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

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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 4306

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Dr. Anna Rita Bernadette Cammerino

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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

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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
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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

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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 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 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 (6 papers)

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Research

19 pages, 11440 KB

Open AccessArticle

Cross-Sensor Evaluation of ZY1-02E and ZY1-02D Hyperspectral Satellites for Mapping Soil Organic Matter and Texture in the Black Soil Region

by Kun Shang, He Gu, Hongzhao Tang and Chenchao Xiao

Agronomy 2026, 16(8), 781; https://doi.org/10.3390/agronomy16080781 - 10 Apr 2026

Abstract

Soil health monitoring is critical for the sustainable management of the black soil region, a key resource for global food security. However, traditional field surveys are constrained by high operational costs, limited spatial coverage, and low temporal frequency, making them inadequate for high-resolution and time-sensitive soil monitoring. The recently launched ZY1-02E satellite, equipped with an advanced hyperspectral imager, offers a new potential data source, yet its capability for quantitative soil modelling requires rigorous cross-sensor validation. This study conducts a cross-sensor evaluation of ZY1-02E and its predecessor, ZY1-02D, for mapping soil organic matter (SOM) and soil texture (sand, silt, and clay) in Northeast China. Optimal spectral indices were constructed through exhaustive band combination and correlation screening, and quantitative inversion models were established using a hybrid framework integrating Random Frog feature selection with Gaussian Process Regression (GPR) and Boosting Trees, based on synchronous ground observations. Results demonstrate strong cross-sensor consistency, with spectral indices showing significant linear correlations (

R^{2} > 0.65

) between ZY1-02E and ZY1-02D. Furthermore, the quantitative retrieval models applied to ZY1-02E imagery achieved robust performance, with cross-sensor retrieval consistency exceeding

R^{2} = 0.60

for all parameters and SOM exhibiting the highest agreement (

R^{2} = 0.74

). These findings confirm the radiometric stability and algorithm transferability of ZY1-02E, demonstrating its capability to generate soil parameter products comparable to ZY1-02D without extensive model recalibration. The validated interoperability of the twin-satellite constellation substantially enhances temporal observation capacity during the narrow bare-soil window, effectively mitigating cloud-induced data gaps in high-latitude agricultural regions. Importantly, the enhanced monitoring framework provides a scalable technical paradigm for high-frequency hyperspectral soil mapping, offering critical spatial decision support for precision fertilization, soil degradation mitigation, and conservation tillage management in the Mollisol belt. 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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14 pages, 1174 KB

Open AccessArticle

Chinese Cabbage (Brassica rapa L.) Parameters Describing Agronomic Performances of Fall and Summer Cabbage Ecotypes

by Jungi Hong, Dongwoo Kim, Sojung Kim and Sumin Kim

Agronomy 2026, 16(7), 703; https://doi.org/10.3390/agronomy16070703 - 27 Mar 2026

Viewed by 333

Abstract

Intercepted photosynthetically active radiation (IPAR) to biomass method is widely used in plant growth models to simulate biomass accumulation. This method is closely linked to radiation use efficiency (RUE), which can vary by species, cultivars, and location. Although Chinese cabbage (Brassica rapa L.) is a cool-season plant, it is cultivated year-round in South Korea. Therefore, investigating the RUEs of two different ecotypes of Chinese cabbage is crucial for developing accurate plant growth models. In this study, we examined RUEs and other key agronomic characteristics that influence Chinese cabbage growth across different growing seasons. Field studies were conducted to analyze the growth patterns of fall and summer ecotypes and to explore various agronomic traits for developing a leaf area index (LAI) model. Using a multivariate regression method, we developed an LAI model that simulates the leaf area index for both ecotypes across multiple locations in South Korea (R² = 0.92). A total of 218 field data points collected from 35 sites between 2020 and 2023 were used to estimate the RUEs and LAIs of the fall and summer ecotypes. Results indicated that Chinese cabbage demonstrated more efficient photosynthesis in the fall, with RUEs of 2.3 g MJ⁻¹ for the fall ecotype compared to 1.3 g MJ⁻¹ for the summer ecotype based on regional estimation. This difference may be attributed to lower radiation availability per unit of heat growth during the summer season (0.3 MJ °C⁻¹) compared to the fall (0.78 MJ °C⁻¹). The findings of this study will aid plant modelers and enhance the accuracy of simulations for Chinese cabbage growth. 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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23 pages, 2761 KB

