remotesensing-logo

Journal Browser

Journal Browser

Remote Sensing of Soil Properties

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

Deadline for manuscript submissions: closed (30 November 2021) | Viewed by 20338

Special Issue Editors

US Geological Survey, Southwest Biological Science Center, Moab, UT 84532, USA
Interests: soils; soil survey; remote sensing; spatial analysis; soil characterization; python; R; forest ecology; drylands ecology; pedology; ecological sites; GIS; sample design; machine learning
Sustainability Innovation Lab at Colorado (SILC), University of Colorado, Boulder, CO 80303, USA
Interests: soil; ecology; spatial analysis; applied predictive modeling; remote sensing; data mining; machine learning; deep learning; time–series analysis; pedology; digital soil mapping

Special Issue Information

Dear Colleagues,

The spatial and temporal variability of soil properties (e.g., particle size distribution, pH, salinity, moisture dynamics, and nutrient availability) are important to understand environmental function and ecosystem services. With the increased availability of remote sensing products and computing resources, detailed characterization of soil properties across time and space is becoming more feasible and important for many applications ranging from earth system models to local land management decision-making toolsets. In many areas of the world, soil mapping products are not available or are not accurate enough to reasonably constrain uncertainty for end-users.

The lack of quality soil property data often proves to be a limiting factor for process-based modeling, particularly in the context of climate change adaptation and changing land uses. In addition to more traditional soil properties (e.g., texture), there is an increasing need for remote sensing applications monitoring more dynamic soil properties, including soil health indicators (e.g., aggregate stability), or soil cover parameters (e.g., biological soil crust cover).

We invite you to contribute a paper to this Special Issue to highlight new methods or sensors to improve characterization of soil properties with remote sensing. This may include new unmanned aerial vehicle (UAV) approaches, aerial photography, lidar, gamma radiometrics, hyperspectral sensors, satellite imagery archives, or novel new sensors to improve mapping and understanding of soil properties in both space and time. We also invite papers utilizing remote sensing in new predictive workflows for digital soil mapping with a particular focus on improving accuracy for the production of ‘user-ready’ products.

Dr. Travis W. Nauman
Dr. Jonathan J. Maynard
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
  • Soil health
  • Soil moisture
  • Remote sensing
  • Machine learning
  • Digital soil mapping
  • Prediction accuracy

Published Papers (5 papers)

Order results
Result details
Select all
Export citation of selected articles as:

Research

Jump to: Other

16 pages, 3573 KiB  
Article
A Comparison of Model Averaging Techniques to Predict the Spatial Distribution of Soil Properties
by Ruhollah Taghizadeh-Mehrjardi, Hossein Khademi, Fatemeh Khayamim, Mojtaba Zeraatpisheh, Brandon Heung and Thomas Scholten
Remote Sens. 2022, 14(3), 472; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14030472 - 19 Jan 2022
Cited by 19 | Viewed by 3948
Abstract
This study tested and evaluated a suite of nine individual base learners and seven model averaging techniques for predicting the spatial distribution of soil properties in central Iran. Based on the nested-cross validation approach, the results showed that the artificial neural network and [...] Read more.
This study tested and evaluated a suite of nine individual base learners and seven model averaging techniques for predicting the spatial distribution of soil properties in central Iran. Based on the nested-cross validation approach, the results showed that the artificial neural network and Random Forest base learners were the most effective in predicting soil organic matter and electrical conductivity, respectively. However, all seven model averaging techniques performed better than the base learners. For example, the Granger–Ramanathan averaging approach resulted in the highest prediction accuracy for soil organic matter, while the Bayesian model averaging approach was most effective in predicting sand content. These results indicate that the model averaging approaches could improve the predictive accuracy for soil properties. The resulting maps, produced at a 30 m spatial resolution, can be used as valuable baseline information for managing environmental resources more effectively. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Properties)
Show Figures

