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Remote Sensing
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Image Enhancement Techniques to Guarantee Sensors Interoperability
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Image Enhancement Techniques to Guarantee Sensors Interoperability

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

Deadline for manuscript submissions: closed (30 June 2021) | Viewed by 16098

Special Issue Editors

Dr. Piero Toscano

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Guest Editor
Institute of BioEconomy (IBE), National Research Council (CNR), Via Caproni 8, 50145 Florence, Italy
Interests: remote sensing; precision agriculture; crop modeling; climate services
Special Issues, Collections and Topics in MDPI journals
Dr. Nguyen-Thanh Son

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Guest Editor
National Central University, No. 300, Jhongda Rd., Zhongli District, Taoyuan City 32001, Taiwan
Interests: remote sensing; GIS; digital image processing and analysis; crop yield modeling; environmental monitoring

Special Issue Information

Dear Colleagues,

Remote sensing data/images have been widely utilized in many remote sensing applications; however, the trade-off between spatial resolution, temporal frequency, and spectral resolution has limited their capacities in monitoring detailed spatiotemporal dynamics. Furthermore, due to increasingly diverse and temporal datasets provided by different platforms/sensors, there is a need to provide their interoperability.

This Special Issue aims to contribute to the dissemination of pioneering research findings in the monitoring and characterization of terrestrial ecosystems through the development and implementation of new and appropriate enhancement techniques spanning diverse aspects of remote sensing.

Only short letters and communications (maximum length of 10 pages) will be considered for publication in this Special Issue.

Dr. Piero Toscano
Dr. Nguyen-Thanh Son
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

  • spatiotemporal enhancement
  • fusion-based, nonfusion-based
  • interoperability
  • radiometric correction
  • remote sensing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

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11 pages, 43930 KiB  
Article
BRDF Estimations and Normalizations of Sentinel 2 Level 2 Data Using a Kalman-Filtering Approach and Comparisons with RadCalNet Measurements
by Bertrand Saulquin
Remote Sens. 2021, 13(17), 3373; https://doi.org/10.3390/rs13173373 - 25 Aug 2021
Cited by 1 | Viewed by 2487
Abstract
BRDF estimation aims to characterize the anisotropic behaviour of the observed surface, which is directly related to the type of surface. BRDF theoretical models are then used in the normalization of the satellite-derived observations to virtually replace the sensor at the nadir. Such [...] Read more.
BRDF estimation aims to characterize the anisotropic behaviour of the observed surface, which is directly related to the type of surface. BRDF theoretical models are then used in the normalization of the satellite-derived observations to virtually replace the sensor at the nadir. Such normalization reinforces the homogeneity within and between satellite-derived time series. Nevertheless, the inversion of the necessary BRDF parameters for the normalization requires the implementation of robust methods to account for the noise in the Level 2 surface reflectances caused by the atmospheric correction process. Here, we compare normalized reflectances obtained with a Kalman filtering approach with i/the classical weighted linear inversion and ii/a normalization performed using the coefficients of the NASA-MODIS BRDF MCD43A1 band 2 product. We show, using the RadCalNet nadir-view reflectances, that the Kalman filtering approach is a more suitable approach for the Sen2Cor level 2 and the selected sites. Using the proposed approach, daily global maps of land surface BRDF coefficients and the derived normalized Sentinel 2 reflectances would be extremely useful to the global and regional climate modelling communities and for the world’s cover monitoring. Full article
(This article belongs to the Special Issue Image Enhancement Techniques to Guarantee Sensors Interoperability)
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11 pages, 1494 KiB  
Communication
AgroShadow: A New Sentinel-2 Cloud Shadow Detection Tool for Precision Agriculture
by Ramona Magno, Leandro Rocchi, Riccardo Dainelli, Alessandro Matese, Salvatore Filippo Di Gennaro, Chi-Farn Chen, Nguyen-Thanh Son and Piero Toscano
Remote Sens. 2021, 13(6), 1219; https://doi.org/10.3390/rs13061219 - 23 Mar 2021
Cited by 19 | Viewed by 5062
Abstract
Remote sensing for precision agriculture has been strongly fostered by the launches of the European Space Agency Sentinel-2 optical imaging constellation, enabling both academic and private services for redirecting farmers towards a more productive and sustainable management of the agroecosystems. As well as [...] Read more.
Remote sensing for precision agriculture has been strongly fostered by the launches of the European Space Agency Sentinel-2 optical imaging constellation, enabling both academic and private services for redirecting farmers towards a more productive and sustainable management of the agroecosystems. As well as the freely and open access policy adopted by the European Space Agency (ESA), software and tools are also available for data processing and deeper analysis. Nowadays, a bottleneck in this valuable chain is represented by the difficulty in shadow identification of Sentinel-2 data that, for precision agriculture applications, results in a tedious problem. To overcome the issue, we present a simplified tool, AgroShadow, to gain full advantage from Sentinel-2 products and solve the trade-off between omission errors of Sen2Cor (the algorithm used by the ESA) and commission errors of MAJA (the algorithm used by Centre National d’Etudes Spatiales/Deutsches Zentrum für Luft- und Raumfahrt, CNES/DLR). AgroShadow was tested and compared against Sen2Cor and MAJA in 33 Sentinel 2A-B scenes, covering the whole of 2020 and in 18 different scenarios of the whole Italian country at farming scale. AgroShadow returned the lowest error and the highest accuracy and F-score, while precision, recall, specificity, and false positive rates were always similar to the best scores which alternately were returned by Sen2Cor or MAJA. Full article
(This article belongs to the Special Issue Image Enhancement Techniques to Guarantee Sensors Interoperability)
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Other

