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New Advances in Machine Learning for Soil Properties Prediction and Mapping

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Dear Colleagues,

The measurement and digital global mapping of soil properties (e.g., soil moisture, pH, salinity, and organic carbon) are important tools for our understanding of environmental functions, soil–crop interactions, and ecosystem services. There are currently a series of open-access remote sensing data platforms, including Landsat, MODIS, and Sentinel satellites, which act as effective tools for studying and mapping soils. In terms of mining and exploiting remote sensing data, machine learning algorithms, including artificial intelligence (AI), are also providing new methods for monitoring soil properties and global soil mapping.

Machine learning, as an intelligent technique for describing the complex relationships between soil properties and environmental covariates, can perform data preprocessing, learn from existing Earth observation data, and mine hidden and unknown patterns from large databases, thus providing some accurate predictions in spatial and temporal dimensions. However, with the continuous mutation of and increase in environmental information, how to use remote sensing and machine learning algorithms to accurately predict the dynamic changes in global gridded soil information has become one of the current challenges.

It is our pleasure to announce the launch of a new Special Issue in Remote Sensing, where we especially welcome research articles covering but not limited to the following topics:

Computational systems and algorithms for deriving global gridded soil datasets using remote sensing methods;
Fusion of different combinations of remote and proximal remote sensing for global soil mapping;
Cloud computing and big data analysis for soil properties, soil pollution, and risk assessment;
Uncertainty assessment of soil information based on remote sensing techniques;
Proximal soil sensing tools to measure and map moisture, organic carbon, heavy metal molecules, and high salt concentrations in soil.

Dr. Ruhollah Taghizadeh-Mehrjardi
Dr. Mojtaba Zeraatpisheh
Dr. Mahdi Hasanlou
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 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 2700 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

soil properties
machine learning
digital soil mapping
big data analysis
artificial intelligence
remote sensing
uncertainty assessment
environmental soil formation drivers

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20 pages, 3911 KiB

Open AccessArticle

Ground-Based Hyperspectral Retrieval of Soil Arsenic Concentration in Pingtan Island, China

by Meiduan Zheng, Haijun Luan, Guangsheng Liu, Jinming Sha, Zheng Duan and Lanhui Wang

Remote Sens. 2023, 15(17), 4349; https://doi.org/10.3390/rs15174349 - 04 Sep 2023

Cited by 1 | Viewed by 1046

Abstract

The optimal selection of characteristic bands and retrieval models for the hyperspectral retrieval of soil heavy metal concentrations poses a significant challenge. Additionally, satellite-based hyperspectral retrieval encounters several issues, including atmospheric effects, limitations in temporal and radiometric resolution, and data acquisition, among others. Given this, the retrieval performance of the soil arsenic (As) concentration in Pingtan Island, the largest island in Fujian Province and the fifth largest in China, is currently unclear. This study aimed to elucidate this issue by identifying optimal characteristic bands from the full spectrum from both statistical and physical perspectives. We tested three linear models, namely Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR) and Geographically Weighted Regression (GWR), as well as three nonlinear machine learning models, including Back Propagation Neural Network (BP), Support Vector Machine Regression (SVR) and Random Forest Regression (RFR). We then retrieved soil arsenic content using ground-based soil full spectrum data on Pingtan Island. Our results indicate that the RFR model consistently outperformed all others when using both original and optimal characteristic bands. This superior performance suggests a complex, nonlinear relationship between soil arsenic concentration and spectral variables, influenced by diverse landscape factors. The GWR model, which considers spatial non-stationarity and heterogeneity, outperformed traditional models such as BP and SVR. This finding underscores the potential of incorporating spatial characteristics to enhance traditional machine learning models in geospatial studies. When evaluating retrieval model accuracy based on optimal characteristic bands, the RFR model maintained its top performance, and linear models (MLR, PLSR and GWR) showed notable improvement. Specifically, the GWR model achieved the highest r value for the validation data, indicating that selecting optimal characteristic bands based on high Pearson’s correlation coefficients (e.g., abs(Pearson’s correlation coefficient) ≥0.45) and high sensitivity to soil active materials successfully mitigates uncertainties linked to characteristic band selection solely based on Pearson’s correlation coefficients. Consequently, two effective retrieval models were generated: the best-performing RFR model and the improved GWR model. Our study on Pingtan Island provides theoretical and technical support for monitoring and evaluating soil arsenic concentrations using satellite-based spectroscopy in densely populated, relatively independent island towns in China and worldwide. Full article

(This article belongs to the Special Issue New Advances in Machine Learning for Soil Properties Prediction and Mapping)

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