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Land
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Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems...
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Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean

A special issue of Land (ISSN 2073-445X).

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 14367

Special Issue Editors

Dr. Giuseppe Lo Papa

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Guest Editor
Department of Agricultural, Food and Forestry Science, University of Palermo, 90128 Palermo, Italy
Interests: digital soil mapping and modeling; anthropogenic soils; Mediterranean soilscapes; soil conservation.
Dr. Giuliano Langella

E-Mail Website
Guest Editor
Department of Agriculture, University of Naples Federico II, Via Università 100, 80055 Portici, Italy
Interests: digital soil mapping and modeling; data management; Mediterranean soilscapes; soil conservation
Dr. Maria Fantappiè

E-Mail Website
Guest Editor
Council for Agricultural Research and Economics - Research Centre for Agriculture and Environment, Via Lanciola 12/A, 50125 Firenze, Italy
Interests: pedology; digital soil mapping; spatial statistics; geostatistics; soil organic carbon modeling; soil erosion modeling; soil database; GIS; soil surveying; soil classifying and correlating
Special Issues, Collections and Topics in MDPI journals
Dr. Calogero Schillaci

E-Mail Website
Guest Editor
Calogero Schillaci, European Commission, Joint Research Centre, 21027 Ispra, Italy
Interests: land degradation; digital soil mapping and modeling; data management; soil organic carbon
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The knowledge of the soil surveyor in the digital era has changed; thanks to the increased computational capacity of geographic information systems, legacy soil data are now seen as a treasure. Results of international collaboration and national research projects to innovate in the soil-mapping domain are in place. This vast amount of information needs to be valorized and integrated wisely with new soil data. The acquisition and interpretation of the soil properties and their changes over time could steer our understanding of earth processes and positively affect how we manage soil resources. Operational use of the DSM for precision farming is one of the main essential activities in today’s agriculture. This can benefit from many technological advances, such as remote sensing, decision support systems, the web application of soil modelling and mapping and cloud computing. As highlighted in the new European strategy to tackle climate change and environmental challenges, soil maps are needed to support sustainable development and climate mitigation, providing the broader soil user community with the soil knowledge and data flows required to safeguard soils. This Special Issue seeks contributions dealing with digital soil mapping, smart soil data management, legacy data digitalization from soil maps, the extraction of spatial knowledge from soil survey data and remote sensing.

Dr. Giuseppe Lo Papa
Dr. Giuliano Langella
Dr. Maria Fantappiè
Dr. Calogero Schillaci
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. Land 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

  • digital soil mapping
  • soil organic carbon
  • soil monitoring
  • anthropogenic soils
  • soil modeling

Published Papers (7 papers)