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11 pages, 4282 KiB  
Letter
Random Forests for Landslide Prediction in Tsengwen River Watershed, Central Taiwan
by Youg-Sin Cheng, Teng-To Yu and Nguyen-Thanh Son
Remote Sens. 2021, 13(2), 199; https://doi.org/10.3390/rs13020199 - 8 Jan 2021
Cited by 17 | Viewed by 4394
Abstract
Landslides have been identified as one of the costliest and deadliest natural disasters, causing tremendous damage to humans and societies. Information regarding the spatial extent of landslides is thus important to allow officials to devise successful strategies to mitigate landslide hazards. This study [...] Read more.
Landslides have been identified as one of the costliest and deadliest natural disasters, causing tremendous damage to humans and societies. Information regarding the spatial extent of landslides is thus important to allow officials to devise successful strategies to mitigate landslide hazards. This study aims to develop a machine-learning approach for predicting landslide areas in the Tsengwen River Watershed (TRW), which is one of the most landslide-prone areas in Central Taiwan. Various spatial datasets were collected from 2009 to 2015 to derive 36 predictive variables used for landslide modeling with random forests (RF). The results of landslide prediction, compared with ground reference data, indicated an overall accuracy of 91.4% and Kappa coefficient of 0.83, respectively. The findings achieved from estimates of predictor importance also indicated to officials that the land-use/land-cover (LULC) type, distance to previous landslides, distance to roads, bank erosion, annual groundwater recharge, geological line density, aspect, and slope are the most influential factors that trigger landslides in the study region. Full article
(This article belongs to the Special Issue Image Enhancement Techniques to Guarantee Sensors Interoperability)
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13 pages, 4051 KiB  
Letter
Retrieval of Sea Surface Wind Fields Using Multi-Source Remote Sensing Data
by Tangao Hu, Yue Li, Yao Li, Yiyue Wu and Dengrong Zhang
Remote Sens. 2020, 12(9), 1482; https://doi.org/10.3390/rs12091482 - 7 May 2020
Cited by 6 | Viewed by 3049
Abstract
Timely and accurate sea surface wind field (SSWF) information plays an important role in marine environmental monitoring, weather forecasting, and other atmospheric science studies. In this study, a piecewise linear model is proposed to retrieve SSWF information based on the combination of two [...] Read more.
Timely and accurate sea surface wind field (SSWF) information plays an important role in marine environmental monitoring, weather forecasting, and other atmospheric science studies. In this study, a piecewise linear model is proposed to retrieve SSWF information based on the combination of two different satellite sensors (a microwave scatterometer and an infrared scanning radiometer). First, the time series wind speed dataset, extracted from the HY-2A satellite, and the brightness temperature dataset, extracted from the FY-2E satellite, were matched. The piecewise linear regression model with the highest R2 was then selected as the best model to retrieve SSWF information. Finally, experiments were conducted with the Usagi, Fitow, and Nari typhoons in 2013 to evaluate accuracy. The results show that: (1) the piecewise linear model is successfully established for all typhoons with high R2 (greater than 0.61); (2) for all three cases, the root mean square error () and mean bias error (MBE) are smaller than 2.2 m/s and 1.82 m/s, which indicates that it is suitable and reliable for SSWF information retrieval; and (3) it solves the problem of the low temporal resolution of HY-2A data (12 h), and inherits the high temporal resolution of the FY-2E data (0.5 h). It can provide reliable and high temporal SSWF products. Full article
(This article belongs to the Special Issue Image Enhancement Techniques to Guarantee Sensors Interoperability)
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