Graphical abstract

26 pages, 4887 KiB  
Article
Some Peculiarities of Arable Soil Organic Matter Detection Using Optical Remote Sensing Data
by Elena Prudnikova and Igor Savin
Remote Sens. 2021, 13(12), 2313; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13122313 - 12 Jun 2021
Cited by 13 | Viewed by 3101
Abstract
Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection [...] Read more.
Optical remote sensing only provides information about the very thin surface layer of soil. Rainfall splash alters soil surface properties and its spectral reflectance. We analyzed the impact of rainfall on the success of soil organic matter (SOM) content (% by mass) detection and mapping based on optical remote sensing data. The subject of the study was the arable soils of a test field located in the Tula region (Russia), their spectral reflectance, and Sentinel-2 data. Our research demonstrated that rainfall negatively affects the accuracy of SOM predictions based on Sentinel-2 data. Depending on the average precipitation per day, the R2cv of models varied from 0.67 to 0.72, RMSEcv from 0.64 to 1.1% and RPIQ from 1.4 to 2.3. The incorporation of information on the soil surface state in the model resulted in an increase in accuracy of SOM content detection based on Sentinel-2 data: the R2cv of the models increased up to 0.78 to 0.84, the RMSEcv decreased to 0.61 to 0.71%, and the RPIQ increased to 2.1 to 2.4. Further studies are necessary to identify how the SOM content and composition of the soil surface change under the influence of rainfall for other soils, and to determine the relationships between rainfall-induced SOM changes and soil surface spectral reflectance. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Properties)
Show Figures

Figure 1

19 pages, 5697 KiB  
Article
Mapping Particle Size and Soil Organic Matter in Tropical Soil Based on Hyperspectral Imaging and Non-Imaging Sensors
by Marcos Rafael Nanni, José Alexandre Melo Demattê, Marlon Rodrigues, Glaucio Leboso Alemparte Abrantes dos Santos, Amanda Silveira Reis, Karym Mayara de Oliveira, Everson Cezar, Renato Herrig Furlanetto, Luís Guilherme Teixeira Crusiol and Liang Sun
Remote Sens. 2021, 13(9), 1782; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13091782 - 03 May 2021
Cited by 16 | Viewed by 3276
Abstract
We evaluated the use of airborne hyperspectral imaging and non-imaging sensors in the Vis—NIR—SWIR spectral region to assess particle size and soil organic matter in the surface layer of tropical soils (Oxisols, Ultisols, Entisols). The study area is near Piracicaba municipality, São Paulo [...] Read more.
We evaluated the use of airborne hyperspectral imaging and non-imaging sensors in the Vis—NIR—SWIR spectral region to assess particle size and soil organic matter in the surface layer of tropical soils (Oxisols, Ultisols, Entisols). The study area is near Piracicaba municipality, São Paulo state, Brazil, in a sugarcane cultivation area of 135 hectares. The study area, with bare soil, was imaged in April 2016 by the AisaFENIX aerotransported hyperspectral sensor, with spectral resolution of 3.5 nm between 380 and 970 nm, and 12 nm between 970 and 2500 nm. We collected 66 surface soil samples. The samples were analyzed for particle size and soil organic matter content. Laboratory spectral measurements were performed using a non-imaging spectroradiometer (ASD FieldSpec 3 Jr). Partial Least Square Regression (PLSR) was used to predict clay, silt, sand and soil organic matter (SOM). The PLSR functions developed were applied to the hyperspectral image of the study area, allowing development of a prediction map of clay, sand, and SOM. The developed PLSR models demonstrated the relationship between the predictor variables at the cross-validation step, both for the non-imaging and imaging sensors, when the highest r and R2 values were obtained for clay, sand, and SOM, with R2 over 0.67. We did not obtain a satisfactory model for silt content. For the non-imaging sensor at the prediction step, R2 values for clay and SOM were over 0.7 and sand was lower than 0.54. The imaging sensor yielded models for clay, sand, and SOM with R2 values of 0.62, 0.66, and 0.67, respectively. Pearson correlation between sensors was greater than 0.849 for the prediction of clay, sand, and SOM. Our study successfully generated, from the imaging sensor, a large-scale and detailed predicted soil maps for particle size and SOM, which are important in the management of tropical soils. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Properties)
Show Figures

