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Surface Radiative Transfer: Modeling, Inversion, and Applications
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Surface Radiative Transfer: Modeling, Inversion, and Applications

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

Deadline for manuscript submissions: closed (31 January 2025) | Viewed by 5854

Special Issue Editors

Dr. Zunjian Bian

E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Interests: radiative transfer model; land surface temperature
Dr. Xiangyang Liu

E-Mail Website
Guest Editor
Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100101, China
Interests: thermal infrared; agriculture remote sensing; soil science
Dr. Yazhen Jiang

E-Mail Website
Guest Editor
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
Interests: evapotranspiration; remote sensing; water balance
Dr. Jianbo Qi

E-Mail Website
Guest Editor
School of Geography, Beijing Normal University, Beijing 100875, China
Interests: earth observation; vegetation modeling; lidar; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Surface radiative transfer models are an essential foundation for the development of remote sensing. They play a crucial role not only in radiation balance but also in supporting the water and carbon cycles. Currently, these models are applied in vegetation monitoring, climate change studies, weather forecasting, and disaster early warning systems. In recent years, advancements in remote sensing hardware and observation methods have introduced new characteristics in spectral, spatial, temporal, and angular dimensions. Notable developments include transitions from multispectral to hyperspectral remote sensing, from medium and low resolution to high resolution, and from traditional remote sensing processing to onboard real-time processing. Radiative transfer models have significantly contributed to supporting and advancing remote sensing research. They underpin the validation and development of new remote sensing technologies, particularly in transitioning from homogeneous to heterogeneous remote sensing modeling and from single-band to multi-band simulations. Additionally, they facilitate the application of remote sensing data across various industries, including, but not limited to, agricultural yield estimation, weather forecasting, drought monitoring, and urban planning. Furthermore, with the widespread development of machine learning and deep learning, radiative transfer models can provide physical constraints for empirical models and supply research samples. Exploring the integration of physical models and empirical models is currently a significant research direction.

This Special Issue aims to explore the latest advancements and applications of surface radiative transfer models in remote sensing. It seeks to gather innovative research and case studies that highlight the significance of these models in understanding and addressing critical environmental issues. By examining the integration of new remote sensing technologies, machine learning, and deep learning techniques, this Special Issue will offer a comprehensive overview of how surface radiative transfer models contribute to various fields, including vegetation monitoring, climate change, weather forecasting, disaster early warning, and urban planning.

Articles may address, but are not limited to, the following topics:

  • Radiative transfer model;
  • 3D modeling;
  • Machine learning model;
  • Deep learning model;
  • Water balance;
  • Weather forecasting;
  • Drought monitoring;
  • Vegetation monitoring;
  • Climate change;
  • High-resolution remote sensing;
  • Onboard real-time processing;
  • Heterogeneous modeling;
  • Agricultural yield estimation;
  • Urban planning.

Dr. Zunjian Bian
Dr. Xiangyang Liu
Dr. Yazhen Jiang
Dr. Jianbo Qi
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

  • radiative transfer model
  • 1D/3D modeling
  • land surface temperature
  • agriculture application
  • drought monitoring

