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GIS and Remote Sensing in Soil Mapping and Modeling (Second...
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GIS and Remote Sensing in Soil Mapping and Modeling (Second Edition)

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

Deadline for manuscript submissions: 25 May 2025 | Viewed by 2698

Special Issue Editors

Dr. Xiaoling Wu

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Guest Editor
Department of Civil Engineering, Monash University, Melbourne, Australia
Interests: GIS and remote sensing; environmental science; soil science; precision agriculture; smart irrigation
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; Google Earth Engine; remote sensing; agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Liujun Zhu

E-Mail Website
Guest Editor
1. Yangtze Institute for Conservation and Development, Hohai University, Nanjing 210024, China
2. Department of Civil Engineering, Monash University, Clayton, Australia
Interests: remote sensing; radar; water resource; intelligent agriculture; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Dr. Xiaoji Shen

E-Mail Website
Guest Editor
Yangtze Institute for Conservation and Development, Hohai University, Nanjin 211100, China
Interests: remote sensing in ecology and hydrology; microwave remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Due to the great support for and interest in the previous Special Issue, we can now announce a second edition of “GIS and Remote Sensing in Soil Mapping and Modeling"; we would like to thank all the authors and co-authors who contributed to the successful first edition of this Special Issue and look forward to receiving more experts' innovative contributions.

Soil is one of the most important natural resources on our planet and is essential for sustainable agriculture and food production. However, the 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 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, contributing 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

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Further information on MDPI's Special Issue policies can be found here.

Related Special Issue

Published Papers (4 papers)

