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Remote Sensing of Surface BRDF and Albedo

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

Deadline for manuscript submissions: 24 December 2024 | Viewed by 1944

Special Issue Editors


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Guest Editor
German Research Centre for Geosciences, Telegrafenberg, 14473 Potsdam, Germany
Interests: cloud remote sensing; aerosol remote sensing; trace gas remote sensing; snow remote sensing; radiative transfer
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
School of Mechatronical Engineering, Beijing Institute of Technology, Beijing 100081, China
Interests: earth observation; image fusion processing; target detection

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Guest Editor
State Key Laboratory of Remote Sensing Science, Beijing Normal University, Beijing 100875, China
Interests: quantitative remote sensing; modeling; information extraction
School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin 300387, China
Interests: remote sensing; BRDF and albedo

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Guest Editor
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Interests: atmospheric physics; precipitation; climate modeling; climate variability; fluorescence; nanomaterials; optics and lasers; material characterization; air quality; environment
Special Issues, Collections and Topics in MDPI journals
School of Resources and Environmental Engineering, Hefei University of Technology, Hefei 230009, China
Interests: remote sensing; soil; vegetation; snow; BRDF; spatiotemporal

Special Issue Information

Dear Colleagues,

Surface albedo is a key parameter in the surface energy balance and has been identified as a primary essential climate variable (ECV) that can be used as a diagnostic tool for local climate change, land cover change, etc. With the rapid development of spaceborne satellite remotely sensed sensors and various airborne and near-ground measurement platforms in recent years, there has been great improvement in the spatial resolution of remotely sensed data, which provides an opportunity to investigate subtle surface albedo change in natural and artificial objects in high-resolution ranging from centimeters to tens of meters. The traditional estimation method of surface albedo usually relies on a bidirectional reflectance distribution function (BRDF) reconstructed from multi-angular reflectance, and a direct estimation method based on prior information has also been developed and widely used. However, previous studies mostly dealt with medium-resolution sensors that can capture multi-angular observations, and high-resolution albedo estimation still meets the challenge of lacking multi-angular measurements. To solve this problem, the new algorithms for multi-angular reflectance and albedo determination using remote sensing data are needed. In addition, unmanned aerial vehicle (UAV) and large-scale radiative transfer simulation models are newly developed practical tools to obtain remotely sensed data at multiple scales, which can also assist theory and algorithm development. This Special Issue aims to bring together research on remote sensing of surface BRDF and albedo regarding algorithms, measurements, simulations, variance analysis, and applications. Original research as well as review articles and short communications with a particular focus on remote sensing of BRDF and albedo of various surfaces including vegetation, soil, snow, ice and oceanic surface are welcome for submission.

Dr. Alexander Kokhanovsky
Dr. Xiaoning Zhang
Prof. Dr. Ziti Jiao
Dr. Hu Zhang
Prof. Dr. Tao He
Dr. Anxin Ding
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

  • BRDF
  • albedo
  • multi-angle remote sensing
  • high resolution
  • estimation
  • radiative transfer simulation
  • UAV
  • measurement
  • application

Published Papers (2 papers)

