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Special Issue "Remote Sensing for Mapping Global Land Surface Parameters"

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

Deadline for manuscript submissions: 31 May 2023 | Viewed by 3842

Special Issue Editor

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

Special Issue Information

Dear Colleagues,

Global and continental scale-mapping of land surface parameters is essential for understanding, analysis, and management of the large-scale natural and social environment. The remotely sensed land surface parameters generally consist of land cover and land use, climate variables, vegetation, leaf area index, biomass, bushfire, soil properties, river, lake, snow, glaciers, albedo, etc. The remote sensing products, such as nighttime light, can also provide essential datasets for social studies. The process of using remote sensing to address practical issues may include the development of objectives and hypotheses, collection of remote sensing images and in situ data, image processing, visualization, and analysis, model validation and accuracy assessment, and decision making. Recent advances in the remotely sensed land surface parameters at a global scale can provide new findings, methods, datasets, experience, and knowledge for academic research, professional implementations, and community practice.

This Special Issue aims to collect studies on the development, mapping, and implementation of remote-sensing-based global land surface parameters. Topics may include any land surface parameters at a large spatial scale, such as global, continental, and country scales. The land surface parameters may cover any aspects of mapping the natural environment, such as land use, climate, vegetation, water, soil, ecology, air conditions, etc., as well as implementing the parameters in the built environment and social environment. In addition, topics may also cover studies on the development of datasets of global land surface parameters, and methods for data processing, analysis, and decision making.

This Special Issue aims to collect research articles and reviews about “Remote Sensing for Mapping Global Land Surface Parameters”. The broad topics of this Special Issue include, but are not limited to:

  • Methods, datasets, or applications of remotely sensed land surface parameters;
  • Mapping large-scale natural or social environment using remote sensing;
  • Multi-scale spatial analysis of land surface parameters;
  • Remote sensing or grid product comparison and validation;
  • Remote sensing and mapping global sustainability;
  • Mapping global land cover and land use;
  • Mapping global vegetation, leaf area index, greenness, or biomass;
  • Mapping global natural hazards, such as bushfire, droughts, flooding, and extreme temperature;
  • Mapping global water systems, such as river, lake, snow, and glaciers;
  • Mapping global soil properties, such as soil moisture and organic carbon;
  • Mapping global nighttime light and socio-economic development;
  • Mapping global social attributes using remote sensing, such as building, disease, and population;
  • Reviews of remote sensing for mapping land surface parameters.

Dr. Yongze Song
Guest Editor

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 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

  • global mapping
  • global land surface mapping
  • large-scale mapping
  • multi-scale mapping
  • land surface parameters
  • global land surface datasets
  • remote sensing products

Published Papers (4 papers)

