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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 for Geospatial Science".

Deadline for manuscript submissions: closed (30 June 2024) | Viewed by 21440

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Special Issue Editors

Dr. Xiaoling Wu

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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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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
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Dr. Liujun Zhu

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Yangtze Institute for Conservation and Development, Hohai University, Nanjing 211100, China
Interests: satellite image analysis; satellite image processing; radar remote sensing; soil moisture
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Dr. Xiaoji Shen

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Yangtze Institute for Conservation and Development, Hohai University, Nanjin 211100, China
Interests: remote sensing in ecology and hydrology; microwave remote sensing
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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

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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

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

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GIS and Remote Sensing in Soil Mapping and Modeling (Second Edition) in Remote Sensing (4 articles)

Published Papers (11 papers)

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22 pages, 7615 KiB

Open AccessArticle

Landslide Hazard Prediction Based on Small Baseline Subset–Interferometric Synthetic-Aperture Radar Technology Combined with Land-Use Dynamic Change and Hydrological Conditions (Sichuan, China)

by Hongyi Guo and A. M. Martínez-Graña

Remote Sens. 2024, 16(15), 2715; https://doi.org/10.3390/rs16152715 - 24 Jul 2024

Cited by 2 | Viewed by 1425

Abstract

Le’an Town, located in the southwest of Qingchuan County, Guangyuan City, Sichuan Province, boasts a unique geographical position. The town’s terrain is complex, and its geological environment is fragile. Multiple phases of tectonic movements have resulted in numerous cracks and faults, making the area prone to landslides, debris flows, and other disasters. Additionally, heavy rainfall and fluctuating groundwater levels further exacerbate the instability of the mountains. Human activities, such as overdevelopment and deforestation, have significantly increased the risk of geological disasters. Currently, the methods for landslide prediction in Le’an Town are limited; traditional techniques cannot provide precise forecasts, and the study area is largely covered by tall vegetation. Therefore, this paper proposes a method that combines SBAS-InSAR technology with dynamic changes in land use and hydrological conditions. SBAS-InSAR technology is used to obtain surface deformation information, while land-use changes and hydrological condition data are incorporated to analyze the dynamic characteristics and potential influencing factors of landslide areas. The innovation of this method lies in its high-precision surface deformation monitoring capability and the integration of multi-source data, which can more comprehensively reveal the geological environmental characteristics of the study area, thereby achieving accurate predictions of landslide development. The study results indicate that the annual subsidence rate in most deformation areas of Le’an Town ranges from −10 to 0 mm, indicating slow subsidence. In some areas, the subsidence rate exceeds −50 mm per year, showing significant slope aspect differences, reflecting the combined effects of geological structures, climatic conditions, and human activities. It is evident that land-use changes and hydrological conditions have a significant impact on the occurrence and development of landslides. Therefore, by utilizing SBAS-InSAR technology and cross-verifying it with other techniques, the consistency of identified landslide deformation areas can be enhanced, thereby improving results. This method provides a scientific basis for the monitoring and early warning of landslide disasters and has important practical application value. Full article

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

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14 pages, 5602 KiB

Open AccessArticle

Surface Soil Moisture Estimation from Time Series of RADARSAT Constellation Mission Compact Polarimetric Data for the Identification of Water-Saturated Areas

by Igor Zakharov, Sarah Kohlsmith, Jon Hornung, François Charbonneau, Pradeep Bobby and Mark Howell

Remote Sens. 2024, 16(14), 2664; https://doi.org/10.3390/rs16142664 - 21 Jul 2024