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

27 pages, 13502 KiB  
Article
Use of Radiative Transfer Model for Inter-Satellite Microwave Radiometer Calibration
by Patrick N. De La Llana, Faisal Bin Kashem and W. Linwood Jones
Remote Sens. 2025, 17(9), 1519; https://doi.org/10.3390/rs17091519 - 25 Apr 2025
Viewed by 214
Abstract
This paper describes the benefits of using a microwave radiative transfer model (RTM) to improve the inter-satellite radiometric calibration (XCAL) between two independent satellite microwave radiometers. Because this work was sponsored by the NASA Global Precipitation Mission, the emphasis of this paper is [...] Read more.
This paper describes the benefits of using a microwave radiative transfer model (RTM) to improve the inter-satellite radiometric calibration (XCAL) between two independent satellite microwave radiometers. Because this work was sponsored by the NASA Global Precipitation Mission, the emphasis of this paper is on radiometer channels that are used for atmospheric precipitation retrievals; however, this technique is applicable for microwave remote sensing in general, over a wide range of satellite remote-sensing applications. An XCAL example is presented for the NASA Global Precipitation Mission, whereby the GPM Microwave Imager is used to calibrate another microwave radiometer (TROPICS) within the GPM constellation of satellites. This approach involves intercomparing near-simultaneous measured brightness temperatures from these radiometers viewing a common homogeneous ocean scene. The double difference between observed and theoretical brightness temperature, derived using a radiative transfer model, is used to establish a radiometric calibration offset or bias. On-orbit comparisons are presented for two different approaches, namely, with and without the aid of the RTM. The results demonstrate significant improvements in the XCAL biases derived when using the RTM, and this is especially beneficial when one radiometer produces anomalous brightness temperatures. Full article
(This article belongs to the Special Issue Surface Radiative Transfer: Modeling, Inversion, and Applications)
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26 pages, 13283 KiB  
Article
Reconstruction of 30 m Land Cover in the Qilian Mountains from 1980 to 1990 Based on Super-Resolution Generative Adversarial Networks
by Xiaoya Wang, Bo Zhong, Kai Ao, Bailin Du, Longfei Hu, He Cai, Yang Qiao, Junjun Wu, Aixia Yang, Shanlong Wu and Qinhuo Liu
Remote Sens. 2024, 16(22), 4252; https://doi.org/10.3390/rs16224252 - 14 Nov 2024
Viewed by 1037
Abstract
Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land [...] Read more.
Long time series of annual land cover with fine spatio-temporal resolutions play a crucial role in studying environmental climate change, biophysical modeling, carbon cycling models, and land management. Despite a strong consistency exhibited by several publicly available medium to fine resolution global land cover datasets, significant discrepancies exist at the regional scale; moreover, only every 5/10 year land cover were available. Consequently, high-quality annual land cover datasets before 2000 are unavailable in China. In this study, we proposed a deep learning-based method by integrating multiple remote sensing data from different platforms with historical high spatial resolution land cover datasets (CNLUCC) to derive the 30 m annual land cover maps from 1980 to 1990 for Qilian Mountain. First, the super-resolution generative adversarial network models for upscaling the 5.5 km AVHRR NDVI to 250 m were established by employing the AVHRR and MODIS NDVI data with the same year as input, and the early time series AVHRR NDVI data were subsequently upscaled to 250 m through the above models. Second, the breaks for the additive seasonal and trend (BFAST) change detection algorithm was applied to the upscaled time series NDVI data to detect the change time of different land cover types. Third, the CNLUCC data in 1980 and 1990 were updated to annual land cover datasets from 1980 to 1990 and the annual mapping results provided insights into the dynamic processes of urbanization, deforestation, water bodies, and farmland from 1980 to 1990. Finally, comprehensive analysis and validation were carried out for evaluation and an overall accuracy of 77.26% for the land cover product in 1986 was achieved. Full article
(This article belongs to the Special Issue Surface Radiative Transfer: Modeling, Inversion, and Applications)
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17 pages, 2987 KiB  
Article
Melt Pond Evolution along the MOSAiC Drift: Insights from Remote Sensing and Modeling
by Mingfeng Wang, Felix Linhardt, Victor Lion and Natascha Oppelt
Remote Sens. 2024, 16(19), 3748; https://doi.org/10.3390/rs16193748 - 9 Oct 2024
Viewed by 1136
Abstract
Melt ponds play a crucial role in the melting of Arctic sea ice. Studying the evolution of melt ponds is essential for understanding changes in Arctic sea ice. In this study, we used a revised sea ice model to simulate the evolution of [...] Read more.
Melt ponds play a crucial role in the melting of Arctic sea ice. Studying the evolution of melt ponds is essential for understanding changes in Arctic sea ice. In this study, we used a revised sea ice model to simulate the evolution of melt ponds along the MOSAiC drift at a resolution of 10 m. A novel melt pond parameterization scheme simulates the movement of meltwater under the influence of gravity over a realistic sea ice topography. We evaluated different melt pond parameterization schemes based on remote sensing observations. The absolute deviation of the maximum pond coverage simulated by the new scheme is within 3%, while differences among parameterization schemes exceed 50%. Errors were found to be primarily due to the calculation of macroscopic meltwater loss, which is related to sea ice surface topography. Previous studies have indicated that sea ice with a lower surface roughness has a larger catchment area, resulting in larger pond coverage during the melt season. This study has identified an opposing mechanism: sea ice with lower surface roughness has a larger catchment area connected to the macroscopic flaws of the sea ice surface, which leads to more macroscopic drainage into the ocean and thereby a decrease in melt pond coverage. Experimental simulations showed that sea ice with 46% higher surface roughness, resulting in 12% less macroscopic drainage, exhibited a 38% higher maximum pond fraction. The presence of macroscopic flaws is related to the fragmentation of sea ice cover. As Arctic sea ice cover becomes increasingly fragmented and mobile, this mechanism will become more significant. Full article
(This article belongs to the Special Issue Surface Radiative Transfer: Modeling, Inversion, and Applications)
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16 pages, 13318 KiB  
Article
Investigation and Validation of Split-Window Algorithms for Estimating Land Surface Temperature from Landsat 9 TIRS-2 Data
by Qinghua Su, Xiangchen Meng and Lin Sun
Remote Sens. 2024, 16(19), 3633; https://doi.org/10.3390/rs16193633 - 29 Sep 2024
Viewed by 1417
Abstract
Land surface temperature (LST) is important in a variety of applications, such as urban thermal environment monitoring and water resource management. In this paper, eleven candidate split-window (SW) algorithms were adapted to Thermal Infrared Sensor-2 (TIRS-2) data of the Landsat 9 satellite for [...] Read more.
Land surface temperature (LST) is important in a variety of applications, such as urban thermal environment monitoring and water resource management. In this paper, eleven candidate split-window (SW) algorithms were adapted to Thermal Infrared Sensor-2 (TIRS-2) data of the Landsat 9 satellite for estimating the LST. The simulated dataset produced by extensive radiative transfer modeling and five global atmospheric profile databases was used to determine the SW algorithm coefficients. Ground measurements gathered at Surface Radiation Budget Network sites were used to confirm the efficiency of the SW algorithms after their performance was initially examined using the independent simulation dataset. Five atmospheric profile databases perform similarly in training accuracy under various subranges of total water vapor. The candidate SW algorithms demonstrate superior performance compared to the radiative transfer equation algorithm, exhibiting a reduction in overall bias and RMSE by 1.30 K and 1.0 K, respectively. It is expected to provide guidance for the generation of the Landsat 9 LST using the SW algorithms. Full article
(This article belongs to the Special Issue Surface Radiative Transfer: Modeling, Inversion, and Applications)
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Graphical abstract

