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Soil Sensing and Mapping in Precision Agriculture

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

Deadline for manuscript submissions: 31 March 2025 | Viewed by 10955

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Special Issue Editors

Prof. Dr. Francisco Jesús Moral García

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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

Prof. Dr. João Manuel Pereira Ramalho Serrano

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MED—Mediterranean Institute for Agriculture, Environment and Development, Instituto de Investigação e Formação Avançada, Universidade de Évora, Pólo da Mitra, Ap. 94, 7006-554 Évora, Portugal
Interests: agricultural mechanization; precision agriculture; sensors; agro-silvo-pastoral systems

Prof. Dr. Fernando Carvajal-Ramírez

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Guest Editor

Department of Engineering, University of Almería, 04120 Almería, Spain
Interests: UAV photogrammetry applied to remote sensing of natural resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue welcomes the contribution of studies focusing on the use of proximal or remote soil sensing techniques with the aim of 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 in pasture and grassland fields. The following are examples of suitable topics: methods for the collection of soil and soil-related data, data modelling, interpretation and elaboration of focused soil and/or plant information, application of soil and/or plant information in sectors (agriculture, forestry, natural resource management, climate change mitigation, etc.) and soil and/or plant information systems at different spatial levels, as a basis for the implementation of field differentiated management, for example, through of variable-rate technology (soil amendment, soil fertilization, etc.).

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

Prof. Dr. Francisco Jesús Moral García
Prof. 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 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. 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

Published Papers (6 papers)

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Research

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18 pages, 11490 KiB

Open AccessArticle

Mapping Soil Properties in the Haihun River Sub-Watershed, Yangtze River Basin, China, by Integrating Machine Learning and Variable Selection

by Jun Huang, Jia Liu, Yingcong Ye, Yameng Jiang, Yuying Lai, Xianbing Qin, Lin Zhang and Yefeng Jiang

Sensors 2024, 24(12), 3784; https://doi.org/10.3390/s24123784 - 11 Jun 2024

Viewed by 479

Abstract

Mapping soil properties in sub-watersheds is critical for agricultural productivity, land management, and ecological security. Machine learning has been widely applied to digital soil mapping due to a rapidly increasing number of environmental covariates. However, the inclusion of many environmental covariates in machine learning models leads to the problem of multicollinearity, with poorly understood consequences for prediction performance. Here, we explored the effects of variable selection on the prediction performance of two machine learning models for multiple soil properties in the Haihun River sub-watershed, Jiangxi Province, China. Surface soils (0–20 cm) were collected from a total of 180 sample points in 2022. The optimal covariates were selected from 40 environmental covariates using a recursive feature elimination algorithm. Compared to all-variable models, the random forest (RF) and extreme gradient boosting (XGBoost) models with variable selection improved in prediction accuracy. The R² values of the RF and XGBoost models increased by 0.34 and 0.47 for the soil organic carbon, by 0.67 and 0.62 for the total phosphorus, and by 0.43 and 0.62 for the available phosphorus, respectively. The models with variable selection presented reduced global uncertainty, and the overall uncertainty of the RF model was lower than that of the XGBoost model. The soil properties showed high spatial heterogeneity based on the models with variable selection. Remote sensing covariates (particularly principal component 2) were the major factors controlling the distribution of the soil organic carbon. Human activity covariates (mainly land use) and organism covariates (mainly potential evapotranspiration) played a predominant role in driving the distribution of the soil total and soil available phosphorus, respectively. This study indicates the importance of variable selection for predicting multiple soil properties and mapping their spatial distribution in sub-watersheds. Full article

(This article belongs to the Special Issue Soil Sensing and Mapping in Precision Agriculture)

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15 pages, 2281 KiB

Open AccessArticle

Performance of Different Crop Models in Simulating Soil Temperature

by Janani Kandasamy, Yuan Xue, Paul Houser and Viviana Maggioni

Sensors 2023, 23(6), 2891; https://doi.org/10.3390/s23062891 - 7 Mar 2023

Cited by 1 | Viewed by 1586

Abstract

Soil temperature is one of the key factors to be considered in precision agriculture to increase crop production. This study is designed to compare the effectiveness of a land surface model (Noah Multiparameterization (Noah-MP)) against a traditional crop model (Environmental Policy Integrated Climate Model (EPIC)) in estimating soil temperature. A sets of soil temperature estimates, including three different EPIC simulations (i.e., using different parameterizations) and a Noah-MP simulations, is compared to ground-based measurements from across the Central Valley in California, USA, during 2000–2019. The main conclusion is that relying only on one set of model estimates may not be optimal. Furthermore, by combining different model simulations, i.e., by taking the mean of two model simulations to reconstruct a new set of soil temperature estimates, it is possible to improve the performance of the single model in terms of different statistical metrics against the reference ground observations. Containing ratio (CR), Euclidean distance (dist), and correlation co-efficient (R) calculated for the reconstructed mean improved by 52%, 58%, and 10%, respectively, compared to both model estimates. Thus, the reconstructed mean estimates are shown to be more capable of capturing soil temperature variations under different soil characteristics and across different geographical conditions when compared to the parent model simulations. Full article

