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GIS and Remote Sensing in Soil Mapping and Modeling

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing and Geo-Spatial Science".

Deadline for manuscript submissions: 30 June 2024 | Viewed by 4110

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Special Issue Editors

Dr. Xiaoling Wu

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Guest Editor

Department of Civil Engineering, Monash University, Melbourne, Australia
Interests: remote sensing of environment; automated farming; water quality; flood and bushfire prediction; robotics; machine learning
Special Issues, Collections and Topics in MDPI journals

Dr. Chong Luo

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Guest Editor

Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, No. 4888, Shengbei Street, Changchun 130102, China
Interests: crop mapping; soil mapping
Special Issues, Collections and Topics in MDPI journals

Dr. Liujun Zhu

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Guest Editor

Yangtze Institute for Conservation and Development, Hohai University, Nanjing 211100, China
Interests: satellite image analysis; satellite image processing; radar remote sensing; soil moisture
Special Issues, Collections and Topics in MDPI journals

Dr. Xiaoji Shen

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Guest Editor

Yangtze Institute for Conservation and Development, Hohai University, Nanjing 211100, China
Interests: eco-hydrological remote sensing; microwave remote sensing

Special Issue Information

Dear Colleagues,

Soil is one of the most important natural resources on our planet and is essential for sustainable agriculture and food production. However, mapping and modeling soil properties across large areas can be a challenging task due to the complex nature of soil variability. Therefore, the use of advanced GIS and remote sensing technologies can greatly aid in soil mapping and modeling.

We encourage submissions that highlight the use of cutting-edge GIS and remote sensing technologies, as well as those that address the practical applications of soil mapping and modeling in real-world scenarios. Overall, this Special Issue will provide a platform for researchers and practitioners to share their knowledge and experiences in the field of soil mapping and modeling, and contribute to the advancement of this important area of research. This Special Issue invites original research articles, reviews, and case studies on the following topics:

Remote sensing data for soil mapping and modeling;
GIS-based soil mapping and modeling;
Machine learning and artificial intelligence for soil mapping and modeling;
Spatial and temporal analysis of soil properties;
Integration of soil data with other environmental data;
Uncertainty and error analysis in soil mapping and modeling;
Applications of soil mapping and modeling in agriculture, forestry, and land-use planning.

Dr. Xiaoling Wu
Dr. Chong Luo
Dr. Liujun Zhu
Dr. Xiaoji Shen
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

GIS
remote sensing
soil science
soil moisture
spatial modeling
climate change
precision agriculture

Published Papers (5 papers)

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Research

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17 pages, 7990 KiB

Open AccessArticle

Mapping Dissolved Organic Carbon and Organic Iron by Comparing Deep Learning and Linear Regression Techniques Using Sentinel-2 and WorldView-2 Imagery (Byers Peninsula, Maritime Antarctica)

by Susana del Carmen Fernández, Rubén Muñiz, Juanjo Peón, Ricardo Rodríguez-Cielos, Jesús Ruíz and Javier F. Calleja

Remote Sens. 2024, 16(7), 1192; https://doi.org/10.3390/rs16071192 - 28 Mar 2024

Viewed by 758

Abstract

Byers Peninsula is considered one of the largest ice-free areas in maritime Antarctica. Since 2006, the Spanish Polar Program has taken part in a large number of environmental studies involving the effects of climate change on biological life cycles, limnology, and microbiology. Soils from maritime Antarctica are generally weakly developed and have chemical, physical, and morphological characteristics that are strongly influenced by the parent material. However, biological activity during the short Antarctic summer promotes intense transference of nutrients and organic matter in areas occupied by different species of birds and marine mammals. Mapping and monitoring those areas that are highly occupied by various species could be very useful to create models prepared from satellite images of the edaphic properties. In this approach, deep learning and linear regression models of the soil properties and spectral indexes, which were considered as explicative variables, were used. We trained the models on soil properties closely related to biological activity such as dissolved organic carbon (DOC) and the iron fraction associated with the organic matter (Fe). We tested the best approach to model the spatial distribution of DOC, Fe, and pH by training the linear regression and deep learning models on Sentinel-2 and WorldView-2 images. The most robust models, the pH model built with the deep learning approach on Sentinel images (MAE of 0.51, RMSE of 0.70, and R2 with a residual of −0.49), the DOC model built with linear regression on Sentinel images (MAE of 189.39, RMSE of 342.23, and R2 with a residual of 0.0), and the organic Fe model built with deep learning (MAE of 116.20, RMSE of 209.93, and R2 of −0.05), were used to track possible areas with ornithogenic soils, as well as areas of Byers Peninsula that could be supporting the highest biological development. Full article

