Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Browser

Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Published Papers

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

Deadline for manuscript submissions: 20 June 2024 | Viewed by 8174

Share This Special Issue

Special Issue Editors

Prof. Dr. Kebiao Mao

E-Mail Website
Guest Editor

Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China
Interests: artificial intelligence; deep learning; retrieval paradigm; soil moisture retrieval; land surface temperature retrieval; water vapor content retrieval; near surface temperature retrieval
Special Issues, Collections and Topics in MDPI journals

Dr. Sayed M. Bateni

E-Mail Website
Guest Editor

Department of Civil and Environmental Engineering, Water Resources Research Center, University of Hawaii at Manoa, Honolulu, HI 96822, USA
Interests: satellite data processing; land surface product algorithm; remote sensing classification with machine learning;agrometeorology; agrometeorological disater monitoring with remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Jongmin Park

E-Mail Website
Guest Editor

Department of Civil and Environmental Envineering, Korea National University of Transportation, Chungbuk 27469, Republic of Korea
Interests: remote sensing of hydro-meteorological variable; terrestrial hydrology; artificial intelligence; land surface modeling and data assimilation; flood and drought monitoring

Dr. Lixin Dong

E-Mail Website
Guest Editor

National Satellite Meteorological Center (National Center for Space Weather), China Meteorological Administration, Beijing 100081, China
Interests: agricultural meteorology & remote sensing; thermal infrared land surface temperature inversion; lidar; biomass inversion; authenticity validation; neural network

Special Issue Information

Dear Colleagues,

In recent years, artificial intelligence has become the core driving force behind a new wave of industrial transformation; this will further unleash the enormous energy of technological innovation. The combination of artificial intelligence and specific industries will lead to the emergence of novel technologies and products, profoundly changing human thinking and production patterns, and achieving an overall leap in social and industrial productivity. Considering the potential and significance of deep learning in the fields of geology and agriculture, in order to promote the application of artificial intelligence in the fields of geology and agriculture, it is necessary to accelerate the deep integration of artificial intelligence and remote sensing technology, provide key technical support for meteorological forecasting, agricultural monitoring, and agricultural disaster prediction, and thus facilitate global disaster monitoring and food security. Cross-disciplinary research is only in its preliminary stages, and the majority of deep learning applications in geosciences are still “black boxes”, with most applications lacking physical significance, interpretability, and universality.

This Special Issue aims to study the application of artificial intelligence methods in the retrieval of remote sensing key parameters in geology and agriculture. Topics may address anything from the retrieval of surface temperature or soil moisture, to atmospheric water vapor content and rainfall in the atmosphere.

Hence, submissions describing remote sensing parameters retrieved from multi-source data (such as multispectral, hyperspectral, thermal infrared, and microwave) at multiple scales are welcome. Articles may address, but are not limited, to the following topics:

Surface Temperature
Near-Surface Air Temperature
Surface Emissivity
Soil Moisture
Vegetation Moisture Content
Water Vapor Content
Precipitation
LAI
Drought and Flood

Prof. Dr. Kebiao Mao
Dr. Sayed M. Bateni
Dr. Jongmin Park
Dr. Lixin Dong
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.

Published Papers (7 papers)

Download All Papers

Research

Jump to: Other

20 pages, 14683 KiB

Open AccessArticle

A Spatiotemporal Enhanced SMAP Freeze/Thaw Product (1980–2020) over China and Its Preliminary Analyses

by Hongjing Cui, Linna Chai, Heng Li, Shaojie Zhao, Xiaoyan Li and Shaomin Liu

Remote Sens. 2024, 16(6), 950; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16060950 - 08 Mar 2024