(This article belongs to the Special Issue Soil Sensing and Mapping in Precision Agriculture)

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28 pages, 7984 KiB

Open AccessArticle

Sensing and Mapping the Effects of Cow Trampling on the Soil Compaction of the Montado Mediterranean Ecosystem

by João Serrano, João Marques, Shakib Shahidian, Emanuel Carreira, José Marques da Silva, Luís Paixão, Luís Lorenzo Paniagua, Francisco Moral, Isabel Ferraz de Oliveira and Elvira Sales-Baptista

Sensors 2023, 23(2), 888; https://doi.org/10.3390/s23020888 - 12 Jan 2023

Cited by 9 | Viewed by 2614

Abstract

The economic and environmental sustainability of extensive livestock production systems requires the optimisation of soil management, pasture production and animal grazing. Soil compaction is generally viewed as an indicator of soil degradation processes and a determinant factor in crop productivity. In the Montado silvopastoral ecosystem, characteristic of the Iberian Peninsula, animal trampling is mentioned as a variable to consider in soil compaction. This study aims: (i) to assess the spatial variation in the compaction profile of the 0–0.30 m deep soil layer over several years; (ii) to evaluate the effect of animal trampling on soil compaction; and (iii) to demonstrate the utility of combining various technological tools for sensing and mapping indicators of soil characteristics (Cone Index, CI; and apparent electrical conductivity, EC_a), of pastures’ vegetative vigour (Normalised Difference Vegetation Index, NDVI) and of cows’ grazing zones (Global Positioning Systems, GPS collars). The significant correlation between CI, soil moisture content (SMC) and EC_a and between EC_a and soil clay content shows the potential of using these expedient tools provided by the development of Precision Agriculture. The compaction resulting from animal trampling was significant outside the tree canopy (OTC) in the four evaluated dates and in the three soil layers considered (0–0.10 m; 0.10–0.20 m; 0.20–0.30 m). However, under the tree canopy (UTC), the effect of animal trampling was significant only in the 0–0.10 m soil layer and in three of the four dates, with a tendency for a greater CI at greater depths (0.10–0.30 m), in zones with a lower animal presence. These results suggest that this could be a dynamic process, with recovery cycles in the face of grazing management, seasonal fluctuations in soil moisture or spatial variation in specific soil characteristics (namely clay contents). The NDVI shows potential for monitoring the effect of livestock trampling during the peak spring production phase, with greater vigour in areas with less animal trampling. These results provide good perspectives for future studies that allow the calibration and validation of these tools to support the decision-making process of the agricultural manager. Full article

(This article belongs to the Special Issue Soil Sensing and Mapping in Precision Agriculture)

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20 pages, 5553 KiB

Open AccessArticle

Spatial Interpolation of Gravimetric Soil Moisture Using EM38-mk Induction and Ensemble Machine Learning (Case Study from Dry Steppe Zone in Volgograd Region)

by Anatoly Zeyliger, Andrey Chinilin and Olga Ermolaeva

Sensors 2022, 22(16), 6153; https://doi.org/10.3390/s22166153 - 17 Aug 2022

Cited by 7 | Viewed by 2157

Abstract

The implementation of the sustainable management of the interaction between agriculture and the environment requires an increasingly deep understanding and numerical description of the soil genesis and properties of soils. One of the areas of application of relevant knowledge is digital irrigated agriculture. During the development of such technologies, the traditional methods of soil research can be quite expensive and time consuming. Proximal soil sensing in combination with predictive soil mapping can significantly reduce the complexity of the work. In this study, we used topographic variables and data from the Electromagnetic Induction Meter (EM38-mk) in combination with soil surface hydrological variables to produce cartographic models of the gravimetric soil moisture for a number of depth intervals. For this purpose, in dry steppe zone conditions, a test site was organized. It was located at the border of the parcel containing the irrigated soybean crop, where 50 soil samples were taken at different points alongside electrical conductivity data (EC_a) measured in situ in the field. The modeling of the gravimetric soil moisture was carried out with the stepwise inclusion of independent variables, using methods of ensemble machine learning and spatial cross-validation. The obtained cartographic models showed satisfactory results with the best performance R²_cv 0.59–0.64. The best combination of predictors that provided the best results of the model characteristics for predicting gravimetric soil moisture were geographical variables (buffer zone distances) in combination with the initial variables converted into the principal components. The cartographic models of the gravimetric soil moisture variability obtained this way can be used to solve the problems of managed irrigated agriculture, applying fertilizers at variable rates, thereby optimizing the use of resources by crop producers, which can ultimately contribute to the sustainable management of natural resources. Full article