Graphical abstract

21 pages, 7824 KiB  
Article
Predicting Soil Organic Carbon and Soil Nitrogen Stocks in Topsoil of Forest Ecosystems in Northeastern China Using Remote Sensing Data
by Shuai Wang, Qianlai Zhuang, Xinxin Jin, Zijiao Yang and Hongbin Liu
Remote Sens. 2020, 12(7), 1115; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12071115 - 31 Mar 2020
Cited by 26 | Viewed by 4221
Abstract
Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. [...] Read more.
Forest ecosystems play an important role in regional carbon and nitrogen cycling. Accurate and effective monitoring of their soil organic carbon (SOC) and soil total nitrogen (STN) stocks provides important information for soil quality assessment, sustainable forestry management and climate change policy making. In this study, a geographical weighted regression (GWR) model, a multiple stepwise regression (MLSR) model, and a boosted regression trees (BRT) model were compared to obtain the best prediction of SOC and STN stocks of the forest ecosystems in northeastern China. Five-hundred and thirteen topsoil (0–30 cm) samples (10.32 kg m−2 (±0.53) for SOC, 1.21 kg m−2 (±0.32) for STN), and 9 remotely-sensed environmental variables were collected and used for the model development and verification. By comparing with independent verification data, the best model (BRT) achieved R2 = 0.56 and root mean square error (RMSE) = 00.85 kg m−2 for SOC stocks, R2 = 0.51 and RMSE = 0.22 kg m−2 for STN stocks. Of all the remotely-sensed environment variables, soil adjusted vegetation index (SAVI) and normalized difference vegetation index (NDVI) are of the highest relative importance in predicting SOC and STN stocks. The spatial distribution of the predicted SOC and STN stocks gradually decreased from northeast to southwest. This study provides an attempt to rapidly predict SOC and STN stocks in the dense vegetation covered area. The results can help evaluate soil quality and facilitate land policy and regulation making by the government in the region. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Properties)
Show Figures

Graphical abstract

Other

Jump to: Research

15 pages, 4275 KiB  
Technical Note
Using Sentinel-2 for Simplifying Soil Sampling and Mapping: Two Case Studies in Umbria, Italy
by Francesco Saverio Santaga, Alberto Agnelli, Angelo Leccese and Marco Vizzari
Remote Sens. 2021, 13(17), 3379; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13173379 - 26 Aug 2021
Cited by 9 | Viewed by 3138
Abstract
Soil-sample collection and strategy are costly and time-consuming endeavors, mainly when the goal is in-field variation mapping that usually requires dense sampling. This study developed and tested a streamlined soil mapping methodology, applicable at the field scale, based on an unsupervised classification of [...] Read more.
Soil-sample collection and strategy are costly and time-consuming endeavors, mainly when the goal is in-field variation mapping that usually requires dense sampling. This study developed and tested a streamlined soil mapping methodology, applicable at the field scale, based on an unsupervised classification of Sentinel-2 (S2) data supporting the definition of reduced soil-sampling schemes. The study occurred in two agricultural fields of 20 hectares each near Deruta, Umbria, Italy. S2 images were acquired for the two bare fields. After a band selection based on bibliography, PCA (Principal Component Analysis) and cluster analysis were used to identify points of two reduced-sample schemes. The data obtained by these samplings were used in linear regressions with principal components of the selected S2 bands to produce maps for clay and organic matter (OM). Resultant maps were assessed by analyzing residuals with a conventional soil sampling of 30 soil samples for each field to quantify their accuracy level. Although of limited extent and with a specific focus, the low average errors (Clay ± 2.71%, OM ± 0.16%) we obtained using only three soil samples suggest a wider potential for this methodology. The proposed approach, integrating S2 data and traditional soil-sampling methods could considerably reduce soil-sampling time and costs in ordinary and precision agriculture applications. Full article
(This article belongs to the Special Issue Remote Sensing of Soil Properties)
Show Figures

Graphical abstract

Back to TopTop