27 pages, 32217 KiB  
Article
Stripe Noise Elimination with a Novel Trend Repair Method for Push-Broom Thermal Images
by Zelin Zhang, Hua Li, Yongming Du, Yao Chen, Guoxiang Zhao, Zunjian Bian, Biao Cao, Qing Xiao and Qinhuo Liu
Remote Sens. 2024, 16(17), 3299; https://doi.org/10.3390/rs16173299 - 5 Sep 2024
Viewed by 1070
Abstract
Stripe noise is a general phenomenon in original remote sensing images that both degrades image quality and severely limits its quantitative application. While the classical statistical method is effective in correcting common stripes caused by inaccurately calibrating relative gains and offsets between detectors, [...] Read more.
Stripe noise is a general phenomenon in original remote sensing images that both degrades image quality and severely limits its quantitative application. While the classical statistical method is effective in correcting common stripes caused by inaccurately calibrating relative gains and offsets between detectors, it falls short in correcting other nonlinear stripe noises originating from subtle nonlinear changes or random contamination within the same detector. Therefore, this paper proposes a novel trend repair method based on two normal columns directly adjacent to a defective column to rectify the trend by considering the geospatial structure of contaminated pixels, eliminating residual stripe noise evident in level 0 (L0) remote sensing images after histogram matching. GF5-02 VIMI (Gaofen5-02, visual and infrared multispectral imager) images and simulated Landsat 8 thermal infrared sensor (TIRS) images deliberately infused with stripe noise are selected to test the new method and two other existing methods, the piece-wise method and the iterated weighted least squares (WLS) method. The effectiveness of these three methods is reflected by streaking metrics (Streaking), structural similarity (SSIM), peak signal-to-noise ratio (PSNR), and improvement factor (IF) on the uniformity, structure, and information content of the corrected GF5-02 VIMI images and by the accuracy of the corrected simulated Landsat 8 TIRS images. The experimental results indicate that the trend repair method proposed in this paper removes nonlinear stripe noise effectively, making the results of IF > 20. The remaining indicators also show satisfactory results; in particular, the mean accuracy derived from the simulated image remains below a digital number (DN) of 15, which is far superior to the other two methods. Full article
(This article belongs to the Special Issue Surface Radiative Transfer: Modeling, Inversion, and Applications)
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