Cited by 1 | Viewed by 1151

Abstract

Soil moisture is one of the main factors affecting microwave radar backscatter from the ground. While there are other factors that affect backscatter levels (for instance, surface roughness, vegetation, and incident angle), relative variations in soil moisture can be estimated using space-based, medium resolution, multi-temporal synthetic aperture radar (SAR). Understanding the distribution and identification of water-saturated areas using SAR soil moisture can be important for wetland mapping. The SAR soil moisture retrieval algorithm provides a relative assessment and requires calibration over wet and dry periods. In this work, relative soil moisture indicators are derived from a time series of the RADARSAT Constellation Mission (RCM) SAR compact polarimetric (CP) data over reclaimed areas of an oil sands mine in Alberta, Canada. An evaluation of the soil moisture product is performed using in situ measurements showing agreement from June to September. The surface scattering component of m-chi CP decomposition and the RL SAR products demonstrated a good agreement with the field data (low RMSE values and a perfect alignment with field-identified wetlands). Full article

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

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18 pages, 3681 KiB

Open AccessArticle

Random Forest-Based Soil Moisture Estimation Using Sentinel-2, Landsat-8/9, and UAV-Based Hyperspectral Data

by Hadi Shokati, Mahmoud Mashal, Aliakbar Noroozi, Ali Akbar Abkar, Saham Mirzaei, Zahra Mohammadi-Doqozloo, Ruhollah Taghizadeh-Mehrjardi, Pegah Khosravani, Kamal Nabiollahi and Thomas Scholten

Remote Sens. 2024, 16(11), 1962; https://doi.org/10.3390/rs16111962 - 29 May 2024

Cited by 8 | Viewed by 3556

Abstract

Accurate spatiotemporal monitoring and modeling of soil moisture (SM) is of paramount importance for various applications ranging from food production to climate change adaptation. This study deals with modeling SM with the random forest (RF) algorithm using datasets comprising multispectral data from Sentinel-2, Landsat-8/9, and hyperspectral data from the CoSpectroCam sensor (CSC, licensed to AgriWatch BV, Enschede, The Netherlands) mounted on an unmanned aerial vehicle (UAV) in Iran. The model included nine bands from Landsat-8/9, 11 bands from Sentinel-2, and 1252 bands from the CSC (covering the wavelength range between 420 and 850 nm). The relative feature importance and band sensitivity to SM variations were analyzed. In addition, four indices, including the perpendicular index (PI), ratio index (RI), difference index (DI), and normalized difference index (NDI) were calculated from the different bands of the datasets, and their sensitivity to SM was evaluated. The results showed that the PI exhibited the highest sensitivity to SM changes in all datasets among the four indices considered. Comparisons of the performance of the datasets in SM estimation emphasized the superior performance of the UAV hyperspectral data (R² = 0.87), while the Sentinel-2 and Landsat-8/9 data showed lower accuracy (R² = 0.49 and 0.66, respectively). The robust performance of the CSC data is likely due to its superior spatial and spectral resolution as well as the application of preprocessing techniques such as noise reduction and smoothing filters. The lower accuracy of the multispectral data from Sentinel-2 and Landsat-8/9 can also be attributed to their relatively coarse spatial resolution compared to the CSC, which leads to pixel non-uniformities and impurities. Therefore, employing the CSC on a UAV proves to be a valuable technology, providing an effective link between satellite observations and ground measurements. Full article

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

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18 pages, 5088 KiB

Open AccessArticle

Synergistic Use of Multi-Temporal Radar and Optical Remote Sensing for Soil Organic Carbon Prediction

by Sara Dahhani, Mohamed Raji and Yassine Bouslihim

Remote Sens. 2024, 16(11), 1871; https://doi.org/10.3390/rs16111871 - 24 May 2024

