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Soil Sensing and Mapping in Precision Agriculture: 2nd Edition
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Soil Sensing and Mapping in Precision Agriculture: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 2491

Special Issue Editors

Prof. Dr. Francisco Jesús Moral García

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Guest Editor
Departamento de Expresión Gráfica, Escuela de Ingenierías Industriales, Universidad de Extremadura, Badajoz, Spain
Interests: precision agriculture; probabilistic models; sensing; GIS
Special Issues, Collections and Topics in MDPI journals
Dr. João Manuel Pereira Ramalho Serrano

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Guest Editor
MED—Mediterranean Institute for Agriculture, Environment and Development and CHANGE—Global Change and Sustainability Institute, Universidade de Évora, Évora, Portugal
Interests: agricultural mechanization; precision agriculture; sensors; agro-silvo-pastoral systems
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Fernando Carvajal-Ramírez

E-Mail Website
Guest Editor
Department of Engineering, University of Almería, 04120 Almería, Spain
Interests: sensing; GIS; multispectral imagery; unmanned aerial vehicles (UAVs)
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue welcomes studies focusing on the use of proximal or remote soil sensing techniques for obtaining information related to any soil property and/or digital mapping, allowing, for example, the definition of homogeneous zones and promoting the use of the precision agriculture approach. Manuscripts may consider applications in agricultural or pasture and grassland fields. The following are examples of suitable topics: methods for the collection of soil and soil-related data, data modelling, the interpretation and elaboration of focused soil and/or plant information, and the application of soil and/or plant information in sectors (agriculture, forestry, natural resource management, climate change mitigation, etc.) or systems at different spatial levels, providing a basis for the implementation of field differentiated management, for example, through variable-rate technology (soil amendment, soil fertilization, etc.).

Particular interest will be paid to research using and/or developing novel data integration techniques, studies using novel proximal or remotely sensed data, and research outcomes for local stakeholders, including GIS-based planning and decision support tools.

Prof. Dr. Francisco Jesús Moral García
Dr. João Manuel Pereira Ramalho Serrano
Prof. Dr. Fernando Carvajal-Ramírez
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. Sensors 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

  • proximal soil sensing
  • remote soil sensing
  • digital soil mapping
  • precision agriculture
  • management zones

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Related Special Issue

Published Papers (3 papers)

