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New Advances in Machine Learning for Soil Properties Prediction and Mapping

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 2311

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Dr. Ruhollah Taghizadeh-Mehrjardi

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Guest Editor

Soil Science and Geomorphology, University of Tübingen, Tübingen, Germany
Interests: digital soil mapping; machine learning; pedology; remote sensing
Special Issues, Collections and Topics in MDPI journals

Dr. Mojtaba Zeraatpisheh

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Guest Editor

Rubenstein School of Environment and Natural Resources, The University of Vermont, Burlington, VT, USA
Interests: soil science; soil formation; soil health; digital soil mapping; machine learning
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Dr. Mahdi Hasanlou

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Guest Editor

School of Surveying and Geospatial Engineering, College of Engineering, University of Tehran, Tehran, Iran
Interests: remote sensing; information systems; computer science applications; ecological modeling
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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

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 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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Research

34 pages, 15812 KiB

Open AccessArticle

Exploring the Potential of PRISMA Satellite Hyperspectral Image for Estimating Soil Organic Carbon in Marvdasht Region, Southern Iran

by Mehdi Golkar Amoli, Mahdi Hasanlou, Ruhollah Taghizadeh Mehrjardi and Farhad Samadzadegan

Remote Sens. 2024, 16(12), 2149; https://doi.org/10.3390/rs16122149 - 13 Jun 2024

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Abstract

Soil organic carbon (SOC) is a crucial factor for soil fertility, directly impacting agricultural yields and ensuring food security. In recent years, remote sensing (RS) technology has been highly recommended as an efficient tool for producing SOC maps. The PRISMA hyperspectral satellite was used in this research to predict the SOC map in Fars province, located in southern Iran. The main purpose of this research is to investigate the capabilities of the PRISMA satellite in estimating SOC and examine hyperspectral processing techniques for improving SOC estimation accuracy. To this end, denoising methods and a feature generation strategy have been used. For denoising, three distinct algorithms were employed over the PRISMA image, including Savitzky–Golay + first-order derivative (SG + FOD), VisuShrink, and total variation (TV), and their impact on SOC estimation was compared in four different methods: Method One (reflectance bands without denoising, shown as M#1), Method Two (denoised with SG + FOD, shown as M#2), Method Three (denoised with VisuShrink, shown as M#3), and Method Four (denoised with TV, shown as M#4). Based on the results, the best denoising algorithm was TV (Method Four or M#4), which increased the estimation accuracy by about 27% (from 40% to 67%). After TV, the VisuShrink and SG + FOD algorithms improved the accuracy by about 23% and 18%, respectively. In addition to denoising, a new feature generation strategy was proposed to enhance accuracy further. This strategy comprised two main steps: first, estimating the number of endmembers using the Harsanyi–Farrand–Chang (HFC) algorithm, and second, employing Principal Component Analysis (PCA) and Independent Component Analysis (ICA) transformations to generate high-level features based on the estimated number of endmembers from the HFC algorithm. The feature generation strategy was unfolded in three scenarios to compare the ability of PCA and ICA transformation features: Scenario One (without adding any extra features, shown as S#1), Scenario Two (incorporating PCA features, shown as S#2), and Scenario Three (incorporating ICA features, shown as S#3). Each of these three scenarios was repeated for each denoising method (M#1–4). After feature generation, high-level features were added to the outputs of Methods One, Three, and Four. Subsequently, three machine learning algorithms (LightGBM, GBRT, RF) were employed for SOC modeling. The results showcased the highest accuracy when features obtained from PCA transformation were added to the results from the TV algorithm (Method Four—Scenario Two or M#4–S#2), yielding an R² of 81.74%. Overall, denoising and feature generation methods significantly enhanced SOC estimation accuracy, escalating it from approximately 40% (M#1–S#1) to 82% (M#4–S#2). This underscores the remarkable potential of hyperspectral sensors in SOC studies. Full article

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

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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 - 4 Sep 2023

Cited by 1 | Viewed by 1264

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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