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Research

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16 pages, 3496 KiB  
Article
Mapping Soil Organic Carbon Stock and Uncertainties in an Alpine Valley (Northern Italy) Using Machine Learning Models
by Sara Agaba, Chiara Ferré, Marco Musetti and Roberto Comolli
Land 2024, 13(1), 78; https://doi.org/10.3390/land13010078 - 10 Jan 2024
Viewed by 1040
Abstract
In this study, we conducted a comprehensive analysis of the spatial distribution of soil organic carbon stock (SOC stock) and the associated uncertainties in two soil layers (0–10 cm and 0–30 cm; SOC stock 10 and SOC stock 30, respectively), in Valchiavenna, an [...] Read more.
In this study, we conducted a comprehensive analysis of the spatial distribution of soil organic carbon stock (SOC stock) and the associated uncertainties in two soil layers (0–10 cm and 0–30 cm; SOC stock 10 and SOC stock 30, respectively), in Valchiavenna, an alpine valley located in northern Italy (450 km2). We employed the digital soil mapping (DSM) approach within different machine learning models, including multivariate adaptive regression splines (MARS), random forest (RF), support vector regression (SVR), and elastic net (ENET). Our dataset comprised soil data from 110 profiles, with SOC stock calculations for all sampling points based on bulk density (BD), whether measured or estimated, considering the presence of rock fragments. As environmental covariates for our research, we utilized environmental variables, in particular, geomorphometric parameters derived from a digital elevation model (with a 20 m pixel resolution), land cover data, and climatic maps. To evaluate the effectiveness of our models, we evaluated their capacity to predict SOC stock 10 and SOC stock 30 using the coefficient of determination (R2). The results for the SOC stock 10 were as follows: MARS 0.39, ENET 0.41, RF 0.69, and SVR 0.50. For the SOC stock 30, the corresponding R2 values were: MARS 0.45, ENET 0.48, RF 0.65, and SVR 0.62. Additionally, we calculated the root-mean-squared error (RMSE), mean absolute error (MAE), the bias, and Lin’s concordance correlation coefficient (LCCC) for further assessment. To map the spatial distribution of SOC stock and address uncertainties in both soil layers, we chose the RF model, due to its better performance, as indicated by the highest R2 and the lowest RMSE and MAE. The resulting SOC stock maps using the RF model demonstrated an accuracy of RMSE = 1.35 kg m−2 for the SOC stock 10 and RMSE = 3.36 kg m−2 for the SOC stock 30. To further evaluate and illustrate the precision of our soil maps, we conducted an uncertainty assessment and mapping by analyzing the standard deviation (SD) from 50 iterations of the best-performing RF model. This analysis effectively highlighted the high accuracy achieved in our soil maps. The maps of uncertainty demonstrated that the RF model better predicts the SOC stock 10 compared to the SOC stock 30. Predicting the correct ranges of SOC stocks was identified as the main limitation of the methodology. Full article
(This article belongs to the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean)
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17 pages, 6124 KiB  
Article
Digital Mapping of Soil Properties Using Ensemble Machine Learning Approaches in an Agricultural Lowland Area of Lombardy, Italy
by Odunayo David Adeniyi, Alexander Brenning, Alice Bernini, Stefano Brenna and Michael Maerker
Land 2023, 12(2), 494; https://doi.org/10.3390/land12020494 - 16 Feb 2023
Cited by 4 | Viewed by 2317
Abstract
Sustainable agricultural landscape management needs reliable and accurate soil maps and updated geospatial soil information. Recently, machine learning (ML) models have commonly been used in digital soil mapping, together with limited data, for various types of landscapes. In this study, we tested linear [...] Read more.
Sustainable agricultural landscape management needs reliable and accurate soil maps and updated geospatial soil information. Recently, machine learning (ML) models have commonly been used in digital soil mapping, together with limited data, for various types of landscapes. In this study, we tested linear and nonlinear ML models in predicting and mapping soil properties in an agricultural lowland landscape of Lombardy region, Italy. We further evaluated the ability of an ensemble learning model, based on a stacking approach, to predict the spatial variation of soil properties, such as sand, silt, and clay contents, soil organic carbon content, pH, and topsoil depth. Therefore, we combined the predictions of the base learners (ML models) with two meta-learners. Prediction accuracies were assessed using a nested cross-validation procedure. Nonetheless, the nonlinear single models generally performed well, with RF having the best results; the stacking models did not outperform all the individual base learners. The most important topographic predictors of the soil properties were vertical distance to channel network and channel network base level. The results yield valuable information for sustainable land use in an area with a particular soil water cycle, as well as for future climate and socioeconomic changes influencing water content, soil pollution dynamics, and food security. Full article
(This article belongs to the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean)
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20 pages, 1691 KiB  
Article
Pedodiversity and Organic Matter Stock of Soils Developed on Sandstone Formations in the Northern Apennines (Italy)
by Livia Vittori Antisari, William Trenti, Alessandro Buscaroli, Gloria Falsone, Gilmo Vianello and Mauro De Feudis
Land 2023, 12(1), 79; https://doi.org/10.3390/land12010079 - 27 Dec 2022
Cited by 4 | Viewed by 2057
Abstract
Pedodiversity is considered the cornerstone of biodiversity. This work aimed to (1) assess pedodiversity according to vegetation, topographic factors, and lithology and to (2) identify the major soil-forming factors on soil organic matter (SOM) stock at a 0–30 cm depth. These goals were [...] Read more.
Pedodiversity is considered the cornerstone of biodiversity. This work aimed to (1) assess pedodiversity according to vegetation, topographic factors, and lithology and to (2) identify the major soil-forming factors on soil organic matter (SOM) stock at a 0–30 cm depth. These goals were reached using data from 147 georeferenced soil profiles distributed along 400–1000 m (≤1000) and 1000–2134 m (>1000) altitudinal gradients in the northern part of the Apennine chain in Italy. Soils showed mainly weak or incipient development (i.e., Entisols and Inceptisols), which could be attributed to sand-based lithology, high slope gradients, and low SOM accumulation rates, which promote soil erosion processes. However, higher pedodiversity was observed at >1000 m than at ≤1000 m, likely due to the higher vegetation cover diversity and climate variability; Spodosols and Mollisols were also found. A greater SOM stock was found at >1000 than ≤1000 m, and vegetation seemed to not affect SOM amounts, suggesting a greater influence of climate on SOM content compared to vegetation. Considering ecosystem conservation, the observed spatial pedodiversity could be considered a critical basis for the protection of soil resources and pedodiversity itself in mountain regions. Full article
(This article belongs to the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean)
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16 pages, 1654 KiB  
Article
Insights into the Effects of Study Area Size and Soil Sampling Density in the Prediction of Soil Organic Carbon by Vis-NIR Diffuse Reflectance Spectroscopy in Two Forest Areas
by Massimo Conforti and Gabriele Buttafuoco