Open AccessArticle

Spatial Modelling of Soil Quality Index Using Regression–Kriging and Delineation of Nutrient Management Zones in High-Andean Quinoa Fields, Southern Peru

by Nestor Cuellar-Condori, Sharon Mejia, Robert Quiñones, Ruth Mercado, Ali Cristhian, Karla Chávez-Zea, Elvis Ccosi, Madeleiny Cahuide and Kenyi Quispe

Agronomy 2026, 16(7), 680; https://doi.org/10.3390/agronomy16070680 - 24 Mar 2026

Viewed by 692

Abstract

The pronounced heterogeneity of high-Andean soils constitutes a critical constraint to the sustainable productivity of quinoa in southern Peru, where current yields (1.6 t ha⁻¹) remain well below potential (>5 t ha⁻¹). This study aimed to develop a spatially predictive model of a weighted soil quality index (SQIw), the edaphic supply of nitrogen (N), phosphorus (P) and potassium (K), and the agricultural gypsum requirement by integrating edaphoclimatic covariates through regression–kriging. A total of 198 quinoa-cultivated soil samples were analysed; a minimum data set (MDS) was defined using correlation and principal component analyses, and regression–kriging was applied to map SQIw and the variables of interest. The MDS comprised electrical conductivity (EC), organic matter (OM), available P, exchangeable Na, sand, clay, and effective cation exchange capacity (ECEC); exchangeable Na (Wi = 0.160) and available P (Wi = 0.158) received the largest weights in the SQIw. SQIw values ranged from 0.22 to 0.84 and supported a five-class soil quality taxonomy; spatial modelling revealed a dominance of moderate-quality soils across the territory (85.21% of the agricultural area, 13,461.19 ha). The model achieved R² = 0.56, RMSE = 0.05, and MAE = 0.04 for SQIw. Most of the area (12,175.65 ha; 77%) exhibited an intermediate gypsum requirement (9.73–14.33 t ha⁻¹). Nitrogen and phosphorus showed the greatest territorial limitations, whereas potassium was largely non-limiting (84.82–570.17 kg ha⁻¹). These results indicate that sodicity and N–P deficiencies are the primary functional constraints; the generated maps enable prioritisation of gypsum amendments and targeted variable-rate fertilisation strategies to optimise the sustainability of quinoa production in the Altiplano. 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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20 pages, 4813 KB

Open AccessArticle

Hybrid Physical–Machine Learning Soil Moisture Modeling at Orchard Scale in Irrigated Citrus Orchards Using Sentinel 1 and 2 and Agroclimatic Data

by Héctor Izquierdo-Sanz and Enrique Moltó

Agronomy 2026, 16(5), 541; https://doi.org/10.3390/agronomy16050541 - 28 Feb 2026

Viewed by 414

Abstract

Accurate orchard-scale soil moisture information is a key requirement for efficient irrigation management in perennial crops such as citrus orchards, particularly in Mediterranean environments characterized by water scarcity and strong spatial and temporal variability in soil moisture, canopy structure, and irrigation scheduling. This study proposes a hybrid physical–machine learning methodology for soil moisture estimation that integrates in situ capacitance sensor measurements, Sentinel-1 SAR observations, Sentinel-2 optical imagery, and ERA5-Land agroclimatic variables. Physically based soil moisture estimates were first obtained through the inversion of Sentinel-1 backscatter using integral equation scattering models, a physically based soil dielectric model, and a simplified vegetation attenuation scheme. These physically derived estimates were subsequently incorporated as predictors within supervised machine learning models, together with multi-source remote sensing and meteorological variables. Several algorithms were evaluated, including regularized linear models, support vector regression, random forests, and gradient boosting methods. Model performance was assessed using a strict interannual validation strategy based on independent-year predictions to ensure robust generalization. Within this methodology, tree-based ensemble models achieved the highest and most consistent performance at the orchard scale, with coefficients of determination ranging from 0.55 to 0.76 and root mean square errors typically between 0.7 and 1.1% volumetric soil moisture in the best-performing cases. Benchmarking against a physical-only baseline demonstrated that the hybrid methodology consistently reduced prediction errors and improved temporal robustness under independent-year validation. Overall, the results demonstrate that hybrid physical–machine learning approaches provide a robust and scalable solution for orchard-scale soil moisture monitoring in irrigated citrus orchards using operational data streams, supporting advanced irrigation management and precision agriculture applications in Mediterranean perennial cropping systems. 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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19 pages, 4088 KB

Open AccessArticle

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

Cited by 1 | Viewed by 509

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 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

Open AccessEditor’s ChoiceArticle

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 1156

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 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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