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Research

21 pages, 3645 KiB  
Article
Evaluating the Performance of the Enhanced Ross-Li Models in Characterizing BRDF/Albedo/NBAR Characteristics for Various Land Cover Types in the POLDER Database
by Anxin Ding, Ziti Jiao, Alexander Kokhanovsky, Xiaoning Zhang, Jing Guo, Ping Zhao, Mingming Zhang, Hailan Jiang and Kaijian Xu
Remote Sens. 2024, 16(12), 2119; https://doi.org/10.3390/rs16122119 - 11 Jun 2024
Viewed by 297
Abstract
The latest versions of the Ross-Li model include kernels that represent isotropic reflection of the surface, describe backward reflection of soil and vegetation systems, characterize strong forward reflection of snow, and adequately consider the hotspot effect (i.e., RossThick-LiSparseReciprocalChen-Snow, RTLSRCS), theoretically able to effectively [...] Read more.
The latest versions of the Ross-Li model include kernels that represent isotropic reflection of the surface, describe backward reflection of soil and vegetation systems, characterize strong forward reflection of snow, and adequately consider the hotspot effect (i.e., RossThick-LiSparseReciprocalChen-Snow, RTLSRCS), theoretically able to effectively characterize BRDF/Albedo/NBAR features for various land surface types. However, a systematic evaluation of the RTLSRCS model is still lacking for various land cover types. In this paper, we conducted a thorough assessment of the RTLSRCS and RossThick-LiSparseReciprocalChen (RTLSRC) models in characterizing BRDF/Albedo/NBAR characteristics by using the global POLDER BRDF database. The primary highlights of this paper include the following: (1) Both models demonstrate high accuracy in characterizing the BRDF characteristics across 16 IGBP types. However, the accuracy of the RTLSRC model is notably reduced for land cover types with high reflectance and strong forward reflection characteristics, such as Snow and Ice (SI), Deciduous Needleleaf Forests (DNF), and Barren or Sparsely Vegetated (BSV). In contrast, the RTLSRCS model shows a significant improvement in accuracy for these land cover types. (2) These two models exhibit highly consistent albedo inversion across various land cover types (R2 > 0.9), particularly in black-sky and blue-sky albedo, except for SI. However, significant differences in white-sky albedo inversion persist between these two models for Evergreen Needleleaf Forests (ENF), Evergreen Broadleaf Forests (EBF), Urban Areas (UA), and SI (p < 0.05). (3) The NBAR values inverted by these two models are nearly identical across the other 15 land cover types. However, the consistency of NBAR results is relatively poor for SI. The RTLSRC model tends to overestimate compared to the RTLSRCS model, with a noticeable bias of approximately 0.024. This study holds significant importance for understanding different versions of Ross-Li models and improving the accuracy of satellite BRDF/Albedo/NBAR products. Full article
(This article belongs to the Special Issue Remote Sensing of Surface BRDF and Albedo)
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21 pages, 13549 KiB  
Article
Methodology and Modeling of UAV Push-Broom Hyperspectral BRDF Observation Considering Illumination Correction
by Zhuo Wang, Haiwei Li, Shuang Wang, Liyao Song and Junyu Chen
Remote Sens. 2024, 16(3), 543; https://doi.org/10.3390/rs16030543 - 31 Jan 2024
Viewed by 882
Abstract
The Bidirectional Reflectance Distribution Function (BRDF) is a critical spatial distribution parameter in the quantitative research of remote sensing and has a wide range of applications in radiometric correction, elemental inversion, and surface feature estimation. As a new means of BRDF modeling, UAV [...] Read more.
The Bidirectional Reflectance Distribution Function (BRDF) is a critical spatial distribution parameter in the quantitative research of remote sensing and has a wide range of applications in radiometric correction, elemental inversion, and surface feature estimation. As a new means of BRDF modeling, UAV push-broom hyperspectral imaging is limited by the push-broom imaging method, and the multi-angle information is often difficult to obtain. In addition, the random variation of solar illumination during UAV low-altitude flight makes the irradiance between different push-broom hyperspectral rows and different airstrips inconsistent, which significantly affects the radiometric consistency of BRDF modeling and results in the difficulty of accurately portraying the three-dimensional spatial reflectance distribution in the UAV model. These problems largely impede the application of outdoor BRDF. Based on this, this paper proposes a fast multi-angle information acquisition scheme with a high-accuracy BRDF modeling method considering illumination variations, which mainly involves a lightweight system for BRDF acquisition and three improved BRDF models considering illumination corrections. We adopt multi-rectangular nested flight paths for multi-gray level targets, use multi-mode equipment to acquire spatial illumination changes and multi-angle reflectivity information in real-time, and introduce the illumination correction factor K through data coupling to improve the kernel, Hapke, and RPV models, and, overall, the accuracy of the improved model is increased by 20.83%, 11.11%, and 31.48%, respectively. The results show that our proposed method can acquire multi-angle information quickly and accurately using push-broom hyperspectral imaging, and the improved model eliminates the negative effect of illumination on BRDF modeling. This work is vital for expanding the multi-angle information acquisition pathway and high-efficiency and high-precision outdoor BRDF modeling. Full article
(This article belongs to the Special Issue Remote Sensing of Surface BRDF and Albedo)
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