Land 2023, 12(1), 44; https://doi.org/10.3390/land12010044 - 23 Dec 2022
Cited by 3 | Viewed by 1570
Abstract
Sustainable forest land management requires measuring and monitoring soil organic carbon. Visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR, 350–2500 nm), although it has become an important method for predicting soil organic carbon (SOC), requires further studies and methods of analysis to realize its [...] Read more.
Sustainable forest land management requires measuring and monitoring soil organic carbon. Visible and near-infrared diffuse reflectance spectroscopy (Vis-NIR, 350–2500 nm), although it has become an important method for predicting soil organic carbon (SOC), requires further studies and methods of analysis to realize its full potential. This study aimed to determine if the size of the study area and soil sampling density may affect the performance of Vis-NIR diffuse reflectance spectroscopy in the prediction of soil organic carbon. Two forest sites in the Calabria region (southern Italy), which differ in terms of area and soil sampling density, were used. The first one was Bonis catchment area (139 ha) with a cover consisting mainly of Calabrian pine, while the second was Mongiana forest area (33.2 ha) within the “Marchesale” Biogenetic Nature Reserve, which is covered by beech. The two study areas are relatively homogeneous regarding parent material and soil type, while they have very different soil sampling density. In particular, Bonis catchment has a lower sampling density (135 samples out of 139 ha) than Mongiana area (231 samples out of 33.2 ha). Three multivariate calibration methods (principal component regression (PCR), partial least square regression (PLSR), and support vector machine regression (SVMR)) were combined with different pretreatment techniques of diffuse reflectance spectra (absorbance, ABS, standard normal variate, SNV, and Savitzky–Golay filtering with first derivative (SG 1st D). All soil samples (0–20 cm) were analyzed in the laboratory for SOC concentration and for measurements of diffuse reflectance spectra in the Vis-NIR region. The set of samples from each study area was randomly divided into a calibration set (70%) and a validation set (30%). The assessment of the goodness for the different calibration models and the following SOC predictions using the validation sets was based on three parameters: the coefficient of determination (R2), the root mean square error (RMSE), and the interquartile range (RPIQ). The results showed that for the two study areas, different levels of goodness of the prediction models depended both on the type of pretreatment and the multivariate method used. Overall, the prediction models obtained with PLSR and SVMR performed better than those of PCR. The best performance was obtained with the SVMR method combined with ABS + SNV + SG 1st D pretreatment (R2 ≥ 0.77 and RPIQ > 2.30). However, there is no result that can absolutely provide definitive indications of either the effects of the study area size and soil sampling density in the prediction of SOC by vis-NIR spectroscopy, but this study fostered the need for future investigations in areas and datasets of different sizes from those in this study and including also different soil landscapes. Full article
(This article belongs to the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean)
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21 pages, 2758 KiB  
Article
Predictive Mapping of Electrical Conductivity and Assessment of Soil Salinity in a Western Türkiye Alluvial Plain
by Fuat Kaya, Calogero Schillaci, Ali Keshavarzi and Levent Başayiğit
Land 2022, 11(12), 2148; https://doi.org/10.3390/land11122148 - 28 Nov 2022
Cited by 9 | Viewed by 2853
Abstract
The increase in soil salinity due to human-induced processes poses a severe threat to agriculture on a regional and global scale. Soil salinization caused by natural and anthropogenic factors is a vital environmental hazard, specifically in semi-arid and arid regions of the world. [...] Read more.
The increase in soil salinity due to human-induced processes poses a severe threat to agriculture on a regional and global scale. Soil salinization caused by natural and anthropogenic factors is a vital environmental hazard, specifically in semi-arid and arid regions of the world. The detection and monitoring of salinity are critical to the sustainability of soil management. The current study compared the performance of machine learning models to produce spatial maps of electrical conductivity (EC) (as a proxy for salinity) in an alluvial irrigation plain. The current study area is located in the Isparta province (100 km2), land cover is mainly irrigated, and the dominant soils are Inceptisols, Mollisols, and Vertisols. Digital soil mapping (DSM) methodology was used, referring to the increase in the digital representation of soil formation factors with today’s technological advances. Plant and soil-based indices produced from the Sentinel 2A satellite image, topographic indices derived from the digital elevation model (DEM), and CORINE land cover classes were used as predictors. The support vector regression (SVR) algorithm revealed the best relationships in the study area. Considering the estimates of different algorithms, according to the FAO salinity classification, a minimum of 12.36% and a maximum of 20.19% of the study area can be classified as slightly saline. The low spatial dependence between model residuals limited the success of hybrid methods. The land irrigated cover played a significant role in predicting the current level of EC. Full article
(This article belongs to the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean)
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16 pages, 5879 KiB  
Article
Diachronic Mapping of Soil Organic Matter in Eastern Croatia Croplands
by Sebastiano Trevisani and Igor Bogunovic
Land 2022, 11(6), 861; https://doi.org/10.3390/land11060861 - 07 Jun 2022
Cited by 2 | Viewed by 1709
Abstract
The spatiotemporal analysis and mapping of soil organic matter (SOM) play a pivotal role for evaluating soil health and for implementing preservation and restoration actions. In this context, the first aim of the study is to furnish a high-resolution mapping of current SOM [...] Read more.
The spatiotemporal analysis and mapping of soil organic matter (SOM) play a pivotal role for evaluating soil health and for implementing preservation and restoration actions. In this context, the first aim of the study is to furnish a high-resolution mapping of current SOM content in eastern Croatia. The second aim is to perform a diachronic analysis of SOM content, comparing two datasets characterized by an extreme data imbalance. The more recent dataset (SOM2010), representative of 2010s, comprises 19,386 samples and the older dataset (SOM1970), representative of the 1970s, comprises 152 samples. The marked data imbalance and the different modalities in soil sampling and laboratory analysis of the two datasets are taken into consideration in performing the comparison. The study reveals a general depletion trend of SOM from the 1970s to the 2010s, more evident in with regard to Fluvisols and Gleysols. At a regional scale, the SOM2010 is characterized by lower variability compared to SOM1970, indicating a process of homogenization of SOM spatial distribution in recent years. Considering the local scale, there is limited information for the 1970s; for the 2010s the SOM spatial distribution is characterized by a high short-range spatial variability, with a characteristic spotty appearance, likely related to agricultural practices. Full article
(This article belongs to the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean)
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Review

