Special Issue "Cross-Platform Remote Sensing for Enhanced Land Surface Characterization"

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

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

Special Issue Editors

Dr. Rasmus Houborg
Website
Guest Editor
Geospatial Sciences Center of Excellence, South Dakota State University, Brookings, SD 57007, USA
Interests: satellite remote sensing; CubeSats; UAVs; plant biophysical traits and function; machine-learning; precision agriculture
Dr. Christopher R. Hain
Website
Guest Editor
NASA, 320 Sparkman Drive, Huntsville, AL 35805, USA
Interests: surface energy balance modeling; soil moisture retrieval; hydrologic data assimilation and drought monitoring
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Special Issue Information

Dear Colleagues,

Earth observing capacity is continually expanding as a result of conventional large satellite missions, in combination with a growing number of sensor constellations comprised of smaller and comparatively inexpensive satellites. The increasing volume, dimensionality, and capability of remote sensing data provide unprecedented opportunities for advancing retrieval capabilities and overcoming traditional constraints associated with space-borne monitoring. Single platform sensor data is associated with a unique set of compromising attributes with enhanced capability in one domain countered by reduced capability in another. The traditional trade-off between spatial resolution and temporal frequency is a classic example of this conundrum. It follows, that critical requirements regarding radiometric quality and spectral and spatiotemporal resolution often cannot be met based on a single source. Only through complimentary cross-platform methodologies can the full potential of these enhanced observational resources be realized.

The focus of this Special Issue is to promote novel synergistic cross-platform approaches to advance retrieval capabilities and enhance the versatility and robustness of retrieved biophysical properties. We will be accepting submissions within the following broadly-defined topics:

  • Multi-sensor integration and data fusion to enhance the spatiotemporal resolution of retrievable surface properties (e.g., spectral vegetation indices, vegetation traits, land surface temperature, soil moisture).
  • Adaptation of models and retrieval algorithms to take advance of cross-sensor synergies in sensor observations across the electromagnetic spectrum (e.g., visible to shortwave infrared, sun-induced fluorescence, thermal, microwave) in order to improve land surface monitoring and modeling.
  • Combination of observations from multi-scale platforms (e.g., geostationary, polar-orbiting, unmanned aerial vehicles, proximal) to advance retrieval capability and enhance retrieval robustness.
  • Advancing the use of machine-learning for analyzing and interpreting multi-sensor data streams.
  • Advancing the use of data assimilation to take advantage of multi-sensor and multi-resolution data streams.
Dr. Rasmus Houborg
Dr. Christopher R. Hain
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. 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 2200 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-sensor integration
  • land surface characterization
  • remote sensing
  • data fusion
  • data assimilation
  • machine-learning
  • biophysical properties
  • cross-sensor synergies
  • multi-scale platforms

Published Papers (1 paper)

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Research

Open AccessArticle
An SRTM-Aided Epipolar Resampling Method for Multi-Source High-Resolution Satellite Stereo Observation
Remote Sens. 2019, 11(6), 678; https://doi.org/10.3390/rs11060678 - 21 Mar 2019
Cited by 1
Abstract
Binocular stereo observation with multi-source satellite images used to be challenging and impractical, but is now a valuable research issue with the introduction of powerful deep-learning-based stereo matching approaches. However, epipolar resampling, which is critical for binocular stereo observation, has rarely been studied [...] Read more.
Binocular stereo observation with multi-source satellite images used to be challenging and impractical, but is now a valuable research issue with the introduction of powerful deep-learning-based stereo matching approaches. However, epipolar resampling, which is critical for binocular stereo observation, has rarely been studied with multi-source satellite images. The main problem is that, under the multi-source stereo mode, the epipolar-line-direction (ELD) at an image location may vary when computed with different elevations. Thus, a novel SRTM (Shuttle Radar Topography Mission)-aided approach is proposed, where a point is transformed from the original image-space to the epipolar image-space through a global rotation, followed by a block-wise homography transformation. The global rotation transfers the ELDs at the center of the overlapping area to the x-axis, and then block-wise transformation shifts the ELDs of all grid-points to the x-axis and eliminates the y-disparities between the virtual corresponding points. Experiments with both single-source and multi-source stereo images showed that the proposed method is obviously more accurate than the previous methods that do not use SRTM. Moreover, with some of the multi-source image pairs, only the proposed method ensured the y-disparities remained within ±1 pixel. Full article
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