Viewed by 459

Abstract

The soil freeze/thaw (FT) state has emerged as a critical role in the ecosystem, hydrological, and biogeochemical processes, but obtaining representative soil FT state datasets with a long time sequence, fine spatial resolution, and high accuracy remains challenging. Therefore, we propose a decision-level spatiotemporal data fusion algorithm based on Convolutional Long Short-Term Memory networks (ConvLSTM) to expand the SMAP-enhanced L3 landscape freeze/thaw product (SMAP_E_FT) temporally. In the algorithm, the Freeze/Thaw Earth System Data Record product (ESDR_FT) is sucked in the ConvLSTM and fused with SMAP_E_FT at the decision level. Eight predictor datasets, i.e., soil temperature, snow depth, soil moisture, precipitation, terrain complexity index, area of open water data, latitude and longitude, are used to train the ConvLSTM. Direct validation using six dense observation networks located in the Genhe, Maqu, Naqu, Pali, Saihanba, and Shandian river shows that the fusion product (ConvLSTM_FT) effectively absorbs the high accuracy characteristics of ESDR_FT and expands SMAP_E_FT with an overall average improvement of 2.44% relative to SMAP_E_FT, especially in frozen seasons (averagely improved by 7.03%). The result from indirect validation based on categorical triple collocation also shows that ConvLSTM_FT performs stable regardless of land cover types, climate types, and terrain complexity. The findings, drawn from preliminary analyses on ConvLSTM_FT from 1980 to 2020 over China, suggest that with global warming, most parts of China suffer from different degrees of shortening of the frozen period. Moreover, in the Qinghai–Tibet region, the higher the permafrost thermal stability, the faster the degradation rate. Full article

(This article belongs to the Special Issue Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing)

► Show Figures

Figure 1

22 pages, 1917 KiB

Open AccessArticle

MSSFF: Advancing Hyperspectral Classification through Higher-Accuracy Multistage Spectral–Spatial Feature Fusion

by Yuhan Chen, Qingyun Yan and Weimin Huang

Remote Sens. 2023, 15(24), 5717; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15245717 - 13 Dec 2023

Viewed by 903

Abstract

This paper presents the MSSFF (multistage spectral–spatial feature fusion) framework, which introduces a novel approach for semantic segmentation from hyperspectral imagery (HSI). The framework aims to simplify the modeling of spectral relationships in HSI sequences and unify the architecture for semantic segmentation of HSIs. It incorporates a spectral–spatial feature fusion module and a multi-attention mechanism to efficiently extract hyperspectral features. The MSSFF framework reevaluates the potential impact of spectral and spatial features on segmentation models and leverages the spectral–spatial fusion module (SSFM) in the encoder component to effectively extract and enhance these features. Additionally, an efficient Transformer (ET) is introduced in the skip connection part of deep features to capture long-term dependent features and extract global spectral–spatial information from the entire feature map. This highlights the significant potential of Transformers in modeling spectral–spatial feature maps within the context of hyperspectral remote sensing. Moreover, a spatial attention mechanism is adopted in the shallow skip connection part to extract local features. The framework demonstrates promising capabilities in hyperspectral remote sensing applications. The conducted experiments provide valuable insights for optimizing the model depth and the order of feature fusion, thereby contributing to the advancement of hyperspectral semantic segmentation research. Full article

(This article belongs to the Special Issue Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing)

► Show Figures

Figure 1

21 pages, 8701 KiB

Open AccessArticle

Spatial Downscaling of Near-Surface Air Temperature Based on Deep Learning Cross-Attention Mechanism

by Zhanfei Shen, Chunxiang Shi, Runping Shen, Ruian Tie and Lingling Ge

Remote Sens. 2023, 15(21), 5084; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15215084 - 24 Oct 2023