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22 pages, 2724 KiB  
Review
A Systematic Review on Digital Soil Mapping Approaches in Lowland Areas
by Odunayo David Adeniyi, Hauwa Bature and Michael Mearker
Land 2024, 13(3), 379; https://doi.org/10.3390/land13030379 - 17 Mar 2024
Viewed by 1006
Abstract
Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil [...] Read more.
Digital soil mapping (DSM) around the world is mostly conducted in areas with a certain relief characterized by significant heterogeneities in soil-forming factors. However, lowland areas (e.g., plains, low-relief areas), prevalently used for agricultural purposes, might also show a certain variability in soil characteristics. To assess the spatial distribution of soil properties and classes, accurate soil datasets are a prerequisite to facilitate the effective management of agricultural areas. This systematic review explores the DSM approaches in lowland areas by compiling and analysing published articles from 2008 to mid-2023. A total of 67 relevant articles were identified from Web of Science and Scopus. The study reveals a rising trend in publications, particularly in recent years, indicative of the growing recognition of DSM’s pivotal role in comprehending soil properties in lowland ecosystems. Noteworthy knowledge gaps are identified, emphasizing the need for nuanced exploration of specific environmental variables influencing soil heterogeneity. This review underscores the dominance of agricultural cropland as a focus, reflecting the intricate relationship between soil attributes and agricultural productivity in lowlands. Vegetation-related covariates, relief-related factors, and statistical machine learning models, with random forest at the forefront, emerge prominently. The study concludes by outlining future research directions, highlighting the urgency of understanding the intricacies of lowland soil mapping for improved land management, heightened agricultural productivity, and effective environmental conservation strategies. Full article
(This article belongs to the Special Issue Digital Soil Mapping, Decision Support Tools and Soil Monitoring Systems in the Mediterranean)
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