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Remote Sensing
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Recent Progress in GIS and Remote Sensing for Agricultural Applications
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Recent Progress in GIS and Remote Sensing for Agricultural Applications

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

Deadline for manuscript submissions: closed (30 November 2023) | Viewed by 13328

Special Issue Editors

Dr. Li Lin

E-Mail Website
Guest Editor
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
Interests: pollution and natural dissaster; crop mapping; geospatial service discovery and integration; economic GIS
Special Issues, Collections and Topics in MDPI journals
Dr. Chen Zhang

E-Mail Website
Guest Editor
Center for Spatial Information Science and Systems, George Mason University, Fairfax, VA 22030, USA
Interests: remote sensing; agro-geoinformatics; environmental modeling; geospatial information interoperability and standards; cyberinfrastructure; digital twin; AI/machine learning; image processing and analysis; pattern recognition; crop mapping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Liping Di

E-Mail Website
Guest Editor
Center for Spatial Information Science and Systems, George Mason University, 4400 University Drive, MSN 6E1, George Mason University, Fairfax, VA 22030, USA
Interests: earth system science; geospatial information science; agro-geoinformatics; geospatial web service; spatial data infrastructure; geospatial data catalog; interoperability standard; agricultural drought monitoring and forecasting
Special Issues, Collections and Topics in MDPI journals
Dr. Zhengwei Yang

E-Mail Website
Guest Editor
United States Department of Agriculture, National Agricultural Statistics Service (NASS), Washington, DC, USA
Interests: spatial statistics; spatial analysis; satellite amage analysis; geoinformation; satellite image processing; geostatistical analysis; geospatial science; decision support systems; object recognition; image registration; earth observation; satellite data; vegetation mapping; remote sensing; web services; web GIS; geographic information system; machine learning; classification; vegetation; land cover land use change

Special Issue Information

Dear Colleagues,

Monitoring agricultural activities can provide valuable information to farmers, investors, and decision makers. In recent years, space-based crop identification and monitoring have played a significant role in agricultural studies due to the development of Remote Sensing and GIS technology. Moreover, recent progress in digital technologies, such as cloud computing, data fusion, deep learning, artificial intelligence, social media, and cyber-infrastructure, has enhanced the spatial and temporal scale of crop mapping with new tools and methods for facilitating and promoting new innovative approaches in agriculture. This Special Issue will enable papers exploring the progress in innovative crop mapping and monitoring research and applications to be published.

Papers that are suitable for the Special Issue must address relevant topics in agricultural mapping and monitoring and include sound implementation and validation procedures. We welcome submissions that describe state-of-the-art approaches in the progress in GIS and remote sensing for agricultural-related applications, including, but not limited to, the following:

  • Research on remote sensing crop mapping theory, methodology, and practices;
  • Geospatial information for stratification and sampling;
  • Data fusion, calibration, validation, and ground truths for agricultural monitoring;
  • Crop mapping, condition monitoring, acreage estimation, and yield modeling;
  • Cropland evapotranspiration, soil moisture, and drought monitoring and assessment;
  • Remote sensing-based agricultural disaster monitoring, assessment, mitigation, and emergency response;
  • Global climate and environmental change and its impacts on agriculture sustainability and food security;
  • Remote sensing monitoring and modeling on agricultural greenhouse gases;
  • Agricultural environment and public health;
  • Crop/plant disease detection, monitoring, and assessment;
  • Cloud computing and big data in agriculture-related applications;
  • Agricultural land use and land cover change;
  • Agricultural deep learning and artificial intelligence technology;
  • Spatial data uncertainty analysis.

Dr. Li Lin
Dr. Chen Zhang
Prof. Dr. Liping Di
Dr. Zhengwei Yang
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

  • remote sensing
  • GIS
  • crop mapping
  • crop modeling
  • crop yield estimation
  • data processing
  • agricultural monitoring
  • AI and machine learning
  • spatial-temporal analysis

Related Special Issue

Published Papers (5 papers)

