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AgriEngineering
Special Issues
Application of Remote Sensing and GIS in Agricultural Engineering
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Application of Remote Sensing and GIS in Agricultural Engineering

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Remote Sensing in Agriculture".

Deadline for manuscript submissions: closed (15 November 2024) | Viewed by 5770

Special Issue Editors

Dr. Jiang Chen

E-Mail Website
Guest Editor
Department of Biological Systems Engineering, University of Wisconsin-Madison, Madison, WI 53706, USA
Interests: remote sensing; data fusion; precision agriculture; digital agriculture; carbon-water-crop nexus; global change
Special Issues, Collections and Topics in MDPI journals
Dr. Lorena Nunes Lacerda

E-Mail Website
Guest Editor
Department of Crop & Soil Sciences, The University of Georgia Tifton Campus, Tifton, GA, USA
Interests: precision agricultural management systems; UAV; satellite remote sensing
Dr. Lirong Xiang

E-Mail Website
Guest Editor
Department of Biological and Agricultural Engineering, North Carolina State University, Raleigh, NC, USA
Interests: agricultural robotics; plant phenotyping; machine vision; digital twin; automation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Currently, global food security is increasingly threatened by many uncertainties, such as drought, flood, heat wave, and air pollution. It is well recognized that the traditional means of agricultural production cannot meet the growing demand for high-quality food around the world. Fortunately, precision agriculture management and agricultural engineering applications with remote sensing and GIS provide a hopeful way of capturing crop growth. Recently, many new technologies (e.g., deep learning) and multisource satellite remote sensing data (e.g., Landsat, Sentinel-1/2, and Planet) are drawing more and more attention for practical application in agricultural engineering. In particular, crop growth is largely influenced by the surrounding water and heat conditions, while the carbon in the atmosphere is absorbed through the stage of crop growth. This means that agriculture production is an important bridge connecting carbon and water dynamics across the agroecosystem. Therefore, to advance the understanding of the role of remote sensing and GIS in agricultural engineering, it is necessary to (1) monitor and manage agriculture production using multisource satellite remote sensing images with advanced deep learning algorithms; (2) capture and quantify the carbon and water parameters during agriculture production; and (3) evaluate the impact of different water and heat conditions on agriculture production.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Precision agriculture;
  • Digital agriculture;
  • Global food security monitoring;
  • Fusing multisource satellite remote sensing images;
  • Deep learning model application;
  • Agricultural engineering.

I/We look forward to receiving your contributions.

Dr. Jiang Chen
Dr. Lorena Nunes Lacerda
Dr. Lirong Xiang
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. AgriEngineering is an international peer-reviewed open access quarterly 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 1600 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

  • food security
  • multisource satellite remote sensing
  • GIS technologies
  • deep learning
  • agricultural engineering
  • agroecosystem management

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

Published Papers (3 papers)