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Research

28 pages, 5379 KiB  
Article
Influences of Sampling Design and Model Selection on Predictions of Chemical Compounds in Petroferric Formations in the Brazilian Amazon
by Niriele Bruno Rodrigues, Theresa Rocco Barbosa, Helena Saraiva Koenow Pinheiro, Marcelo Mancini, Quentin D. Read, Joshua Blackstock, Edwin H. Winzeler, David Miller, Phillip R. Owens and Zamir Libohova
Remote Sens. 2025, 17(9), 1644; https://doi.org/10.3390/rs17091644 - 6 May 2025
Viewed by 234
Abstract
Morro de Seis Lagos, a region in the Brazilian Amazon, contains a small (less than 1%) formation of siderite carbonatites which is considered to be one of the world’s largest niobium reserves. This highly weathered geological and pedological occurrence makes the site ideal [...] Read more.
Morro de Seis Lagos, a region in the Brazilian Amazon, contains a small (less than 1%) formation of siderite carbonatites which is considered to be one of the world’s largest niobium reserves. This highly weathered geological and pedological occurrence makes the site ideal for studying the pedogenetic process of lateralization and the spatial variability of chemical elements. The aim of this study was to investigate the influences of various sampling combinations (scenarios) derived from three sampling designs on the spatial predictions associated with chemical compounds (Al2O3, Fe2O3, MnO, Nb2O5, TiO2, and SiO2), using multiple machine learning algorithms combined with remotely sensed imagery. The dataset comprised 341 samples from the Geological Survey of Brazil (CPRM). Covariates included remotely sensed data collected from Sentinel-2 MSI, Sentinel-1A, and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and topographic attributes were calculated from a 20 m digital elevation model derived from hydrologic data (HC-DEM). The machine learning algorithms (Generalized Linear Models with Elastic Net Regularization (GLMNET), Nearest Neighbors (KNN), Neural Network (NNET), Random Forest (RF) and Support Vector Machine (SVMRadial) were used in combination with covariates and measured elements at point locations to spatially map the concentrations of these chemical elements. The optimal covariates for modeling were selected using Recursive Feature Elimination (RFE), processing 10 runs for each chemical element. The RF, SVMRadial, and KNN models performed best, followed by the models from the Neural Network group (NNET). The sampling scenarios were not significantly different, based on root mean square error (F = 1.7; p-value = 0.15) and mean absolute error (F = 0.4; p-value = 0.79); however, significant differences were observed in the coefficient of determination (F = 41.2; p-value < 0.00) across all models. Overall, the models performed poorly for all elements, with R2 ranging from 0.07 to 0.27, regardless of sampling scenario (F = 1.6; p-value = 0.08). Relatively, RF, GLMET, and KNN performed better, compared to other models. The terrain attributes were significantly more successful as to the spatial predictions of the elements contained in laterites than were the remote sensing spectral indices, likely due to the fact that the underlying spatial structures of the two formations (laterite and talus) occur at different elevations. Full article
(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling (Second Edition))
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23 pages, 5331 KiB  
Article
Unveiling the Effects of Crop Rotation on Cropland Soil pH Mapping: A Remote Sensing-Based Soil Sample Grouping Strategy
by Yuan Liu, Songchao Chen, Ge Shen, Cheng Chen, Zejiang Cai, Ji Zhu, Xia Zhang, Guofei Shang, Qingbo Zhou, Sonoko Dorothea Bellingrath-Kimura, Qiangyi Yu and Wenbin Wu
Remote Sens. 2025, 17(9), 1643; https://doi.org/10.3390/rs17091643 - 6 May 2025
Viewed by 221
Abstract
Crop rotation affects soil pH by disturbing H+ production and consumption within soil–crop systems, primarily through fertilization, irrigation, cropping, and harvest. Studies have shown that crop rotation improves soil organic matter prediction. However, simply incorporating crop rotation may not significantly improve soil [...] Read more.
Crop rotation affects soil pH by disturbing H+ production and consumption within soil–crop systems, primarily through fertilization, irrigation, cropping, and harvest. Studies have shown that crop rotation improves soil organic matter prediction. However, simply incorporating crop rotation may not significantly improve soil pH prediction, because the spatial variability in soil pH is lower and the way crop rotation influences pH is different. To quantify the extent to which crop rotation improves soil pH mapping, we introduced the strategy of grouping soil samples by crop rotation and modeling separately. We chose a typical multiple-cropping region suffering soil acidification in Southern China, where the complex crop rotation was mapped by Sentinel-1/2 time series and a legend featuring three main systems (i.e., paddy, vegetable, and orchard) and nine subsystems. This crop rotation map was then combined with other variables to derive multiple combinations and predict soil pH. Based on the best combination, we further assessed the grouping strategy. The results showed that simply incorporating crop rotation in one joint model was useful but could not obtain the expected accuracy, with a root mean squared error (RMSE) of 0.66 and an R2 of 0.36. The individual statistical accuracies were quite low for the vegetable and orchard rotations, with an RMSE of 0.77/0.70 and an R2 of 0.30/−0.04. Grouping soil samples by crop rotation significantly enhanced soil pH predictability with a decrease in the RMSE of 15% and an increase in the R2 of 53%. The results proved that grouping by crop rotation can fit and optimize the sub-models after learning the characteristics of the rotation subsamples, offering a way for improving digital mapping of soil pH over heterogeneous agricultural landscapes. Full article
(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling (Second Edition))
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18 pages, 4411 KiB  
Article
High-Resolution Mapping of Topsoil Sand Content in Planosol Regions Using Temporal and Spectral Feature Optimization
by Jiaying Meng, Nanchen Chu, Chong Luo, Huanjun Liu and Xue Li
Remote Sens. 2025, 17(3), 553; https://doi.org/10.3390/rs17030553 - 6 Feb 2025
Viewed by 667
Abstract
Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, [...] Read more.
Soil sand content is an important characterization index of soil texture, which directly affects soil water regulation, nutrient cycling, and crop growth potential. Therefore, its high-precision spatial distribution information is of great importance for agricultural resource management and land use. In this study, a remote sensing prediction method based on the combination of time-phase optimization and spectral feature preference is innovatively proposed for improving the mapping accuracy of the sand content in the till layer of a planosol area. The study first analyzed the prediction performance of single-time-phase images, screened the optimal time-phase (May), and constructed a single-time-phase model, which achieved significant prediction accuracy, with a coefficient of determination (R2) of 0.70 and a root mean square error (RMSE) of 1.26%. Subsequently, the model was further optimized by combining multiple time phases, and the prediction accuracy was improved to R2 = 0.77 and the RMSE decreased to 1.10%. At the feature level, the recursive feature elimination (RF-RFE) method was utilized to preferentially select 19 key spectral variables from the initial feature set, among which the short-wave infrared bands (b11, b12) and the visible bands (b2, b3, b4) contributed most significantly to the prediction. Finally, the prediction accuracy was further improved to R2 = 0.79 and RMSE = 1.05% by multi-temporal-multi-feature fusion modeling. The spatial distribution map of sand content generated by the optimized model shows that areas with high sand content are primarily located in the northern and central regions of Shuguang Farm. This study not only provides a new technical path for accurate mapping of soil texture in the planosol area, but also provides a reference for the improvement of remote sensing monitoring methods in other typical soil areas. The research results can provide a reference for mapping high-resolution soil sand maps over a wider area in the future. Full article
(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling (Second Edition))
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25 pages, 8935 KiB  
Article
Soil Reflectance Composite for Digital Soil Mapping in a Mediterranean Cropland District
by Monica Zanini, Uta Heiden, Leonardo Pace, Raffaele Casa and Simone Priori
Remote Sens. 2025, 17(1), 89; https://doi.org/10.3390/rs17010089 - 29 Dec 2024
Cited by 1 | Viewed by 1029
Abstract
Accurate soil maps are essential for soil protection, management, and digital agriculture. However, traditional soil maps often lack the detail required for local applications, while farm-scale surveys are often not economically viable. This study uses legacy soil data and digital soil mapping (DSM) [...] Read more.
Accurate soil maps are essential for soil protection, management, and digital agriculture. However, traditional soil maps often lack the detail required for local applications, while farm-scale surveys are often not economically viable. This study uses legacy soil data and digital soil mapping (DSM) to produce accurate, low-cost maps of key soil properties, namely clay, sand, total lime (CaCO3), organic carbon (SOC), total nitrogen (TN), and the cation-exchange capacity (CEC). The DSM procedure involved multivariate stepwise regression kriging that uses the terrain attributes and bare soil reflectance composite (SRC) from Sentinel-2 multitemporal images. The procedure to obtain the SRC was carried out following the Soil Composite Mapping Processor (SCMaP) methodology. The Sentinel-2 bands of the SRC showed strong correlations with soil features, making them very suitable explicative variables for regression kriging. In particular, the SWIR bands (b11 and b12) were important covariates in predicting clay, sand, and CEC maps. The accuracy of the regression models was very good for clay, sand, SOC, and CEC (R2 > 0.90), while CaCO3 showed lower accuracy (R2 = 0.67). Normalization of SOC, TN, and CaCO3 did not significantly improve the prediction accuracy, except for SOC, which showed a slight improvement. In addition, a supervised classification approach was applied to predict soil typological units (STUs) using the mapped soil attributes. This methodology demonstrates the potential of SRCs and regression kriging to produce detailed soil property maps to support precision agriculture and sustainable land management. Full article
(This article belongs to the Special Issue GIS and Remote Sensing in Soil Mapping and Modeling (Second Edition))
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