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Research

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20 pages, 8917 KiB  
Article
TTNet: A Temporal-Transform Network for Semantic Change Detection Based on Bi-Temporal Remote Sensing Images
by Liangcun Jiang, Feng Li, Li Huang, Feifei Peng and Lei Hu
Remote Sens. 2023, 15(18), 4555; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15184555 - 15 Sep 2023
Cited by 1 | Viewed by 1144
Abstract
Semantic change detection (SCD) holds a critical place in remote sensing image interpretation, as it aims to locate changing regions and identify their associated land cover classes. Presently, post-classification techniques stand as the predominant strategy for SCD due to their simplicity and efficacy. [...] Read more.
Semantic change detection (SCD) holds a critical place in remote sensing image interpretation, as it aims to locate changing regions and identify their associated land cover classes. Presently, post-classification techniques stand as the predominant strategy for SCD due to their simplicity and efficacy. However, these methods often overlook the intricate relationships between alterations in land cover. In this paper, we argue that comprehending the interplay of changes within land cover maps holds the key to enhancing SCD’s performance. With this insight, a Temporal-Transform Module (TTM) is designed to capture change relationships across temporal dimensions. TTM selectively aggregates features across all temporal images, enhancing the unique features of each temporal image at distinct pixels. Moreover, we build a Temporal-Transform Network (TTNet) for SCD, comprising two semantic segmentation branches and a binary change detection branch. TTM is embedded into the decoder of each semantic segmentation branch, thus enabling TTNet to obtain better land cover classification results. Experimental results on the SECOND dataset show that TTNet achieves enhanced performance when compared to other benchmark methods in the SCD task. In particular, TTNet elevates mIoU accuracy by a minimum of 1.5% in the SCD task and 3.1% in the semantic segmentation task. Full article
(This article belongs to the Special Issue Recent Progress in GIS and Remote Sensing for Agricultural Applications)
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18 pages, 3945 KiB  
Article
Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model
by El houssaine Bouras, Per-Ola Olsson, Shangharsha Thapa, Jesús Mallol Díaz, Johannes Albertsson and Lars Eklundh
Remote Sens. 2023, 15(18), 4425; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15184425 - 08 Sep 2023
Cited by 3 | Viewed by 1667
Abstract
Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially [...] Read more.
Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production. Full article
(This article belongs to the Special Issue Recent Progress in GIS and Remote Sensing for Agricultural Applications)
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16 pages, 25178 KiB  
Article
Rapid Rice Yield Estimation Using Integrated Remote Sensing and Meteorological Data and Machine Learning
by Md Didarul Islam, Liping Di, Faisal Mueen Qamer, Sravan Shrestha, Liying Guo, Li Lin, Timothy J. Mayer and Aparna R. Phalke
Remote Sens. 2023, 15(9), 2374; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15092374 - 30 Apr 2023
Cited by 7 | Viewed by 3541
Abstract
This study developed a rapid rice yield estimation workflow and customized yield prediction model by integrating remote sensing and meteorological data with machine learning (ML). Several issues need to be addressed while developing a crop yield estimation model, including data quality issues, data [...] Read more.
This study developed a rapid rice yield estimation workflow and customized yield prediction model by integrating remote sensing and meteorological data with machine learning (ML). Several issues need to be addressed while developing a crop yield estimation model, including data quality issues, data processing issues, selecting a suitable machine learning model that can learn from few available time-series data, and understanding the non-linear relationship between historical crop yield and remote sensing and meteorological factors. This study applied a series of data processing techniques and a customized ML model to improve the accuracy of crop yield estimation at the district level in Nepal. It was found that remote sensing-derived NDVI product alone was not sufficient for accurate estimation of crop yield. After incorporating other meteorological variables into the ML models, estimation accuracy improved dramatically. Along with NDVI, the meteorological variables of rainfall, soil moisture, and evapotranspiration also exhibited a strong association with rice yield. This study also found that stacking multiple tree-based regression models together could achieve better accuracy than benchmark linear regression or standalone ML models. Due to the unique and distinct physio-geographical setting of each district, a variation in estimation accuracy from district to district could be observed. Our data processing and ML model workflow achieved an average of 92% accuracy of yield estimation with RMSE 328.06 kg/ha and MAE 317.21 kg/ha. This methodological workflow can be replicated in other study areas and the results can help the local authorities and stakeholders understand the factors affecting crop yields as well as estimating crop yield before harvesting season to ensure food security and sustainability. Full article
(This article belongs to the Special Issue Recent Progress in GIS and Remote Sensing for Agricultural Applications)
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18 pages, 13195 KiB  
Article
Evaluation and Comparison of Semantic Segmentation Networks for Rice Identification Based on Sentinel-2 Imagery
by Huiyao Xu, Jia Song and Yunqiang Zhu
Remote Sens. 2023, 15(6), 1499; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15061499 - 08 Mar 2023
Cited by 3 | Viewed by 2393
Abstract
Efficient and accurate rice identification based on high spatial and temporal resolution remote sensing imagery is essential for achieving precision agriculture and ensuring food security. Semantic segmentation networks in deep learning are an effective solution for crop identification, and they are mainly based [...] Read more.
Efficient and accurate rice identification based on high spatial and temporal resolution remote sensing imagery is essential for achieving precision agriculture and ensuring food security. Semantic segmentation networks in deep learning are an effective solution for crop identification, and they are mainly based on two architectures: the commonly used convolutional neural network (CNN) architecture and the novel Vision Transformer architecture. Research on crop identification from remote sensing imagery using Vision Transformer has only emerged in recent times, mostly in sub-meter resolution or even higher resolution imagery. Sub-meter resolution images are not suitable for large scale crop identification as they are difficult to obtain. Therefore, studying and analyzing the differences between Vision Transformer and CNN in crop identification in the meter resolution images can validate the generalizability of Vision Transformer and provide new ideas for model selection in crop identification research at large scale. This paper compares the performance of two representative CNN networks (U-Net and DeepLab v3) and a novel Vision Transformer network (Swin Transformer) on rice identification in Sentinel-2 of 10 m resolution. The results show that the three networks have different characteristics: (1) Swin Transformer has the highest rice identification accuracy and good farmland boundary segmentation ability. Although Swin Transformer has the largest number of model parameters, the training time is shorter than DeepLab v3, indicating that Swin Transformer has good computational efficiency. (2) DeepLab v3 also has good accuracy in rice identification. However, the boundaries of the rice fields identified by DeepLab v3 tend to shift towards the upper left corner. (3) U-Net takes the shortest time for both training and prediction and is able to segment the farmland boundaries accurately for correctly identified rice fields. However, U-Net’s accuracy of rice identification is lowest, and rice is easily confused with soybean, corn, sweet potato and cotton in the prediction. The results reveal that the Vision Transformer network has great potential for identifying crops at the country or even global scale. Full article
(This article belongs to the Special Issue Recent Progress in GIS and Remote Sensing for Agricultural Applications)
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Review

