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Environmental Remote Sensing

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Remote Sensors".

Deadline for manuscript submissions: 17 May 2024 | Viewed by 6974

Special Issue Editors

Dr. Zuchuan Li

E-Mail Website1 Website2
Guest Editor
Division of Natural and Applied Sciences, Duke Kunshan University, Kunshan 215316, China
Interests: data science; machine learning; biogeochemistry
Prof. Dr. Qiangqiang Yuan

grade E-Mail Website
Guest Editor
School of Geodesy and Geomatics, Wuhan University, Wuhan 430079, China
Interests: image reconstruction; image denoising; image super-resolution; remote sensing image processing; data fusion and application
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Kun Shi

E-Mail Website
Guest Editor
Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing 210008, China
Interests: optical properties of inland waters; remote sensing of lake environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Ecosystems have been and will continue to be forced by multiple factors, for example, increased atmospheric CO2 and global warming in this and following centuries. However, we may not document how the ecosystems respond to global warming because of limited observations. Remotely sensed data have exponentially increased in recent years, and can cover the Earth within several days with a spatial resolution of several kilometers. Remotely sensed data can be used to derive parameters such as the temperature and biomass of ecosystems. While satellite remote sensing offers a great opportunity to investigate aquatic and terrestrial ecosystems and the environment, we are also overwhelmed by the sheer quantities of big data. Therefore, environmental scientists must determine how to maximize the benefit of increased observations and remotely sensed data. One approach is to leverage methods from modern statistics, machine learning, and data science. In this Special Issue, we invite submissions focused on the applications of remote sensing to global warming, water and ocean color, biogeochemistry, and research on the intersection of remote sensing, environmental science, machine learning, and data science.

Dr. Zuchuan Li
Prof. Dr. Qiangqiang Yuan
Prof. Dr. Kun Shi
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. Sensors 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 2600 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 warming
  • remote sensing
  • water and ocean color
  • aquatic and terrestrial biogeochemistry
  • global carbon cycle
  • big data
  • environmental data science
  • machine learning for environmental sciences

Published Papers (4 papers)