(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling)

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14 pages, 2863 KiB

Open AccessArticle

Digital Mapping of Soil Particle Size Fractions in the Loess Plateau, China, Using Environmental Variables and Multivariate Random Forest

by Wenjie He, Zhiwei Xiao, Qikai Lu, Lifei Wei and Xing Liu

Remote Sens. 2024, 16(5), 785; https://doi.org/10.3390/rs16050785 - 24 Feb 2024

Viewed by 514

Abstract

Soil particle size fractions (PSFs) are important properties for understanding the physical and chemical processes in soil systems. Knowledge about the distribution of soil PSFs is critical for sustainable soil management. Although log-ratio transformations have been widely applied to soil PSFs prediction, the statistical distribution of original data and the transformed data given by log-ratio transformations is different, resulting in biased estimates of soil PSFs. Therefore, multivariate random forest (MRF) was utilized for the simultaneous prediction of soil PSFs, as it is able to capture dependencies and internal relations among the three components. Specifically, 243 soil samples collected across the Loess Plateau were used. Meanwhile, Landsat data, terrain attributes, and climatic variables were employed as environmental variables for spatial prediction of soil PSFs. The results depicted that MRF gave satisfactory soil PSF prediction performance, where the R² values were 0.62, 0.53, and 0.73 for sand, silt, and clay, respectively. Among the environmental variables, nighttime land surface temperature (LST_N) presented the highest importance in predicting soil PSFs in the Loess Plateau, China. Maps of soil PSFs and texture were generated at a 30 m resolution, which can be utilized as alternative data for soil erosion management and ecosystem conservation. Full article

(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling)

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25 pages, 12990 KiB

Open AccessArticle

Methodology for Regional Soil Organic Matter Prediction with Spectroscopy: Optimal Sample Grouping, Input Variables, and Prediction Model

by Xinle Zhang, Chang Dong, Huanjun Liu, Xiangtian Meng, Chong Luo, Yongqi Han and Hongfu Ai

Remote Sens. 2024, 16(3), 565; https://doi.org/10.3390/rs16030565 - 31 Jan 2024

Viewed by 873

Abstract

Soil organic matter (SOM) is an essential component of soil and is crucial for increasing agricultural production and soil fertility. The combination of hyperspectral remote sensing and deep learning can be used to predict the SOM content efficiently, rapidly, and cost-effectively on various scales. However, determining the optimal groups, inputs, and models for reducing the spatial heterogeneity of soil nutrients in large regions and to improve the accuracy of SOM prediction remains a challenge. Hyperspectral reflectance data from 1477 surface soil samples in Northeast China were utilized to evaluate three grouping methods (no groups (NG), traditional grouping (TG), and spectral grouping (SG)) and four inputs (raw reflectance (RR), continuum removal (CR), fractional-order differentiation (FOD), and spectral characteristic parameters (SCPs)). The SOM prediction accuracies of random forest (RF), convolutional neural network (CNN), and long short-term memory (LSTM) models were assessed. The results were as follows: (1) The highest accuracy was achieved using SG, SCPs, and the LSTM model, with a coefficient of determination (R2) of 0.82 and a root mean squared error (RMSE) of 0.69%. (2) The LSTM model exhibited the highest accuracy in SOM prediction (R2 = 0.82, RMSE = 0.89%), followed by the CNN model (R2 = 0.72, RMSE = 0.85%) and the RF model (R2 = 0.69, RMSE = 0.91%). (3) The SG provided higher SOM prediction accuracy than TG and NG. (4) The SCP-based prediction results were significantly better than those of the other inputs. The R2 of the SCP-based model was 0.27 higher and the RMSE was 0.40% lower than that of the RR-based model with NG. In addition, the LSTM model had higher prediction errors at low (0–2%) and high (8–10%) SOM contents, whereas the error was minimal at intermediate SOM contents (2–8%). The study results provide guidance for selecting grouping methods and approaches to improve the prediction accuracy of the SOM content and reduce the spatial heterogeneity of the SOM content in large regions. Full article

(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling)