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33 pages, 2047 KiB  
Review
Enhancing FAIR Data Services in Agricultural Disaster: A Review
by Lei Hu, Chenxiao Zhang, Mingda Zhang, Yuming Shi, Jiasheng Lu and Zhe Fang
Remote Sens. 2023, 15(8), 2024; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15082024 - 11 Apr 2023
Cited by 5 | Viewed by 3469
Abstract
The agriculture sector is highly vulnerable to natural disasters and climate change, leading to severe impacts on food security, economic stability, and rural livelihoods. The use of geospatial information and technology has been recognized as a valuable tool to help farmers reduce the [...] Read more.
The agriculture sector is highly vulnerable to natural disasters and climate change, leading to severe impacts on food security, economic stability, and rural livelihoods. The use of geospatial information and technology has been recognized as a valuable tool to help farmers reduce the adverse impacts of natural disasters on agriculture. Remote sensing and GIS are gaining traction as ways to improve agricultural disaster response due to recent advancements in spatial resolution, accessibility, and affordability. This paper presents a comprehensive overview of the FAIR agricultural disaster services. It holistically introduces the current status, case studies, technologies, and challenges, and it provides a big picture of exploring geospatial applications for agricultural disaster “from farm to space”. The review begins with an overview of the governments and organizations worldwide. We present the major international and national initiatives relevant to the agricultural disaster context. The second part of this review illustrates recent research on remote sensing-based agricultural disaster monitoring, with a special focus on drought and flood events. Traditional, integrative, and machine learning-based methods are highlighted in this section. We then examine the role of spatial data infrastructure and research on agricultural disaster services and systems. The generic lifecycle of agricultural disasters is briefly introduced. Eventually, we discuss the grand challenges and emerging opportunities that range from analysis-ready data to decision-ready services, providing guidance on the foreseeable future. Full article
(This article belongs to the Special Issue Recent Progress in GIS and Remote Sensing for Agricultural Applications)
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