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Agronomy
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Advances in Remote Sensing Agronomic Application for Mapping and Modeling...
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Advances in Remote Sensing Agronomic Application for Mapping and Modeling Soil Properties

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 20 July 2026 | Viewed by 1712

Special Issue Editors

Dr. Anna Rita Bernadette Cammerino

E-Mail Website
Guest Editor
Department of Science of Agriculture, Food and Environment, University of Foggia, 71122 Foggia, Italy
Interests: geographic information system; remote sensing; land planning; agroecology; soil quality
Prof. Dr. Massimo Monteleone

E-Mail Website
Guest Editor
Department of Agriculture, Food and Environment, University of Foggia, 71122 Foggia, Italy
Interests: agroecology; crop ecology; land planning; resource use in agriculture; bioenergy for and from agriculture; bio-base economy; circular economy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil plays a crucial role as a natural resource that sustains life on Earth, providing a wide range of ecosystem services, such as the production of food, recycling of nutrients, sequestration of carbon, and provision of habitat. Global warming, land use and land cover changes, and unsustainable agricultural practises contribute to accelerated soil quality loss. In spatial explicit analysis related to both agricultural and environmental issues, soil is one of the most important criteria to be considered. Although the assessment of soil properties is key to monitor soil health, data availability is scarce. Accordingly, in recent years, the potential of modern technologies and advanced methods like remote sensing has been widely investigated for mapping and monitoring soil properties. However, the obtained accuracies in previous research have varied, largely depending on various factors such as the spatial/spectral resolutions of sensors, the used methodology, and the study sites. In this context, the identification of new, reliable remote sensing techniques to monitor soil health and model soil dynamics at different spatial scale becomes extremely important. We kindly invite authors to submit original research articles or review articles on topics related to the mapping and modelling of soil salinity, organic matter content, moisture, and all other soil properties using remote sensing technology.

Dr. Anna Rita Bernadette Cammerino
Prof. Dr. Massimo Monteleone
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 250 words) can be sent to the Editorial Office for assessment.

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. Agronomy is an international peer-reviewed open access monthly 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 2600 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
  • mapping soil properties
  • modelling soil properties
  • agro-environmental application
  • multicriteria analysis

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Published Papers (2 papers)

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19 pages, 4088 KB  
Article
Research on Spatiotemporal Combination Optimization of Remote Sensing Mapping of Farmland Soil Organic Matter Considering Annual Variability
by Wenzhu Dou, Wenqi Zhang, Shiyu He, Xue Li and Chong Luo
Agronomy 2025, 15(12), 2714; https://doi.org/10.3390/agronomy15122714 - 25 Nov 2025
Viewed by 184
Abstract
Soil organic matter (SOM) is a key indicator of cropland quality and carbon cycling. Accurate SOM mapping is essential for sustainable soil management and carbon sink assessment. This study investigated the effects of interannual climatic variability on SOM prediction using remote sensing and [...] Read more.
Soil organic matter (SOM) is a key indicator of cropland quality and carbon cycling. Accurate SOM mapping is essential for sustainable soil management and carbon sink assessment. This study investigated the effects of interannual climatic variability on SOM prediction using remote sensing and machine learning. Youyi Farm in the Sanjiang Plain, Heilongjiang Province, was selected as the study area, covering three representative years: 2019 (flood), 2020 (normal), and 2021 (drought). Based on multi-temporal Sentinel-2 imagery and environmental covariates, Random Forest models were used to evaluate single- and dual-period combinations. Results showed that combining bare-soil and crop-season images consistently improved accuracy, with optimal combinations varying by year (R2 = 0.544–0.609). Incorporating temperature, precipitation, and elevation enhanced model performance, particularly temperature, which contributed most to prediction accuracy. Feature selection further improved model stability and generalization. Spatially, SOM showed a pattern of higher values in the northeast and lower in the central region, shaped by topography and cultivation. This study innovatively integrates interannual climatic variability with remote sensing temporal combination and feature selection, constructing a climate-adaptive SOM mapping framework and providing new insights for accurate inversion of cropland SOM under extreme climates, highlights the importance of multi-temporal imagery, environmental factors, and feature selection for robust SOM mapping under different climatic conditions, providing technical support for long-term cropland quality monitoring. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Agronomic Application for Mapping and Modeling Soil Properties)
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17 pages, 2867 KB  
Article
Onion Yield Analysis Using a Satellite Image-Based Soil Moisture Prediction Model
by Junyoung Seo, Sumin Kim and Sojung Kim
Agronomy 2025, 15(11), 2479; https://doi.org/10.3390/agronomy15112479 - 25 Oct 2025
Viewed by 671
Abstract
From 2020 to 2021, crop production increased by 54% globally, and the popularity of commercial agriculture to increase profitability is gradually increasing. However, global warming and climate issues make it difficult to maintain stable crop production. To improve crop production efficiency, techniques for [...] Read more.
From 2020 to 2021, crop production increased by 54% globally, and the popularity of commercial agriculture to increase profitability is gradually increasing. However, global warming and climate issues make it difficult to maintain stable crop production. To improve crop production efficiency, techniques for efficiently managing large-scale commercial farmland are needed. This study proposes a satellite image-based soil moisture and onion yield prediction technique as a methodology for managing large-scale farmland. This preemptive soil moisture management technique effectively manages increased soil pressure, resulting in soil drying due to rising temperatures. To remotely identify agricultural land, vegetation indices were extracted from satellite image data, and K-means clustering was applied. Ensemble machine learning is performed on soil images collected from satellite images. This model combines soil physical properties with soil environmental factor information to develop a model. The results show that soil color information obtained from satellite images is highly correlated with soil organic matter content. The proposed model is validated using crop yield data and environmental factor data obtained from actual crop production experiments. Consequently, the proposed methodology can be effectively applied to manage large-scale farmland and enables decision-making to improve profitability. Full article
(This article belongs to the Special Issue Advances in Remote Sensing Agronomic Application for Mapping and Modeling Soil Properties)
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