(This article belongs to the Special Issue Soil Sensing and Mapping in Precision Agriculture)

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21 pages, 6138 KiB

Open AccessArticle

Monitoring and Identification of Agricultural Crops through Multitemporal Analysis of Optical Images and Machine Learning Algorithms

by José M. Espinosa-Herrera, Antonia Macedo-Cruz, Demetrio S. Fernández-Reynoso, Héctor Flores-Magdaleno, Yolanda M. Fernández-Ordoñez and Jesús Soria-Ruíz

Sensors 2022, 22(16), 6106; https://doi.org/10.3390/s22166106 - 16 Aug 2022

Cited by 4 | Viewed by 2328

Abstract

The information about where crops are distributed is useful for agri-environmental assessments, but is chiefly important for food security and agricultural policy managers. The quickness with which this information becomes available, especially over large areas, is important for decision makers. Methodologies have been proposed for the study of crops. Most of them require field survey for ground truth data and a single crop map is generated for the whole season at the end of the crop cycle and for the next crop cycle a new field survey is necessary. Here, we present models for recognizing maize (Zea mays L.), beans (Phaseolus vulgaris L.), and alfalfa (Medicago sativa L.) before the crop cycle ends without current-year field survey for ground truth data. The models were trained with an exhaustive field survey at plot level in a previous crop cycle. The field surveys begin since days before the emergence of crops to maturity. The algorithms used for classification were support vector machine (SVM) and bagged tree (BT), and the spectral information captured in the visible, red-edge, near infrared, and shortwave infrared regions bands of Sentinel 2 images was used. The models were validated within the next crop cycle each fifteen days before the mid-season. The overall accuracies range from 71.9% (38 days after the begin of cycle) to 87.5% (81 days after the begin cycle) and a kappa coefficient ranging from 0.53 at the beginning to 0.74 at mid-season Full article

(This article belongs to the Special Issue Soil Sensing and Mapping in Precision Agriculture)

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Review

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19 pages, 321 KiB

Open AccessReview

Use of Optical and Radar Imagery for Crop Type Classification in Africa: A Review

by Maryam Choukri, Ahmed Laamrani and Abdelghani Chehbouni

Sensors 2024, 24(11), 3618; https://doi.org/10.3390/s24113618 - 3 Jun 2024

Viewed by 495

Abstract

Multi-source remote sensing-derived information on crops contributes significantly to agricultural monitoring, assessment, and management. In Africa, some challenges (i.e., small-scale farming practices associated with diverse crop types and agricultural system complexity, and cloud coverage during the growing season) can imped agricultural monitoring using multi-source remote sensing. The combination of optical remote sensing and synthetic aperture radar (SAR) data has emerged as an opportune strategy for improving the precision and reliability of crop type mapping and monitoring. This work aims to conduct an extensive review of the challenges of agricultural monitoring and mapping in Africa in great detail as well as the current research progress of agricultural monitoring based on optical and Radar satellites. In this context optical data may provide high spatial resolution and detailed spectral information, which allows for the differentiation of different crop types based on their spectral signatures. However, synthetic aperture radar (SAR) satellites can provide important contributions given the ability of this technology to penetrate cloud cover, particularly in African tropical regions, as opposed to optical data. This review explores various combination techniques employed to integrate optical and SAR data for crop type classification and their applicability and limitations in the context of African countries. Furthermore, challenges are discussed in this review as well as and the limitations associated with optical and SAR data combination, such as the data availability, sensor compatibility, and the need for accurate ground truth data for model training and validation. This study also highlights the potential of advanced modelling (i.e., machine learning algorithms, such as support vector machines, random forests, and convolutional neural networks) in improving the accuracy and automation of crop type classification using combined data. Finally, this review concludes with future research directions and recommendations for utilizing optical and SAR data combination techniques in crop type classification for African agricultural systems. Furthermore, it emphasizes the importance of developing robust and scalable classification models that can accommodate the diversity of crop types, farming practices, and environmental conditions prevalent in Africa. Through the utilization of combined remote sensing technologies, informed decisions can be made to support sustainable agricultural practices, strengthen nutritional security, and contribute to the socioeconomic development of the continent. Full article

(This article belongs to the Special Issue Soil Sensing and Mapping in Precision Agriculture)

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