Special Issue "Remote Sensing on Land Surface Albedo"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (10 September 2021).

Special Issue Editors

Dr. Yongze Song
E-Mail Website
Guest Editor
School of Design and the Built Environment, Curtin University, Kent Street, Bentley, WA 6102, Australia
Interests: sustainable development; spatial statistics; geospatial methods; urban remote sensing; sustainable infrastructure
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Joseph Awange
E-Mail Website
Guest Editor
School of Earth and Planetary Sciences, Curtin University, Perth, Australia
Interests: environmental geoinformatics (satellite environmental monitoring; water resource research; climate change; geodesy; hydrology; environmental impact assessment and management) and mathematical geosciences (algebra and robust statistics)
Dr. Chi Chen
E-Mail Website
Guest Editor
Climate and Ecosystem Sciences Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA
Interests: remote sensing of vegetation; land-atmosphere interaction; land surface modeling; carbon cycle; physical modeling of vegetation canopy

Special Issue Information

Dear Colleagues,

Land surface albedo is an essential Earth observation variable of the surface radiation budget and energy balance. It has been widely applied in characterizing spatiotemporal patterns of land use change, surface temperature, vegetation, soil, built-up surface, and urban microclimate and environment. Diverse global and regional remote sensing products with multiple spatial and temporal resolutions are available for monitoring land surface albedo, such as MODIS, MISR, AVHRR, Landsat, MSG, and Meteosat. The land surface albedo can also be estimated with images acquired by unmanned aerial vehicles (UAV). Studies of the land surface albedo generally include three categories: data acquisition, assessment of products, and applications. Knowledge of land surface albedo has been increasingly accumulated in recent years, but there are still challenges and new problems for the three categories of studies.

The aim of this Special Issue is to present latest research of applying land surface albedo in addressing the urban, climate, environmental, and social challenges. The Special Issue also encourages studies that integrate new technologies and methods to acquire more accurate and efficient data at various spatial scales.

Dr. Yongze Song
Prof. Dr. Joseph Awange
Dr. Chi Chen
Dr. Naoto Yokoya
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 2500 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

  • Land surface albedo
  • Bidirectional reflectance distribution function (BRDF)
  • Coupled land–atmosphere interaction
  • Microclimate and environment
  • Climate change
  • Urban land surface
  • Urbanization
  • Land use/land cover change
  • Deep learning/machine learning
  • Land surface albedo product validation
  • Land surface albedo data acquisition
  • High-resolution land surface albedo
  • Long term land surface albedo
  • Spatiotemporal variations
  • UAV-based land surface albedo
  • Spatial accuracy and uncertainty
  • Spatial patterns

Published Papers (2 papers)

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Research

Article
Identifying Spatial and Temporal Characteristics of Land Surface Albedo Using GF-1 WFV Data
Remote Sens. 2021, 13(20), 4070; https://doi.org/10.3390/rs13204070 - 12 Oct 2021
Viewed by 458
Abstract
Land surface albedo (LSA) is an important parameter that affects surface–air interactions and controls the surface radiation energy budget. The spatial and temporal variation characteristics of LSA reflect land surface changes and further influence the local climate. Ganzhou District, which belongs to the [...] Read more.
Land surface albedo (LSA) is an important parameter that affects surface–air interactions and controls the surface radiation energy budget. The spatial and temporal variation characteristics of LSA reflect land surface changes and further influence the local climate. Ganzhou District, which belongs to the middle of the Hexi Corridor, is a typical irrigated agricultural and desert area in Northwest China. The study of the interaction of LSA and the land surface is of great significance for understanding the land surface energy budget and for ground measurements. In this study, high spatial and temporal resolution GF-1 wide field view (WFV) data were used to explore the spatial and temporal variation characteristics of LSA in Ganzhou District. First, the surface albedo of Ganzhou District was estimated by the GF-1 WFV. Then, the estimated results were verified by the surface measured data, and the temporal and spatial variation characteristics of surface albedo from 2014 to 2018 were analyzed. The interaction between albedo and precipitation or temperature was analyzed based on precipitation and temperature data. The results show that the estimation of surface albedo based on GF-1 WFV data was of high accuracy, which can meet the accuracy requirements of spatial and temporal variation characteristic analysis of albedo. There are obvious geographic differences in the spatial distribution of surface albedo in Ganzhou, with the overall distribution characteristics being high in the north and low in the middle. The interannual variation in annual average surface albedo in Ganzhou shows a trend of slow fluctuations and gradual increases. The variation in annual albedo is characterized by “double peaks and a single valley”, with the peaks occurring from December to February at the end and beginning of the year, and the valley occurring from June to August. Surface albedo was negatively correlated with precipitation and temperature in most areas of Ganzhou. Full article
(This article belongs to the Special Issue Remote Sensing on Land Surface Albedo)
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Article
An Efficient Decision Support System for Flood Inundation Management Using Intermittent Remote-Sensing Data
Remote Sens. 2021, 13(14), 2818; https://doi.org/10.3390/rs13142818 - 17 Jul 2021
Cited by 1 | Viewed by 996
Abstract
Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day [...] Read more.
Timely acquisition of spatial flood distribution is an essential basis for flood-disaster monitoring and management. Remote-sensing data have been widely used in water-body surveys. However, due to the cloudy weather and complex geomorphic environment, the inability to receive remote-sensing images throughout the day has resulted in some data being missing and unable to provide dynamic and continuous flood inundation process data. To fully and effectively use remote-sensing data, we developed a new decision support system for integrated flood inundation management based on limited and intermittent remote-sensing data. Firstly, we established a new multi-scale water-extraction convolutional neural network named DEU-Net to extract water from remote-sensing images automatically. A specific datasets training method was created for typical region types to separate the water body from the confusing surface features more accurately. Secondly, we built a waterfront contour active tracking model to implicitly describe the flood movement interface. In this way, the flooding process was converted into the numerical solution of the partial differential equation of the boundary function. Space upwind difference format and the time Euler difference format were used to perform the numerical solution. Finally, we established seven indicators that considered regional characteristics and flood-inundation attributes to evaluate flood-disaster losses. The cloud model using the entropy weight method was introduced to account for uncertainties in various parameters. In the end, a decision support system realizing the flood losses risk visualization was developed by using the ArcGIS application programming interface (API). To verify the effectiveness of the model constructed in this paper, we conducted numerical experiments on the model’s performance through comparative experiments based on a laboratory scale and actual scale, respectively. The results were as follows: (1) The DEU-Net method had a better capability to accurately extract various water bodies, such as urban water bodies, open-air ponds, plateau lakes etc., than the other comparison methods. (2) The simulation results of the active tracking model had good temporal and spatial consistency with the image extraction results and actual statistical data compared with the synthetic observation data. (3) The application results showed that the system has high computational efficiency and noticeable visualization effects. The research results may provide a scientific basis for the emergency-response decision-making of flood disasters, especially in data-sparse regions. Full article
(This article belongs to the Special Issue Remote Sensing on Land Surface Albedo)
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