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Special Issue "Global Gridded Soil Information Based on Machine Learning"

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (31 May 2022) | Viewed by 5231

Special Issue Editors

Dr. Brigitta Szabó (Tóth)
E-Mail Website
Guest Editor
Institute for Soil Sciences and Agricultural Chemistry, Centre for Agricultural Research, Budapest, Hungary
Interests: soil science; machine learning; pedotransfer functions; predictive soil mapping; uncertainty assessment
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Eyal Ben-Dor
E-Mail Website
Guest Editor
The Remote Sensing Laboratory, Tel-Aviv University, Tel-Aviv, Israel
Interests: soil spectroscopy; hyperspectral remote sensing; remote sensing of the environment
Special Issues, Collections and Topics in MDPI journals
Dr. Yijian Zeng
E-Mail Website1 Website2
Guest Editor
Geo-Information Science and Earth Observation (ITC), University of Twente, 7514 AE Twente, The Netherlands
Interests: the interface of soil–water–plant–energy interactions and Earth observations, including physically-based modelling of soil and subsurface processes
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Salvatore Manfreda
E-Mail Website
Guest Editor
Department of Civil, Architectural and Environmental Engineering, University of Naples Federico II, Via Claudio 21, 80125 Napoli, Italy
Interests: distributed modeling; flood risk; stochastic processes in hydrology and UAS-based monitoring
Special Issues, Collections and Topics in MDPI journals
Dr. Madlene Nussbaum
E-Mail Website
Guest Editor
School of Agricultural, Forest and Food Sciences (BFH-HAFL), Bern University of Applied Sciences, Bern, Switzerland
Interests: regional digital soil mapping; machine learning for spatial prediction; pedotransfer functions; methodological knowledge transfer on soil mapping

Special Issue Information

Dear Colleagues,


Recent technological advances in both remote sensing and soil mapping approaches and progress in establishing harmonized soil profile datasets have opened up the potential to derive global gridded soil information. This has been possible because worldwide researchers have gained a growing experience in building standardized soil profile datasets with measured physical, chemical data and taxonomical information; filling data gaps; using Earth observation data for soil mapping; optimizing soil sampling strategy; processing big data; applying machine learning algorithms; and assessing uncertainty; which support the preparation of global soil maps with increasing accuracy and spatiotemporal resolution.

 

Data-intensive computing solutions to process and analyze the exploding amount of environmental information are continuously updated. Machine learning algorithms are among the most frequently used tools for data preprocessing and describing the complex relationship between soil properties and environmental covariates with the ability to assess the uncertainty of the predictions. One of the greatest challenges in deriving global gridded soil information is to make the most of the predictive power of machine learning algorithms with the continuously increasing amount of environmental information. This Special Issue is dedicated to machine learning-based methods in:

  • proximal and digital global mapping of soil properties (e.g., basic, hydraulic, thermal, functional, ecosystem services);
  • computing systems/algorithms/approaches using Earth observation data to derive global gridded soil datasets;
  • preprocessing Earth observation data to feed into global soil mapping;
  • data-intensive computing methods for incorporating Earth observation data for predictive soil mapping;
  • optimizing temporal resolution to globally track the changes of soil properties,
  • uncertainty assessment of the derived gridded soil information;
  • specifying algorithms to local soil specificities in, e.g., proximal soil mapping;
  • the engagement of remote sensing data with digital soil mapping;
  • downscaling of large-scale soil feature;
  • other related topics.

Review contributions on the abovementioned topics are welcomed as well.

 

Dr. Brigitta Szabó (Tóth)
Prof.Dr. Eyal Ben-Dor
Dr. Yijian Zeng
Prof.Dr. Salvatore Manfreda
Dr. Madlene Nussbaum
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. Remote Sensing 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 2500 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

  • Global gridded soil information
  • Predictive soil mapping
  • Uncertainty assessment
  • Spectral data
  • Parallel distributive platforms
  • Machine learning
  • Digital soil mapping

Published Papers (3 papers)