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Research

Article
Improved Global Gross Primary Productivity Estimation by Considering Canopy Nitrogen Concentrations and Multiple Environmental Factors
Remote Sens. 2023, 15(3), 698; https://doi.org/10.3390/rs15030698 - 24 Jan 2023
Viewed by 528
Abstract
The terrestrial gross primary productivity (GPP) plays a crucial role in regional or global ecological environment monitoring and carbon cycle research. Many previous studies have produced multiple products using different models, but there are still significant differences between these products. This study generated [...] Read more.
The terrestrial gross primary productivity (GPP) plays a crucial role in regional or global ecological environment monitoring and carbon cycle research. Many previous studies have produced multiple products using different models, but there are still significant differences between these products. This study generated a global GPP dataset (NI-LUE GPP) with 0.05° spatial resolution and at 8 day-intervals from 2001 to 2018 based on an improved light use efficiency (LUE) model that simultaneously considered temperature, water, atmospheric CO2 concentrations, radiation components, and nitrogen (N) index. To simulate the global GPP, we mapped the global optimal ecosystem temperatures (Topteco) using satellite-retrieved solar-induced chlorophyll fluorescence (SIF) and applied it to calculate temperature stress. In addition, green chlorophyll index (CIgreen), which had a strong correlation with the measured canopy N concentrations (r = 0.82), was selected as the vegetation index to characterize the canopy N concentrations to calculate the spatiotemporal dynamic maximum light use efficiency (εmax). Multiple existing global GPP datasets were used for comparison. Verified by FLUXNET GPP, our product performed well on daily and yearly scales. NI-LUE GPP indicated that the mean global annual GPP is 129.69 ± 3.11 Pg C with an increasing trend of 0.53 Pg C/yr from 2001 to 2018. By calculating the SPAtial Efficiency (SPAEF) with other products, we found that NI-LUE GPP has good spatial consistency, which indicated that our product has a reasonable spatial pattern. This product provides a reliable and alternative dataset for large-scale carbon cycle research and monitoring long-term GPP variations. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping Global Land Surface Parameters)
Article
Extraction of Aquaculture Ponds along Coastal Region Using U2-Net Deep Learning Model from Remote Sensing Images
Remote Sens. 2022, 14(16), 4001; https://doi.org/10.3390/rs14164001 - 17 Aug 2022
Cited by 1 | Viewed by 749
Abstract
The main challenge in extracting coastal aquaculture ponds is how to weaken the influence of the “same-spectrum foreign objects” effect and how to improve the definition of the boundary and accuracy of the extraction results of coastal aquaculture ponds. In this study, a [...] Read more.
The main challenge in extracting coastal aquaculture ponds is how to weaken the influence of the “same-spectrum foreign objects” effect and how to improve the definition of the boundary and accuracy of the extraction results of coastal aquaculture ponds. In this study, a recognition model based on the U2-Net deep learning model using remote sensing images for extracting coastal aquaculture ponds has been constructed. Firstly, image preprocessing is performed to amplify the spectral features. Second, samples are produced by visual interpretation. Third, the U2-Net deep learning model is used to train and extract aquaculture ponds along the coastal region. Finally, post-processing is performed to optimize the extraction results of the model. This method was validated in experiments in the Zhoushan Archipelago, China. The experimental results show that the average F-measure of the method in the study for the four study cases reaches 0.93, and the average precision and average recall rate are 92.21% and 93.79%, which is suitable for extraction applications in aquaculture ponds along the coastal region. This study can quickly and accurately carry out the mapping of coastal aquaculture ponds and can provide technical support for marine resource management and sustainable development. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping Global Land Surface Parameters)
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Article
Inundation Resilience Analysis of Metro-Network from a Complex System Perspective Using the Grid Hydrodynamic Model and FBWM Approach: A Case Study of Wuhan
Remote Sens. 2022, 14(14), 3451; https://doi.org/10.3390/rs14143451 - 18 Jul 2022
Viewed by 601
Abstract
The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional [...] Read more.
The upward trend of metro flooding disasters inevitably brings new challenges to urban underground flood management. It is essential to evaluate the resilience of metro systems so that efficient flood disaster plans for preparation, emergency response, and timely mitigation may be developed. Traditional response solutions merged multiple sources of data and knowledge to support decision-making. An obvious drawback is that original data sources for evaluations are often stationary, inaccurate, and subjective, owing to the complexity and uncertainty of the metro station’s actual physical environment. Meanwhile, the flood propagation path inside the whole metro station network was prone to be neglected. This paper presents a comprehensive approach to analyzing the resilience of metro networks to solve these problems. Firstly, we designed a simplified weighted and directed metro network module containing six characteristics by a topological approach while considering the slope direction between sites. Subsequently, to estimate the devastating effects and details of the flood hazard on the metro system, a 100-year rainfall–flood scenario simulation was conducted using high-precision DEM and a grid hydrodynamic model to identify the initially above-ground inundated stations (nodes). We developed a dynamic node breakdown algorithm to calculate the inundation sequence of the nodes in the weighted and directed network of the metro. Finally, we analyzed the resilience of the metro network in terms of toughness strength and organization recovery capacity, respectively. The fuzzy best–worst method (FBWM) was developed to obtain the weight of each assessment metric and determine the toughness strength of each node and the entire network. The results were as follows. (1) A simplified three-dimensional metro network based on a complex system perspective was established through a topological approach to explore the resilience of urban subways. (2) A grid hydrodynamic model was developed to accurately and efficiently identify the initially flooded nodes, and a dynamic breakdown algorithm realistically performed the flooding process of the subway network. (3) The node toughness strength was obtained automatically by a nonlinear FBWM method under the constraint of the minimum error to sustain the resilience assessment of the metro network. The research has considerable implications for managing underground flooding and enhancing the resilience of the metro network. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping Global Land Surface Parameters)
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Article
A Methodology to Generate Integrated Land Cover Data for Land Surface Model by Improving Dempster-Shafer Theory
Remote Sens. 2022, 14(4), 972; https://doi.org/10.3390/rs14040972 - 16 Feb 2022
Cited by 5 | Viewed by 909
Abstract
Land cover type is a key parameter for simulating surface processes in many land surface models (LSMs). Currently, the widely used global remote sensing land cover products cannot meet the requirements of LSMs for classification systems, physical definition, data accuracy, and space-time resolution. [...] Read more.
Land cover type is a key parameter for simulating surface processes in many land surface models (LSMs). Currently, the widely used global remote sensing land cover products cannot meet the requirements of LSMs for classification systems, physical definition, data accuracy, and space-time resolution. Here, a new fusion method was proposed to generate land cover data for LSMs by fusing multi-source remote sensing land cover data, which was based on improving Dempster-Shafer evidence theory with mathematical models and knowledge rules optimization. The new method has the ability to deal with seriously disagreement information, thereby improving the robustness of the theory. The results showed the new method can reduce the disagreement between input data and realized the conversion of multiple land cover classification systems to into a single land cover classification system. China Fusion Land Cover data (CFLC) in 2015 generated by the new method maintained the classification accuracy of the China land use map (CNLULC), which is based on visual image interpretation and further enriched land cover classes of input data. Compared with Geo-Wiki observations in 2015, the overall accuracy for CFLC is higher than other two global land cover data. Compared with the observations, the 0–10 cm soil moisture simulated by the CFLC in Noah–MP LSM during the growing season in 2014 had better performance than that simulated by initial land cover data and MODIS land cover data. Our new method is highly portable and generalizable to generate higher quality land cover data with a specific land cover classification system for LSMs by fusing multiple land cover data, providing a new approach to land cover mapping for LSMs. Full article
(This article belongs to the Special Issue Remote Sensing for Mapping Global Land Surface Parameters)
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