forest cover map for the same year from the Brazilian Amazon Deforestation Monitoring Project (PRODES) data, produced by the National Institute for Space Research (INPE). We also compared the forest cover classifications indicated by each of these maps to 2550 independently assessed Landsat pixels for the year 2000, providing an accuracy assessment for each of these map products. We found that thresholds of 80% and 85% best matched with the PRODES data. Consequently, we recommend using an 80% threshold for the Tree Canopy Cover 2000 data for assessing forest cover in the Amazon basin. Full article
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