Cited by 1 | Viewed by 973

Abstract

Deep learning methods can achieve a finer refinement required for downscaling meteorological elements, but their performance in terms of bias still lags behind physical methods. This paper proposes a statistical downscaling network based on Light-CLDASSD that utilizes a Shuffle–nonlinear-activation-free block (SNBlock) and Swin cross-attention mechanism (SCAM), and is named SNCA-CLDASSD, for the China Meteorological Administration Land Data Assimilation System (CLDAS). This method aims to achieve a more accurate spatial downscaling of a temperature product from 0.05° to 0.01° for the CLDAS. To better utilize the digital elevation model (DEM) for reconstructing the spatial texture of the temperature field, a module named SCAM is introduced, which can activate more input pixels and enable the network to correct and merge the extracted feature maps with DEM information. We chose 90% of the CLDAS temperature data with DEM and station observation data from 2016 to 2020 (excluding 2018) as the training set, 10% as the verification set, and chose the data in 2018 as the test set. We validated the effectiveness of each module through comparative experiments and obtained the best-performing model. Then, we compared it with traditional interpolation methods and state-of-the-art deep learning super-resolution algorithms. We evaluated the experimental results with HRCLDAS, national stations, and regional stations, and the results show that our improved model performs optimally compared to other methods (RMSE of 0.71 °C/0.12 °C/0.72 °C, BIAS of −0.02 °C/0.02 °C/0.002 °C), with the most noticeable improvement in mountainous regions, followed by plains. SNCA-CLDASSDexhibits the most stable performance in intraday hourly bias at temperature under the conditions of improved feature extraction capability in the SNBlock and a better utilization of the DEM by the SCAM. Due to the replacement of the upsampling method from sub pixels to CARAFE, it effectively suppresses the checkerboard effect and shows better robustness than other models. Our approach extends the downscaling model for CLDAS data products and significantly improves performance in this task by enhancing the model’s feature extraction and fusion capabilities and improving upsampling methods. It offers a more profound exploration of historical high-resolution temperature estimation and can be migrated to the downscaling of other meteorological elements. Full article

(This article belongs to the Special Issue Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing)

► Show Figures

Graphical abstract

18 pages, 7694 KiB

Open AccessArticle

An Improved YOLOv5s-Seg Detection and Segmentation Model for the Accurate Identification of Forest Fires Based on UAV Infrared Image

by Kunlong Niu, Chongyang Wang, Jianhui Xu, Chuanxun Yang, Xia Zhou and Xiankun Yang

Remote Sens. 2023, 15(19), 4694; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15194694 - 25 Sep 2023

Cited by 4 | Viewed by 1692

Abstract

With the influence of climate change and human activities, the frequency and scale of forest fires have been increasing continuously, posing a significant threat to the environment and human safety. Therefore, rapid and accurate forest fire detection has become essential for effective control of forest fires. This study proposes a Forest Fire Detection and Segmentation Model (FFDSM) based on unmanned aerial vehicle (UAV) infrared images to address the problems of forest fire occlusion and the poor adaptability of traditional forest fire detection methods. The FFDSM integrates the YOLO (You Only Look Once) v5s-seg, Efficient Channel Attention (ECA), and Spatial Pyramid Pooling Fast Cross-Stage Partial Channel (SPPFCSPC) to improve the detection accuracy of forest fires of different sizes. The FFDSM enhances the detection and extraction capabilities of forest fire features, enabling the accurate segmentation of forest fires of different sizes and shapes. Furthermore, we conducted ablation and controlled experiments on different attention mechanisms, spatial pyramid pooling (SPP) modules, and fire sizes to verify the effectiveness of the added modules and the adaptability of the FFDSM model. The results of the ablation experiment show that, compared to the original YOLOv5s-seg model, the models fused with the ECA and SPPFCSPC achieve an improved accuracy, with FFDSM showing the greatest improvement. FFDSM achieves a 2.1% increase in precision, a 2.7% increase in recall, a 2.3% increase in [email protected], and a 4.2% increase in [email protected]:0.95. The results of the controlled experiments on different attention mechanisms and SPP modules demonstrate that the ECA+SPPFCSPC model (FFDSM) performs the best, with a precision, recall, [email protected], and [email protected]:0.95 reaching 0.959, 0.870, 0.907, and 0.711, respectively. The results of the controlled experiment on different fire sizes show that FFDSM outperforms YOLOv5s-seg for all three fire sizes, and it performs the best for small fires, with a precision, recall, [email protected], and [email protected]:0.95 reaching 0.989, 0.938, 0.964, and 0.769, respectively, indicating its good adaptability for early forest fire detection. The results indicate that the forest fire detection model based on UAV infrared images (FFDSM) proposed in this study exhibits a high detection accuracy. It is proficient in identifying obscured fires in optical images and demonstrates good adaptability in various fire scenarios. The model effectively enables real-time detection and provides early warning of forest fires, providing valuable support for forest fire prevention and scientific decision making. Full article