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Research

13 pages, 2167 KB  
Article
Low-Cost Portable Near-Infrared Spectroscopy for Predicting Soil Properties in Paddy Fields of Southeastern China
by Minwei Li, Yechen Jin, Hancheng Guo, Dietian Yu, Jianping Qian, Qiangyi Yu, Zhou Shi and Songchao Chen
Sensors 2026, 26(6), 1805; https://doi.org/10.3390/s26061805 - 12 Mar 2026
Viewed by 1271
Abstract
Timely and accurate soil property information is critical for sustainable agriculture and precision nutrient management. Conventional laboratory methods are accurate but costly and labor-intensive, restricting their feasibility for high-density soil mapping. Low-cost, portable near-infrared (NIR) spectroscopy presents a promising alternative for rapid, on-site, [...] Read more.
Timely and accurate soil property information is critical for sustainable agriculture and precision nutrient management. Conventional laboratory methods are accurate but costly and labor-intensive, restricting their feasibility for high-density soil mapping. Low-cost, portable near-infrared (NIR) spectroscopy presents a promising alternative for rapid, on-site, and non-destructive soil analysis. This study aimed to evaluate the potential of a low-cost, portable NIR sensor (NeoSpectra) for the quantitative prediction of key soil properties in paddy fields from Southeastern China. The target properties were soil organic matter (SOM), total nitrogen (TN), pH, and particle size fractions (clay, silt, and sand). A total of 995 soil samples were collected from representative paddy fields in the region and spectra measurements were conducted in the laboratory on air-dried samples. We developed and compared the performance of multiple machine learning algorithms, including partial least squares regression (PLSR), Cubist, random forest (RF) and memory-based learning (MBL), to build robust calibration models. The predictive models showed substantial performance for SOM and TN, indicating high accuracy (R2 > 0.75, LCCC > 0.85, RPD > 2) for quantitative prediction. Predictions for pH, silt, sand, and clay were less accurate (R2 of 0.48–0.53, LCCC of 0.67–0.71, RPD of 1.39–1.49), suggesting the sensor’s utility is limited to indicating general trends for these properties. Among the tested algorithms, MBL consistently provided the most accurate and robust predictions across the majority of soil properties. Our findings demonstrate that the low-cost portable NIR sensor, when coupled with appropriate machine learning algorithms, is a powerful and viable tool for the rapid and reliable estimation of critical paddy soil fertility properties (SOM and TN). This technology has significant potential to support field-level soil health monitoring, precision fertilization strategies, and sustainable land management in the agricultural systems of Southeastern China. Full article
(This article belongs to the Special Issue Soil Sensing and Mapping in Precision Agriculture: 2nd Edition)
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30 pages, 24142 KB  
Article
Enhanced Cropland SOM Prediction via LEW-DWT Fusion of Multi-Temporal Landsat 8 Images and Time-Series NDVI Features
by Lixin Ning, Daocheng Li, Yingxin Xia, Erlong Xiao, Dongfeng Han, Jun Yan and Xiaoliang Dong
Sensors 2026, 26(3), 1048; https://doi.org/10.3390/s26031048 - 5 Feb 2026
Viewed by 389
Abstract
Soil organic matter (SOM) is a key indicator of arable land quality and the global carbon cycle; accurate regional-scale SOM estimation is vitally significant for sustainable agricultural development and climate change research. This study evaluates a multisource data-fusion approach for improving cropland SOM [...] Read more.
Soil organic matter (SOM) is a key indicator of arable land quality and the global carbon cycle; accurate regional-scale SOM estimation is vitally significant for sustainable agricultural development and climate change research. This study evaluates a multisource data-fusion approach for improving cropland SOM prediction in Yucheng City, Shandong Province, China. We applied a Local Energy Weighted Discrete Wavelet Transform (LEW-DWT) to fuse multi-temporal Landsat 8 imagery (2014–2023). Quantitative analysis (e.g., Information Entropy and Average Gradient) demonstrated that LEW-DWT effectively preserved high-frequency spatial details and texture features of fragmented croplands better than traditional DWT and simple splicing methods. These were combined with 41 environmental predictors to construct composite Ev–Tn–Mm features (environmental variables, temporal NDVI features, and multi-temporal multispectral information). Random Forest (RF) and Convolutional Neural Network (CNN) models were trained and compared to assess the contribution of the fused data to SOM mapping. Key findings are: (1) Comparative analysis showed that the LEW-DWT fusion strategy achieved the lowest spectral distortion and highest spatial fidelity. Using the fused multitemporal dataset, the CNN attained the highest predictive performance for SOM (R2 = 0.49). (2) Using the Ev–Tn–Mm features, the CNN achieved R2 = 0.62, outperforming the RF model (R2 = 0.53). Despite the limited sample size, the optimized shallow CNN architecture effectively extracted local spatial features while mitigating overfitting. (3) Variable importance analysis based on the RF model reveals that mean soil moisture is the primary single variable influencing the SOM, (relative importance 15.22%), with the NDVI phase among time-series features (1.80%) and the SWIR1 band among fused multispectral bands (1.38%). (4) By category, soil moisture-related variables contributed 45.84% of total importance, followed by climatic factors. The proposed multisource fusion framework offers a practical solution for regional SOM digital monitoring and can support precision agriculture and soil carbon management. Full article
(This article belongs to the Special Issue Soil Sensing and Mapping in Precision Agriculture: 2nd Edition)
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20 pages, 6256 KB  
Article
Spectral Predictability of Soil Organic Matter Depends on Its Humin Fraction Rather than Spectral Fusion
by Zhi Zhang, Meihua Yang and Asim Biswas
Sensors 2025, 25(24), 7616; https://doi.org/10.3390/s25247616 - 16 Dec 2025
Cited by 1 | Viewed by 570
Abstract
Soil organic matter (SOM) governs critical soil functions, including carbon storage, nutrient cycling, and microbial activity; yet the specific fractions responsible for its spectral predictability remain poorly understood. This study addresses a fundamental research gap by comparing visible–near-infrared (vis–NIR), mid-infrared (MIR), and fused [...] Read more.
Soil organic matter (SOM) governs critical soil functions, including carbon storage, nutrient cycling, and microbial activity; yet the specific fractions responsible for its spectral predictability remain poorly understood. This study addresses a fundamental research gap by comparing visible–near-infrared (vis–NIR), mid-infrared (MIR), and fused spectroscopy for predicting SOM and its components: humic acid (HA), fulvic acid (FA), and Humin. Using 93 soil samples from subtropical croplands in southeastern China, we employed partial least squares regression with full spectra and LASSO-selected wavelengths to build predictive models. Results demonstrated that both vis–NIR and MIR individually provided moderately strong predictive performance for SOM and Humin (R2 = 0.79–0.90, CCC = 0.85–0.93), while FA remained unpredictable (R2 < 0.24) due to weak, overlapping spectral features. The strong predictability of SOM was primarily attributed to the Humin fraction, which comprises approximately 50 percent of total SOM and exhibits abundant spectrally active functional groups. Contrary to expectations, spectral fusion did not improve predictions because both spectral regions already contained complementary information, and fusion introduced redundancy and scale imbalance rather than increasing effective dimensionality. This study establishes that accurate SOM estimation depends fundamentally on the predictability and abundance of the Humin fraction, providing new mechanistic insights for spectroscopic soil carbon monitoring and highlighting the need for component-specific modeling approaches in soil organic matter research. Full article
(This article belongs to the Special Issue Soil Sensing and Mapping in Precision Agriculture: 2nd Edition)
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