sustainability-logo

Journal Browser

Journal Browser

Applications of Remote Sensing Technology in Agricultural Sustainable Development

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainability in Geographic Science".

Deadline for manuscript submissions: 31 January 2025 | Viewed by 830

Special Issue Editors


E-Mail Website
Guest Editor
Department of Metrology and Modelling of Agrophysical Processes, Institute of Agrophysics, Polish Academy of Sciences, Doświadczalna 4, 20-290 Lublin, Poland
Interests: climate change; climate change adaptation; crop growth and yield prediction; Earth observation; remote sensing in agriculture; spectral data analysis; multifractality of time series; forecasting
Special Issues, Collections and Topics in MDPI journals

E-Mail Website
Guest Editor
Faculty of Agriculture and Biotechnology, Bydgoszcz University of Science and Technology, Bernardyńska 6/8, 85-029 Bydgoszcz, Poland
Interests: irrigation; metrology; climatology; climate change impacts on agriculture

E-Mail Website
Guest Editor
Department of Agricultural and Environmental Chemistry, University of Life Sciences, Akademicka 15, 20-033 Lublin, Poland
Interests: soil hysicochemical properties; macronutrients in soil; soil organic carbon; soil nitrogen; fruit and crop yield quality; remote sensing in agriculture

Special Issue Information

Dear Colleagues,

With the global population expected to reach 9.8 billion by 2050 and considering the challenges posed by climate change, achieving sustainable agricultural development is imperative. The integration of artificial intelligence methods with remote sensing data enables smart farming systems to efficiently track, predict, and manage crop productivity, resources, and environmental conditions. The diverse and prominent applications of remote sensing in agriculture range from estimating crop yield and quality to monitoring crop health, identifying varieties more phenotypically suited to the current conditions, controlling pests and diseases, optimizing irrigation management, and enhancing post-harvest quality. Additionally, remote sensing facilitates ecosystem service provision related to soil or water resources, plant and animal biodiversity screening, crop and land monitoring, and precision farming. By leveraging remote sensing data, farmers can make informed decisions regarding planting, fertilization, and pest management, leading to increased efficiency, reduced resource usage, and improved crop yields. However, despite the potential benefits, challenges such as technical knowledge requirements, data reproducibility, and the storage of big data remain to be addressed.

Therefore, the journal Sustainability (ISSN: 2071-1050, IF 3.9, Citescore 5.8) has decided to publish a Special Issue entitled “Applications of Remote Sensing Technology in Agricultural Sustainable Development”, for which I am Guest Editor. This Special Issue aims to explore the profound impacts of remote sensing technology in revolutionizing agricultural practices and moving them towards sustainability and efficiency. In this Special Issue, original research articles and reviews that delve into various aspects of remote sensing applications in agriculture are welcome. Research areas may include (but are not limited to) the following:

  • Estimation of crop yield and quality;
  • Monitoring of crop health and disease control;
  • Precision agriculture techniques;
  • Soil and moisture monitoring for efficient irrigation management;
  • Weather pattern analysis for optimal farming strategies;
  • Post-harvest quality monitoring and management.

We look forward to receiving your valuable contributions.

Dr. Jaromir Krzyszczak
Dr. Renata Kuśmierek-Tomaszewska
Prof. Dr. Przemysław Tkaczyk
Dr. Carla Sofia Santos Ferreira
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. Sustainability 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 2400 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
  • agriculture
  • sustainable development
  • precision farming
  • crop yield and quality prediction
  • irrigation management

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

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

Published Papers (1 paper)

Order results
Result details
Select all
Export citation of selected articles as:

Research

14 pages, 2248 KiB  
Article
Design and Use of a Stratum-Based Yield Predictions to Address Challenges Associated with Spatial Heterogeneity and Sample Clustering in Agricultural Fields Using Remote Sensing Data
by Keltoum Khechba, Ahmed Laamrani, Mariana Belgiu, Alfred Stein, Qi Dong and Abdelghani Chehbouni
Sustainability 2024, 16(21), 9196; https://doi.org/10.3390/su16219196 - 23 Oct 2024
Viewed by 485
Abstract
Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. [...] Read more.
Machine learning (ML) models trained with remote sensing data have the potential to improve cereal yield estimation across various geographic scales. However, the complexity and heterogeneity of agricultural landscapes present significant challenges to the robustness of ML-based field-level yield estimation over large areas. In our study, we propose decomposing the landscape complexity into homogeneous zones using existing landform, agroecological, and climate classification datasets, and subsequently applying stratum-based ML to estimate cereal yield. This approach was tested in a heterogeneous region in northern Morocco, where wheat is the dominant crop. We compared the results of the stratum-based ML with those applied to the entire study area. Sentinel-1 and Sentinel-2 satellite imagery were used as input variables to train three ML models: Random Forest, Extreme Gradient Boosting (XGBoost), and Multiple Linear Regression. The results showed that the XGBoost model outperformed the other assessed models. Furthermore, the stratum-based ML approach significantly improved the yield estimation accuracy, particularly when using landform classifications as homogeneous strata. For example, the accuracy of XGBoost model improved from R2 = 0.58 and RMSE = 840 kg ha−1 when the ML models were trained on data from the entire study area to R2 = 0.72 and RMSE = 809 kg ha−1 when trained in the plain area. These findings highlight that developing stratum-based ML models using landform classification as strata leads to more accurate predictions by allowing the models to better capture local environmental conditions and agricultural practices that affect crop growth. Full article
Show Figures

Figure 1

Back to TopTop