(This article belongs to the Special Issue Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing)

► Show Figures

Graphical abstract

21 pages, 40607 KiB

Open AccessArticle

Effective Improvement of the Accuracy of Snow Cover Discrimination Using a Random Forests Algorithm Considering Multiple Factors: A Case Study of the Three-Rivers Headwater Region, Tibet Plateau

by Rui He, Yan Qin, Qiudong Zhao, Yaping Chang and Zizhen Jin

Remote Sens. 2023, 15(19), 4644; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15194644 - 22 Sep 2023

Viewed by 1053

Abstract

Accurate information on snow cover extent plays a crucial role in understanding regional and global climate change, as well as the water cycle, and supports the sustainable development of socioeconomic systems. Remote sensing technology is a vital tool for monitoring snow cover’ extent, but accurate identification of shallow snow cover on the Tibetan Plateau has remained challenging. Focusing on the Three-Rivers Headwater Region (THR), this study addressed this issue by developing a snow cover discrimination model (SCDM) using a random forests (RF) algorithm. Using daily observed snow depth (SD) data from 15 stations in the THR during the period 2001–2013, a comprehensive analysis was conducted, considering various factors influencing regional snow cover distribution, such as land surface reflectance, land surface temperature (LST), Normalized Difference Snow Index (NDSI), Normalized Difference Vegetation Index (NDVI), and Normalized Difference Forest Snow Index (NDFSI). The key results were as follows: (1) Optimal model performance was achieved with the parameters Ntree, Mtry, and ratio set to 1000, 2, and 19, respectively. The SCDM outperformed other snow cover products in both pixel-scale and local spatial-scale discrimination. (2) Spectral information of snow cover proved to be the most influential auxiliary variable in discrimination, and the combined inclusion of NDVI and LST improved model performance. (3) The SCDM achieved accuracy of 99.04% for thick snow cover (SD > 4 cm) and 98.54% for shallow snow cover (SD ≤ 4 cm), significantly (p < 0.01) surpassing the traditional dynamic threshold method. This study can offer valuable reference for monitoring snow cover dynamics in regions with limited data availability. Full article

(This article belongs to the Special Issue Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing)

► Show Figures

Figure 1

22 pages, 21564 KiB

Open AccessArticle

A Novel Physics-Statistical Coupled Paradigm for Retrieving Integrated Water Vapor Content Based on Artificial Intelligence

by Ruyu Mei, Kebiao Mao, Jiancheng Shi, Jeffrey Nielson, Sayed M. Bateni, Fei Meng and Guoming Du

Remote Sens. 2023, 15(17), 4250; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15174250 - 30 Aug 2023