Cited by 10 | Viewed by 1698

Abstract

Exploring soil organic carbon (SOC) mapping is crucial for addressing critical challenges in environmental sustainability and food security. This study evaluates the suitability of the synergistic use of multi-temporal and high-resolution radar and optical remote sensing data for SOC prediction in the Kaffrine region of Senegal, covering over 1.1 million hectares. For this purpose, various scenarios were developed: Scenario 1 (Sentinel-1 data), Scenario 2 (Sentinel-2 data), Scenario 3 (Sentinel-1 and Sentinel-2 combination), Scenario 4 (topographic features), and Scenario 5 (Sentinel-1 and -2 with topographic features). The findings from comparing three different algorithms (Random Forest (RF), XGBoost, and Support Vector Regression (SVR)) with 671 soil samples for training and 281 samples for model evaluation highlight that RF outperformed the other models across different scenarios. Moreover, using Sentinel-2 data alone yielded better results than using only Sentinel-1 data. However, combining Sentinel-1 and Sentinel-2 data (Scenario 3) further improved the performance by 6% to 11%. Including topographic features (Scenario 5) achieved the highest accuracy, reaching an R² of 0.7, an RMSE of 0.012%, and an RPIQ of 5.754 for the RF model. Applying the RF and XGBoost models under Scenario 5 for SOC mapping showed that both models tended to predict low SOC values across the study area, which is consistent with the predominantly low SOC content observed in most of the training data. This limitation constrains the ability of ML models to capture the full range of SOC variability, particularly for less frequent, slightly higher SOC values. Full article

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

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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

Cited by 1 | Viewed by 1556

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

Cited by 5 | Viewed by 2022

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

Cited by 1 | Viewed by 2211

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 2646

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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16 pages, 4971 KiB

Open AccessTechnical Note

Constructing Soil–Landscape Units Based on Slope Position and Land Use to Improve Soil Prediction Accuracy

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

Remote Sens. 2024, 16(21), 4090; https://doi.org/10.3390/rs16214090 - 1 Nov 2024

Viewed by 1210

Abstract

Topography is one of the dominant factors in regional soil formation and development. Soil distribution has a certain pattern from high to low in space, and this pattern has a high degree of consistency with slope position. Most of the current research on soil mapping uses landscape types generated by existing methods directly as environmental covariates, and there are few landscape classification methods specifically oriented toward soil surveys. There is rarely any research on landform classification using relative slope position (RSP) and elevation. Therefore, we designed a landform classification method based on RSP and elevation, Terrainforms (TF), and combined the landform type with land use type to construct soil–landscape units for soil type and attribute spatial prediction. In this study, two commonly used landform classification methods, Geomorphons and Landforms, were also used to compare with this design method. It was found that the constructed soil–landscape units had a high consistency with the soil spatial distribution. The landform types based on RSP and elevation obtained the second-highest prediction accuracy in both soil type and soil organic carbon (SOC), and the constructed soil–landscape types obtained the highest prediction accuracy. The results show that the landform classification method based on RSP and elevation is not easily limited by the analysis scale, and is an efficient and accurate landform classification method. The TF landform type and its constructed soil–landscape types can be used as an important environmental variable in soil prediction and sampling, which can provide some guidance and reference for landform classification and digital soil mapping. Full article

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

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11 pages, 1945 KiB

Open AccessTechnical Note

The Right Triangle Model: Overcoming the Sparse Data Problem in Thermal/Optical Remote Sensing of Soil Moisture

by Toby N. Carlson

Remote Sens. 2024, 16(17), 3231; https://doi.org/10.3390/rs16173231 - 31 Aug 2024

Cited by 1 | Viewed by 1153

Abstract

The simplicity of the so-called triangle method allows estimates of evapotranspiration and soil water content to be made without ancillary data external to the image and with just a few simple algebraic calculations. Drawing on many examples in the literature showing that the pixel distribution in temperature/fractional vegetation cover (NDVI) space closely resembles a right triangle, this paper shows that adoption of a right triangle shape further simplifies the triangle model. Moreover, it allows one to mostly avoid the problem of sparse or low-resolution data. A time dimension can be included showing that trajectories inside the triangle can provide additional information on root zone soil water content. After discussing some of the ambiguities in the triangle method, and the advantageous properties of the right triangle, a proposal is made to illuminate the relationship between thermal/optical measurements and root zone water content within the right triangle framework. Full article

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

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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

Cited by 2 | Viewed by 1550

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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