Atmospheric Environment and Agro-Ecological Environment

A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2924

Special Issue Editors


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Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Interests: agricultural remote sensing; validation of remote sensing products; temporal and spatial analysis
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Aerospace Information Research Institute,Chinese Academy of Sciences, Beijing 100094, China
Interests: remote sensing; geographic information system; atmospheric environment; spatial analysis; data fusion

Special Issue Information

Dear Colleagues,

The quality of the agro-ecological environment is closely related to the atmospheric environment, which is also affected by atmospheric environment monitoring. When conducting atmospheric environment control research, we should select atmospheric environment monitoring technology that can effectively determine the concentration and type of air pollution to identify the relevant control program, reduce air pollution, and properly maintain the agricultural ecological environment.

In recognition of this shift, the open access journal Atmosphere is hosting a Special Issue to showcase the most recent findings related to atmospheric environment monitoring technology and methods, agricultural ecological environment monitoring technology and methods, and atmospheric environment monitoring and agricultural ecological environment interaction, to better serve the important needs of modern agricultural development and atmospheric environment protection in China. It will present the latest trends, current progress and future research directions of multi-source remote sensing in agricultural and atmospheric environment research. Potential topics include (but are not limited to):

  • High-resolution remote sensing agricultural and atmospheric applications.
  • High-accuracy agriculture and atmospheric remote sensing data inversion algorithm.
  • Uncertainty analysis of multi-source agricultural and atmospheric remote sensing products.
  • Multi-source remote sensing data fusion and assimilation model.
  • Atmospheric environment monitoring and assessment.

Dr. Chunmei Wang
Dr. Lili Zhang
Guest Editors

Manuscript Submission Information

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Keywords

  • agricultural resources and environment
  • atmospheric environment
  • cultivated land quality
  • data fusion and assimilation
  • remote sensing observation
  • environment monitor
  • inversion algorithm
  • environment assessment

Published Papers (3 papers)