Viewed by 1128

Abstract

Retrieval of integrated water vapor content (WVC) from remote sensing data is often ill-posed because of insufficient observational information. There are many factors that cause WVC changes, which yield instability in the accuracy of many traditional algorithms. To overcome this problem, we developed a novel fully-coupled paradigm for the robust retrieval of WVC from thermal infrared remote sensing data. Through the derivation of the physical radiative transfer equation, we determined two conditions that need to be satisfied for the deep learning retrieval paradigm of WVC. The first condition is that the input parameters and output parameters of the deep learning need to be able to build a complete set of solvable equations in theory. The second condition is that, if there is a strong relationship between input parameters and output parameters, it can be directly retrieved. If it is a weak relationship, we need to use prior knowledge to improve the portability and accuracy of the algorithm. The training and test data of deep learning is composed of representative solutions of physical methods and solutions of statistical methods. The representative solutions of the physical methods were obtained from the physical forward model, and the statistical solutions were obtained from multi-source data which can compensate for the defect that the physical model cannot simulate mixed pixels. MODIS L1B data was used for case analysis of paradigm retrieval, and the analysis indicated that four thermal infrared bands were usually needed as the input parameters of deep learning and the integrated water vapor content as the output parameter. When land surface temperature and emissivity were taken as prior knowledge, the root-mean-square error (RMSE) of the retrieved WVC was 0.07 g/cm². The optimal accuracy RMSE was 0.27 g/cm². When there was a strong correlation between input parameters and output parameters, i.e., if there were two bands that were very sensitive to WVC in the band combination, high-precision retrieval could also be achieved without prior knowledge. All the analyses show that the paradigm of deep learning coupling physics and statistics can accurately retrieve WVC, which is a significant improvement on the traditional method and solves the problem of lack of physical interpretation of deep learning. Full article

(This article belongs to the Special Issue Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing)

► Show Figures

Graphical abstract

Other

Jump to: Research

16 pages, 13257 KiB

Open AccessTechnical Note

Drought Monitoring from Fengyun Satellite Series: A Comparative Analysis with Meteorological-Drought Composite Index (MCI)

by Aiqing Feng, Lulu Liu, Guofu Wang, Jian Tang, Xuejun Zhang, Yixiao Chen, Xiangjun He and Ping Liu

Remote Sens. 2023, 15(22), 5410; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15225410 - 18 Nov 2023

Cited by 1 | Viewed by 1035

Abstract

Drought is a complex natural hazard that affects various regions of the world, causing significant economic and environmental losses. Accurate and timely monitoring and forecasting of drought conditions are essential for mitigating their impacts and enhancing resilience. Satellite-based drought indices have the advantage of providing spatially continuous and consistent information on drought severity and extent. A new drought product was developed from the thermal infrared observations of the Fengyun (FY) series of satellites. We proposed a data fusion algorithm to combine multiple FY satellites, including FY-2F, FY-2G, and FY-4A, to create a long time series of a land surface temperature (LST) data set without systematic bias. An FY drought index (FYDI) is then derived by coupling the long-term LST data set with the surface–atmospheric energy exchange model at 4 km spatial resolution over China from 2013 to present. The performance and reliability of the new FYDI product are evaluated in this study by comparing it with the Meteorological-drought Composite Index (MCI), one of the authoritative drought monitoring indices used in the Chinese meteorological services. The main objectives of this paper are: (1) to evaluate the performance of the FYDI in capturing the spatiotemporal patterns of drought events over China; (2) to quantitively analyze the consistency between the FYDI and MCI products; and (3) to explore the advantages and limitations of the FYDI for drought monitoring and assessment. The preliminary results show that the FYDI product has good agreement with the MCI, indicating that the FYDI can effectively identify the occurrence, duration, severity, and frequency of drought events over China. These two products have a strong correlation in terms of drought detection, with a correlation coefficient of approximately 0.7. The FYDI was found to be particularly effective in the regions where ground observation is scarce, with the capability of reflecting the spatial heterogeneity and variability of drought patterns more clearly. Overall, the FYDI can be a useful measure for operational drought monitoring and early warning, complementing the existing ground-based MCI drought indices. Full article

(This article belongs to the Special Issue Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing)

► Show Figures

Graphical abstract

Journal Menu

Journal Browser

Advances in Deep Learning in the Retrieval of Key Parameters of Agrometeorological Remote Sensing

Share This Special Issue

Special Issue Editors

Special Issue Information

Published Papers (7 papers)

Research

Other

Further Information

Guidelines

MDPI Initiatives

Follow MDPI