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Research

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15 pages, 2178 KiB  
Article
Characterizing Soil Profile Salinization in Cotton Fields Using Landsat 8 Time-Series Data in Southern Xinjiang, China
by Jiaqiang Wang, Bifeng Hu, Weiyang Liu, Defang Luo and Jie Peng
Sensors 2023, 23(15), 7003; https://doi.org/10.3390/s23157003 - 07 Aug 2023
Cited by 1 | Viewed by 1106
Abstract
Soil salinization is a major obstacle to land productivity, crop yield and crop quality in arid areas and directly affects food security. Soil profile salt data are key for accurately determining irrigation volumes. To explore the potential for using Landsat 8 time-series data [...] Read more.
Soil salinization is a major obstacle to land productivity, crop yield and crop quality in arid areas and directly affects food security. Soil profile salt data are key for accurately determining irrigation volumes. To explore the potential for using Landsat 8 time-series data to monitor soil salinization, 172 Landsat 8 images from 2013 to 2019 were obtained from the Alar Reclamation Area of Xinjiang, northwest China. The multiyear extreme dataset was synthesized from the annual maximum or minimum values of 16 vegetation indices, which were combined with the soil conductivity of 540 samples from soil profiles at 0~0.375 m, 0~0.75 m and 0~1.00 m depths in 30 cotton fields with varying degrees of salinization as investigated by EM38-MK2. Three remote sensing monitoring models for soil conductivity at different depths were constructed using the Cubist method, and digital mapping was carried out. The results showed that the Cubist model of soil profile electrical conductivity from 0 to 0.375 m, 0 to 0.75 m and 0 to 1.00 m showed high prediction accuracy, and the determination coefficients of the prediction set were 0.80, 0.74 and 0.72, respectively. Therefore, it is feasible to use a multiyear extreme value for the vegetation index combined with a Cubist modeling method to monitor soil profile salinization at a regional scale. Full article
(This article belongs to the Special Issue Environmental Remote Sensing)
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24 pages, 13268 KiB  
Article
Reconstruction of Gap-Free Land Surface Temperature at a 100 m Spatial Resolution from Multidimensional Data: A Case in Wuhan, China
by Zefeng Wu, Hongfen Teng, Haoxiang Chen, Lingyu Han and Liangliang Chen
Sensors 2023, 23(2), 913; https://doi.org/10.3390/s23020913 - 12 Jan 2023
Cited by 3 | Viewed by 1349
Abstract
Land surface temperatures (LST) are vital parameters in land surface–atmosphere interactions. Constrained by technology and atmospheric interferences, LST retrievals from various satellite sensors usually return missing data, thus negatively impacting analyses. Reconstructing missing data is important for acquiring gap-free datasets. However, the current [...] Read more.
Land surface temperatures (LST) are vital parameters in land surface–atmosphere interactions. Constrained by technology and atmospheric interferences, LST retrievals from various satellite sensors usually return missing data, thus negatively impacting analyses. Reconstructing missing data is important for acquiring gap-free datasets. However, the current reconstruction methods are limited for maintaining spatial details and high accuracies. We developed a new gap-free algorithm termed the spatial feature-considered random forest regression (SFRFR) model; it builds stable nonlinear relationships to connect the LST with related parameters, including terrain elements, land coverage types, spectral indexes, surface reflectance data, and the spatial feature of the LST, to reconstruct the missing LST data. The SFRFR model reconstructed gap-free LST data retrieved from the Landsat 8 satellite on 27 July 2017 in Wuhan. The results show that the SFRFR model exhibits the best performance according to the various evaluation metrics among the SFRFR, random forest regression and spline interpolation, with a coefficient of determination (R2) reaching 0.96, root-mean-square error (RMSE) of 0.55, and mean absolute error (MAE) of 0.55. Then, we reconstructed gap-free LST data gathered in Wuhan from 2016 to 2021 to analyze urban thermal environment changes and found that 2020 presented the coolest temperatures. The SFRFR model still displayed satisfactory results, with an average R2 of 0.91 and an MAE of 0.63. We further discuss and discover the factors affecting the visual performance of SFRFR and identify the research priority to circumvent these disadvantages. Overall, this study provides a simple, practical method for acquiring gap-free LST data to help us better understand the spatiotemporal LST variation process. Full article
(This article belongs to the Special Issue Environmental Remote Sensing)
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19 pages, 13751 KiB  
Article
Mapping Local Climate Zones in the Urban Environment: The Optimal Combination of Data Source and Classifier
by Siying Cui, Xuhong Wang, Xia Yang, Lifa Hu, Ziqi Jiang and Zihao Feng
Sensors 2022, 22(17), 6407; https://doi.org/10.3390/s22176407 - 25 Aug 2022
Cited by 2 | Viewed by 1392
Abstract
The novel concept of local climate zones (LCZs) provides a consistent classification framework for studies of the urban thermal environment. However, the development of urban climate science is severely hampered by the lack of high-resolution data to map LCZs. Using Gaofen-6 and Sentinel-1/2 [...] Read more.
The novel concept of local climate zones (LCZs) provides a consistent classification framework for studies of the urban thermal environment. However, the development of urban climate science is severely hampered by the lack of high-resolution data to map LCZs. Using Gaofen-6 and Sentinel-1/2 as data sources, this study designed four schemes using convolutional neural network (CNN) and random forest (RF) classifiers, respectively, to demonstrate the potential of high-resolution images in LCZ mapping and evaluate the optimal combination of different data sources and classifiers. The results showed that the combination of GF-6 and CNN (S3) was considered the best LCZ classification scheme for urban areas, with OA and kappa coefficients of 85.9% and 0.842, respectively. The accuracy of urban building categories is above 80%, and the F1 score for each category is the highest, except for LCZ1 and LCZ5, where there is a small amount of confusion. The Sentinel-1/2-based RF classifier (S2) was second only to S3 and superior to the combination of GF-6 and random forest (S1), with OA and kappa coefficients of 64.4% and 0.612, respectively. The Sentinel-1/2 and CNN (S4) combination has the worst classification result, with an OA of only 39.9%. The LCZ classification map based on S3 shows that the urban building categories in Xi’an are mainly distributed within the second ring, while heavy industrial buildings have started to appear in the third ring. The urban periphery is mainly vegetated and bare land. In conclusion, CNN has the best application effect in the LCZ mapping task of high-resolution remote sensing images. In contrast, the random forest algorithm has better robustness in the band-abundant Sentinel data. Full article
(This article belongs to the Special Issue Environmental Remote Sensing)
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Review

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24 pages, 1474 KiB  
Review
Optical Remote Sensing in Provisioning of Ecosystem-Functions Analysis—Review
by Pavel Vyvlečka and Vilém Pechanec
Sensors 2023, 23(10), 4937; https://doi.org/10.3390/s23104937 - 20 May 2023
Cited by 5 | Viewed by 1864
Abstract
Keeping natural ecosystems and their functions in the proper condition is necessary. One of the best contactless monitoring methods is remote sensing, especially optical remote sensing, which is used for vegetation applications. In addition to satellite data, data from ground sensors are necessary [...] Read more.
Keeping natural ecosystems and their functions in the proper condition is necessary. One of the best contactless monitoring methods is remote sensing, especially optical remote sensing, which is used for vegetation applications. In addition to satellite data, data from ground sensors are necessary for validation or training in ecosystem-function quantification. This article focuses on the ecosystem functions associated with aboveground-biomass production and storage. The study contains an overview of the remote-sensing methods used for ecosystem-function monitoring, especially methods for detecting primary variables linked to ecosystem functions. The related studies are summarized in multiple tables. Most studies use freely available Sentinel-2 or Landsat imagery, with Sentinel-2 mostly producing better results at larger scales and in areas with vegetation. The spatial resolution is a key factor that plays a significant role in the accuracy with which ecosystem functions are quantified. However, factors such as spectral bands, algorithm selection, and validation data are also important. In general, optical data are usable even without supplementary data. Full article
(This article belongs to the Special Issue Environmental Remote Sensing)
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