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Research

Article
Global Mapping of Soil Water Characteristics Parameters— Fusing Curated Data with Machine Learning and Environmental Covariates
Remote Sens. 2022, 14(8), 1947; https://doi.org/10.3390/rs14081947 - 18 Apr 2022
Cited by 3 | Viewed by 699
Abstract
Hydrological and climatic modeling of near-surface water and energy fluxes is critically dependent on the availability of soil hydraulic parameters. Key among these parameters is the soil water characteristic curve (SWCC), a function relating soil water content (θ) to matric potential [...] Read more.
Hydrological and climatic modeling of near-surface water and energy fluxes is critically dependent on the availability of soil hydraulic parameters. Key among these parameters is the soil water characteristic curve (SWCC), a function relating soil water content (θ) to matric potential (ψ). The direct measurement of SWCC is laborious, hence, reported values of SWCC are spatially sparse and usually have only a small number of data pairs (θ, ψ) per sample. Pedotransfer function (PTF) models have been used to correlate SWCC with basic soil properties, but evidence suggests that SWCC is also shaped by vegetation-promoted soil structure and climate-modified clay minerals. To capture these effects in their spatial context, a machine learning framework (denoted as Covariate-based GeoTransfer Functions, CoGTFs) was trained using (a) a novel and comprehensive global dataset of SWCC parameters and (b) global maps of environmental covariates and soil properties at 1 km spatial resolution. Two CoGTF models were developed: one model (CoGTF-1) was based on predicted soil covariates because measured soil data are not generally available, and the other (CoGTF-2) used measured soil properties to model SWCC parameters. The spatial cross-validation of CoGTF-1 resulted, for the predicted van Genuchten SWCC parameters, in concordance correlation coefficients (CCC) of 0.321–0.565. To validate the resulting global maps of SWCC parameters and to compare the CoGTF framework to two pedotransfer functions from the literature, the predicted water contents at 0.1 m, 3.3 m, and 150 m matric potential were evaluated. The accuracy metrics for CoGTF were considerably better than PTF-based maps. Full article
(This article belongs to the Special Issue Global Gridded Soil Information Based on Machine Learning)
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Article
In Situ Observation-Constrained Global Surface Soil Moisture Using Random Forest Model
Remote Sens. 2021, 13(23), 4893; https://doi.org/10.3390/rs13234893 - 02 Dec 2021
Cited by 3 | Viewed by 959
Abstract
The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land [...] Read more.
The inherent biases of different long-term gridded surface soil moisture (SSM) products, unconstrained by the in situ observations, implies different spatio-temporal patterns. In this study, the Random Forest (RF) model was trained to predict SSM from relevant land surface feature variables (i.e., land surface temperature, vegetation indices, soil texture, and geographical information) and precipitation, based on the in situ soil moisture data of the International Soil Moisture Network (ISMN.). The results of the RF model show an RMSE of 0.05 m3 m−3 and a correlation coefficient of 0.9. The calculated impurity-based feature importance indicates that the Antecedent Precipitation Index affects most of the predicted soil moisture. The geographical coordinates also significantly influence the prediction (i.e., RMSE was reduced to 0.03 m3 m−3 after considering geographical coordinates), followed by land surface temperature, vegetation indices, and soil texture. The spatio-temporal pattern of RF predicted SSM was compared with the European Space Agency Climate Change Initiative (ESA-CCI) soil moisture product, using both time-longitude and latitude diagrams. The results indicate that the RF SSM captures the spatial distribution and the daily, seasonal, and annual variabilities globally. Full article
(This article belongs to the Special Issue Global Gridded Soil Information Based on Machine Learning)
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Article
Elaborating Hungarian Segment of the Global Map of Salt-Affected Soils (GSSmap): National Contribution to an International Initiative
Remote Sens. 2020, 12(24), 4073; https://doi.org/10.3390/rs12244073 - 12 Dec 2020
Cited by 11 | Viewed by 2395
Abstract
Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing [...] Read more.
Recently, the Global Map of Salt-affected Soils (GSSmap) was launched, which pursued a country-driven approach and aimed to update the global and country-level information on salt-affected soils (SAS). The aim of this paper was to present how Hungary contributed to GSSmap by preparing its own SAS maps using advanced digital soil mapping techniques. We used not just a combination of random forest and multivariate geostatistical techniques for predicting the spatial distribution of SAS indicators (i.e., pH, electrical conductivity and exchangeable sodium percentage) for the topsoil (0–30 cm) and subsoil (30–100 cm), but also a number of indices derived from Sentinel-2 satellite images as environmental covariates. The importance plots of random forests showed that in addition to climatic, geomorphometric parameters and legacy soil information, image indices were the most important covariates. The performance of spatial modelling was checked by 10-fold cross validation showing that the accuracy of the SAS maps was acceptable. By this study and by the resulting maps of it, we not just contributed to GSSmap, but also renewed the SAS mapping methodology in Hungary, where we paid special attention to modelling and quantifying the prediction uncertainty that had not been quantified or even taken into consideration earlier. Full article
(This article belongs to the Special Issue Global Gridded Soil Information Based on Machine Learning)
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