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Research

31 pages, 10669 KiB  
Article
Spatio-Temporal Modeling of Land and Pasture Vulnerability in Dairy Basins in Northeastern Brazil
by Jéssica Bruna Alves da Silva, Gledson Luiz Pontes de Almeida, Marcos Vinícius da Silva, José Francisco de Oliveira-Júnior, Héliton Pandorfi, Pedro Rogerio Giongo, Gleidiana Amélia Pontes de Almeida Macêdo, Cristiane Guiselini, Gabriel Thales Barboza Marinho, Ivonete Alves Bakke and Maria Beatriz Ferreira
AgriEngineering 2024, 6(3), 2970-3000; https://doi.org/10.3390/agriengineering6030171 - 20 Aug 2024
Viewed by 696
Abstract
The objective of this study is to evaluate the spatio-temporal dynamics of land vulnerability and pasture areas in the dairy basins of the states of Pernambuco and Alagoas, which are part of the Ipanema River Watershed (IRW) in the Northeast Region of Brazil. [...] Read more.
The objective of this study is to evaluate the spatio-temporal dynamics of land vulnerability and pasture areas in the dairy basins of the states of Pernambuco and Alagoas, which are part of the Ipanema River Watershed (IRW) in the Northeast Region of Brazil. Maps of the Land Use and Land Cover (LULC); the Index of Vulnerability to Degradation (IVD); the Land Vulnerability Index (LVI); time series of Effective Herd (EH), Milked Cows (MC), and Milk Production (MP); and Pasture Cover (PC) and Quality (PCQ) were created as parameters. An opposite pattern was observed between the land use classes of Livestock, Agriculture, and Forest. The IRW area has predominantly flat terrain with a very high risk of degradation. The analysis of MC was consistent with the information from the EH analysis as well as with MP. When assessing Pasture Quality, Severe Degradation areas increased from 2010 to 2014, decreased after 2015, and rose again in 2020. Moderate Degradation areas remained high, while Not Degraded pasture areas were consistently the lowest from 2012 to 2020. Over the 10 years analyzed (2010–2020), the area showed a strong degradation process, with the loss of approximately 16% of the native vegetation of the Caatinga Biome and an increase in pasture areas and land vulnerability. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Agricultural Engineering)
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16 pages, 3339 KiB  
Article
Localized Crop Classification by NDVI Time Series Analysis of Remote Sensing Satellite Data; Applications for Mechanization Strategy and Integrated Resource Management
by Hafiz Md-Tahir, Hafiz Sultan Mahmood, Muzammil Husain, Ayesha Khalil, Muhammad Shoaib, Mahmood Ali, Muhammad Mohsin Ali, Muhammad Tasawar, Yasir Ali Khan, Usman Khalid Awan and Muhammad Jehanzeb Masud Cheema
AgriEngineering 2024, 6(3), 2429-2444; https://doi.org/10.3390/agriengineering6030142 - 26 Jul 2024
Viewed by 2310
Abstract
In data-scarce regions, prudent planning and precise decision-making for sustainable development, especially in agriculture, remain challenging due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use and land cover (LULC) classes and crop identification. [...] Read more.
In data-scarce regions, prudent planning and precise decision-making for sustainable development, especially in agriculture, remain challenging due to the lack of correct information. Remotely sensed satellite images provide a powerful source for assessing land use and land cover (LULC) classes and crop identification. Applying remote sensing (RS) in conjunction with the Geographical Information System (GIS) and modern tools/algorithms of artificial intelligence (AI) and deep learning has been proven effective for strategic planning and integrated resource management. The study was conducted in the canal command area of the Lower Chenab Canal system in Punjab, Pakistan. Crop features/classes were assessed using the Normalized Difference Vegetation Index (NDVI) algorithm. The Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m and Landsat 5 TM (thematic mapper) images were deployed for NDVI time-series analysis with an unsupervised classification technique to obtain LULC classes that helped to discern cropping pattern, crop rotation, and the area of specific crops, which were then used as key inputs for agricultural mechanization planning and resource management. The accuracy of the LULC map was 78%, as assessed by the error matrix approach. Limitations of high-resolution RS data availability and the accuracy of the results are the concerns observed in this study that could be managed by the availability of good quality local sources and advanced processing techniques, that would make it more useful and applicable for regional agriculture and environmental management. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Agricultural Engineering)
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24 pages, 5953 KiB  
Article
Synergetic Use of Sentinel-1 and Sentinel-2 Data for Wheat-Crop Height Monitoring Using Machine Learning
by Lwandile Nduku, Cilence Munghemezulu, Zinhle Mashaba-Munghemezulu, Phathutshedzo Eugene Ratshiedana, Sipho Sibanda and Johannes George Chirima
AgriEngineering 2024, 6(2), 1093-1116; https://doi.org/10.3390/agriengineering6020063 - 22 Apr 2024
Cited by 1 | Viewed by 1647
Abstract
Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, [...] Read more.
Monitoring crop height during different growth stages provides farmers with valuable information important for managing and improving expected yields. The use of synthetic aperture radar Sentinel-1 (S-1) and Optical Sentinel-2 (S-2) satellites provides useful datasets that can assist in monitoring crop development. However, studies exploring synergetic use of SAR S-1 and optical S-2 satellite data for monitoring crop biophysical parameters are limited. We utilized a time-series of monthly S-1 satellite data independently and then used S-1 and S-2 satellite data synergistically to model wheat-crop height in this study. The polarization backscatter bands, S-1 polarization indices, and S-2 spectral indices were computed from the datasets. Optimized Random Forest Regression (RFR), Support Vector Machine Regression (SVMR), Decision Tree Regression (DTR), and Neural Network Regression (NNR) machine-learning algorithms were applied. The findings show that RFR (R2 = 0.56, RMSE = 21.01 cm) and SVM (R2 = 0.58, RMSE = 20.41 cm) produce a low modeling accuracy for crop height estimation with S-1 SAR data. The S-1 and S-2 satellite data fusion experiment had an improvement in accuracy with the RFR (R2 = 0.93 and RMSE = 8.53 cm) model outperforming the SVM (R2 = 0.91 and RMSE = 9.20 cm) and other models. Normalized polarization (Pol) and the radar vegetation index (RVI_S1) were important predictor variables for crop height retrieval compared to other variables with S-1 and S-2 data fusion as input features. The SAR ratio index (SAR RI 2) had a strong positive and significant correlation (r = 0.94; p < 0.05) with crop height amongst the predictor variables. The spatial distribution maps generated in this study show the viability of data fusion to produce accurate crop height variability maps with machine-learning algorithms. These results demonstrate that both RFR and SVM can be used to quantify crop height during the growing stages. Furthermore, findings show that data fusion improves model performance significantly. The framework from this study can be used as a tool to retrieve other wheat biophysical variables and support decision making for different crops. Full article
(This article belongs to the Special Issue Application of Remote Sensing and GIS in Agricultural Engineering)
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