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22 pages, 15662 KiB

Open AccessArticle

Soil Classification Mapping Using a Combination of Semi-Supervised Classification and Stacking Learning (SSC-SL)

by Fubin Zhu, Changda Zhu, Wenhao Lu, Zihan Fang, Zhaofu Li and Jianjun Pan

Remote Sens. 2024, 16(2), 405; https://doi.org/10.3390/rs16020405 - 20 Jan 2024

Cited by 1 | Viewed by 818

Abstract

In digital soil mapping, machine learning models have been widely applied. However, the accuracy of machine learning models can be limited by the use of a single model and a small number of soil samples. This study introduces a novel method, semi-supervised classification combined with stacking learning (SSC-SL), to enhance soil classification mapping in hilly and low-mountain areas of Northern Jurong City, Jiangsu Province, China. This study incorporated Gaofen-2 (GF-2) remote sensing imagery along with its associated remote sensing indices, the ALOS Digital Elevation Model (DEM) and their derived topographic factors, and soil parent material data in its modelling process. We first used three base learners, Ranger, Rpart, and XGBoost, to construct the SL model. In addition, we employed the fuzzy c-means clustering algorithm (FCM) to construct a clustering map. To fully leverage the information from a multitude of environmental variables, understand the distribution of data, and enhance the effectiveness of the classification, we selected unlabelled samples near the boundaries of the patches on the clustering map. The SSC-SL model demonstrated superior stability and performance, with optimal accuracy at a 0.9 confidence level, achieving an overall accuracy of 0.77 and a kappa coefficient of 0.73. These metrics exceeded those of the highest performing base learner (Ranger model) by 10.4% and 12.3%, respectively, and they outperformed the least effective base learner (Rpart model) by 27.3% and 32.9%. It notably improves the spatial distribution accuracy of soil types. Key environmental variables influencing soil type distribution include soil parent material (SPM), land use (LU), the multi-resolution valley bottom flatness index (MRVBF), and Elevation (Ele). In conclusion, the SSC-SL model offers a novel and effective approach for enhancing the predictive accuracy of soil classification mapping. Full article

(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling)

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Other

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14 pages, 2801 KiB

Open AccessTechnical Note

Extracting Typical Samples Based on Image Environmental Factors to Obtain an Accurate and High-Resolution Soil Type Map

by Changda Zhu, Fubin Zhu, Cheng Li, Yunxin Yan, Wenhao Lu, Zihan Fang, Zhaofu Li and Jianjun Pan

Remote Sens. 2024, 16(7), 1128; https://doi.org/10.3390/rs16071128 - 23 Mar 2024

Viewed by 543

Abstract

Soil surveying and mapping provide important support for environmental science research on soil and other resources. Due to the rapid change in land use and the long update cycle of soil maps, historical conventional soil maps (CSMs) may be outdated and have low accuracy. Therefore, there is an urgent need for accurate and up-to-date soil maps. Soil has a high correlation with its corresponding environmental factors in space, and typical samples contain an appropriate soil–environment relationship of soil types. Understanding how to extract typical samples according to environmental factors and determine the implied soil–environment relationship is the key to updating soil maps. In this study, a hierarchical typical sample extraction method based on land use type and environmental factors was designed. According to the corresponding relationship between the soil type and the land use type (ST-LU), the outdate soil map patches caused by changes in land use were excluded, follow by typical samples being extracted according to the peak intervals of the soil–environmental factor histograms. Additionally, feature selection was performed through variance analysis and mutual information, and four machine learning models were used to predict soil types. In addition, the influence of environmental factors on soil prediction was discussed, in terms of variable importance analysis. Using an overall common validation set, the results show that the prediction accuracy using typical samples for learning in the modeling set is above 0.8, while the prediction accuracy when using random samples is only about 0.4. Compared with the original soil map, the accuracy and resolution of the predicted soil maps based on typical samples are greatly improved. In general, typical samples can effectively explore the actual soil–environment knowledge implied in the soil type map. By extracting typical samples from historical soil type map and combining them with high-resolution remote sensing data, we can generate new soil type maps with high accuracy and short update cycle. This can provide some references for typical sampling design and soil type prediction. Full article

(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling)

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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

Title: Physical understanding remote measurements of soil water content derived from the triangle method
Author: Carlson
Highlights: * Triangle Method elucidated * Inherent errors in using thermal/optical methods to estimate root zone soil water content * Suggest new approach to evaluate triangle data

Title: Mapping Dissolved Organic Carbon (DOC) and Organic Iron comparing Deep Learning and Linear Regression using Sentinel and World View 2 images (Byers Peninsula, Marine Antarctica).
Author: Fernández
Highlights: Maritime Antarctica, Dissolved Organic Carbon, Organic Iron, soil mapping, Linear Regresion , Deep learning , Sentinel images, Word View 2 images

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