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Research

18 pages, 45932 KiB  
Article
A Methodological Approach for Gap Filling of WFV Gaofen-1 Images from Spatial Autocorrelation and Enhanced Weighting
by Tairu Chen, Tao Yu, Lili Zhang, Wenhao Zhang, Xiaofei Mi, Yan Liu, Yulin Zhan, Chunmei Wang, Juan Li and Jian Yang
Atmosphere 2024, 15(3), 252; https://doi.org/10.3390/atmos15030252 - 21 Feb 2024
Viewed by 597
Abstract
Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 [...] Read more.
Clouds and cloud shadow cover cause missing data in some images captured by the Gaofen-1 Wide Field of View (GF-1 WFV) cameras, limiting the extraction and analysis of the image information and further applications. Therefore, this study proposes a methodology to fill GF-1 WFV images using the spatial autocorrelation and improved weighting (SAIW) method. Specifically, the search window size is adaptively determined using Getis-Ord Gi* as a metric. The spatial and spectral weights of the pixels are computed using the Chebyshev distance and spectral angle mapper to better filter the suitable similar pixels. Each missing pixel is predicted using linear regression with similar pixels on the reference image and the corresponding similar pixel located in the non-missing region of the cloudy image. Simulation experiments showed that the average correlation coefficient of the proposed method in this study is 0.966 in heterogeneous areas, 0.983 in homogeneous farmland, and 0.948 in complex urban areas. It suggests that SAIW can reduce the spread of errors in the gap-filling process to significantly improve the accuracy of the filling results and can produce satisfactory qualitative and quantitative fill results in a wide range of typical land cover types and has extensive application potential. Full article
(This article belongs to the Special Issue Atmospheric Environment and Agro-Ecological Environment)
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18 pages, 6676 KiB  
Article
Research on Missing Value Imputation to Improve the Validity of Air Quality Data Evaluation on the Qinghai-Tibetan Plateau
by Yumeng Wang, Ke Liu, Yuejun He, Qiming Fu, Wei Luo, Wentao Li, Xuan Liu, Pengfei Wang and Siyuan Xiao
Atmosphere 2023, 14(12), 1821; https://doi.org/10.3390/atmos14121821 - 13 Dec 2023
Cited by 1 | Viewed by 700
Abstract
In the Qinghai-Tibet Plateau region, operational deficiencies and limited maintenance capacities often impair automatic air quality monitoring stations. This results in frequent data omissions, compromising the reliability of environmental assessment data. Therefore, an effective data imputation method is required to address the gaps [...] Read more.
In the Qinghai-Tibet Plateau region, operational deficiencies and limited maintenance capacities often impair automatic air quality monitoring stations. This results in frequent data omissions, compromising the reliability of environmental assessment data. Therefore, an effective data imputation method is required to address the gaps in observational records. Utilizing a Sequence-to-Sequence framework, we introduce a model termed Bidirectional Recurrent Imputation for Time Series-Attention-based Long Short-Term Memory (BRITS-ALSTM). The encoder of BRITS-ALSTM applies BRITS to integrate single-station historical characteristics with multi-station correlation features. Concurrently, the decoder employs LSTM within an attention mechanism to capitalize on previously observed data, thereby generating hourly imputations for missing air quality data values. The model was trained using six types of air quality data from 16 stations across Qinghai Province. Through localized testing and parameter optimization, BRITS-ALSTM achieved a reduction in mean relative error (MRE) by 74.88% compared to the baseline mean-filling approach. Additionally, ablation studies demonstrated an improvement in the coefficient of determination R-squared (R2) from 0.67 to 0.76, outperforming the standalone BRITS. Consequently, BRITS-ALSTM enhances the accuracy of air quality data evaluations in the Tibetan Plateau and offers an efficacious strategy for data imputation in elevated terrains. Full article
(This article belongs to the Special Issue Atmospheric Environment and Agro-Ecological Environment)
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17 pages, 11979 KiB  
Article
Cloud and Cloud Shadow Detection of GF-1 Images Based on the Swin-UNet Method
by Yuhao Tan, Wenhao Zhang, Xiufeng Yang, Qiyue Liu, Xiaofei Mi, Juan Li, Jian Yang and Xingfa Gu
Atmosphere 2023, 14(11), 1669; https://doi.org/10.3390/atmos14111669 - 10 Nov 2023
Cited by 1 | Viewed by 1022
Abstract
Cloud and cloud shadow detection in remote sensing images is an important preprocessing technique for quantitative analysis and large-scale mapping. To solve the problems of cloud and cloud shadow detection based on Convolutional Neural Network models, such as rough edges and insufficient overall [...] Read more.
Cloud and cloud shadow detection in remote sensing images is an important preprocessing technique for quantitative analysis and large-scale mapping. To solve the problems of cloud and cloud shadow detection based on Convolutional Neural Network models, such as rough edges and insufficient overall accuracy, cloud and cloud shadow segmentation based on Swin-UNet was studied in the wide field of view (WFV) images of GaoFen-1 (GF-1). The Swin Transformer blocks help the model capture long-distance features and obtain deeper feature information in the network. This study selects a public GF1_WHU cloud and cloud shadow detection dataset for preprocessing and data optimization and conducts comparative experiments in different models. The results show that the algorithm performs well on vegetation, water, buildings, barren and other types. The average accuracy of cloud detection is 98.01%, the recall is 96.84% and the F1-score is 95.48%. The corresponding results of cloud shadow detection are 84.64%, 83.12% and 97.55%. In general, compared to U-Net, PSPNet and DeepLabV3+, this model performs better in cloud and cloud shadow detection, with clearer detection boundaries and a higher accuracy in complex surface conditions. This proves that Swin-UNet has great feature extraction capability in moderate and high-resolution remote sensing images. Full article
(This article belongs to the Special Issue Atmospheric Environment and Agro-Ecological Environment)
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