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Recent Advances in Remote Sensing of Soil Science

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

Deadline for manuscript submissions: 31 May 2024 | Viewed by 7387

Special Issue Editors


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Guest Editor
Laboratory of Remote Sensing, Spectroscopy, and Geographical Information Systems, School of Agriculture, Faculty of Agriculture, Forestry and Natural Environment, 54636 Thessaloniki, Greece
Interests: soil science; infrared spectroscopy; big data; remote sensing; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
German Aerospace Center (DLR), Remote Sensing Technology Institute, Photogrammetry and Image Analysis, Oberpfaffenhofen, 82234 Weßling, Germany
Interests: imaging spectroscopy with a focus on urban surface materials; spaceborne imaging spectroscopy missions; EnMAP; DESIS; earth observation for soil information; applied spectroscopy
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Southwest Florida Research and Education Center, Department of Soil and Water Sciences, Institute of Food and Agricultural Sciences, University of Florida, 2685 State Rd 29N, Immokalee, FL 34142, USA
Interests: soil science; remote sensing

Special Issue Information

Dear Colleagues,

Soil is one of Earth's most vital resources, playing a crucial role in supporting terrestrial ecosystems and sustaining global food production. It serves as a habitat for numerous organisms, regulates water flow and quality, sequesters carbon, and provides essential nutrients for plant growth. Understanding soil properties, such as moisture content, texture, organic matter content, and nutrient availability, is essential for effective land management and sustainable agriculture. To protect Earth’s soil resources, new laws, such as the European Union’s soil mission, have highlighted the need for comprehensive soil protection strategies. Remote sensing (and the observation of Earth in general), when coupled with artificial intelligence and machine learning, offers a powerful tool to monitor and assess soil health and degradation on a large scale, enabling a detailed characterization of soil properties to take place across time and space.

The aim of this Special Issue is to highlight novel methodologies, workflows, and sensors that can be used to predict soil properties from remote sensing data (i.e., space- or airborne platforms) using digital soil mapping or artificial intelligence techniques. Methodologies may cover the entire scope of soil property mapping, from the generation of bare soil synthetic images from multi-temporal data (if applicable) to novel techniques on the final layer of the estimation process. Review papers comparing different methodologies are also welcome.

We invite researchers to contribute original research articles, reviews, and case studies focusing on the remote sensing of soil properties. Topics of interest include, but are not limited to:

  • Soil property mapping from unmanned aerial vehicles or other airborne data and satellite imagery (such as data from the Copernicus space program);
  • Monitoring of soil degradation and erosion using remote sensing;
  • Development of novel remote-sensing-based soil monitoring frameworks;
  • Assessing the impact of land use and management practices on soil health;
  • Applications of remote sensing in reduced carbon footprint agriculture and soil fertility management.

The sensors employed may range from optical hyperspectral data to lidar, gamma radiometrics, novel new sensors, or a combination/fusion thereof.

Dr. Nikolaos L. Tsakiridis
Dr. Uta Heiden
Dr. Nikolaos Tziolas
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 organic carbon
  • artificial intelligence
  • earth observation
  • soil quality
  • land degradation
  • digital soil mapping
  • machine learning

Published Papers (5 papers)

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Research

20 pages, 3969 KiB  
Article
Adapting Prediction Models to Bare Soil Fractional Cover for Extending Topsoil Clay Content Mapping Based on AVIRIS-NG Hyperspectral Data
by Elizabeth Baby George, Cécile Gomez and Nagesh D. Kumar
Remote Sens. 2024, 16(6), 1066; https://doi.org/10.3390/rs16061066 - 18 Mar 2024
Viewed by 631
Abstract
The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. [...] Read more.
The deployment of remote sensing platforms has facilitated the mapping of soil properties to a great extent. However, the accuracy of these soil property estimates is compromised by the presence of non-soil cover, which introduces interference with the acquired reflectance spectra over pixels. Therefore, current soil property estimation by remote sensing is limited to bare soil pixels, which are identified based on spectral indices of vegetation. Our study proposes a composite mapping approach to extend the soil properties mapping beyond bare soil pixels, associated with an uncertainty map. The proposed approach first classified the pixels based on their bare soil fractional cover by spectral unmixing. Then, a specific regression model was built and applied to each bare soil fractional cover class to estimate clay content. Finally, the clay content maps created for each bare soil fractional cover class were mosaicked to create a composite map of clay content estimations. A bootstrap procedure was used to estimate the standard deviation of clay content predictions per bare soil fractional cover dataset, which represented the uncertainty of estimations. This study used a hyperspectral image acquired by the Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG) sensor over cultivated fields in South India. The proposed approach provided modest performances in prediction (Rval2 ranging from 0.53 to 0.63) depending on the bare soil fractional cover class and showed a correct spatial pattern, regardless of the bare soil fraction classes. The model’s performance was observed to increase with the adoption of higher bare soil fractional cover thresholds. The mapped area ranged from 10.4% for pixels with bare soil fractional cover >0.7 to 52.7% for pixels with bare soil fractional cover >0.3. The approach thus extended the mapped surface by 42.4%, while maintaining acceptable prediction performances. Finally, the proposed approach could be adopted to extend the mapping capability of planned and current hyperspectral satellite missions. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Science)
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23 pages, 15263 KiB  
Article
Identifying the Spatial Heterogeneity and Driving Factors of Satellite-Based and Hydrologically Modeled Profile Soil Moisture
by Han Yang, Xiaoqi Zhang, Zhe Yuan, Bin Xu and Junjun Huo
Remote Sens. 2024, 16(3), 448; https://doi.org/10.3390/rs16030448 - 24 Jan 2024
Cited by 1 | Viewed by 719
Abstract
Profile soil moisture (PSM), the soil water content in the whole soil layer, directly controls the major processes related to biological interaction, vegetation growth, and runoff generation. Its spatial heterogeneity, which refers to the uneven distribution and complexity in space, influences refined spatial [...] Read more.
Profile soil moisture (PSM), the soil water content in the whole soil layer, directly controls the major processes related to biological interaction, vegetation growth, and runoff generation. Its spatial heterogeneity, which refers to the uneven distribution and complexity in space, influences refined spatial management and decision-making in ecological, agricultural, and hydrological systems. Satellite instruments and hydrological models are two important sources of spatial information on PSM, but there is still a gap in understanding their potential mechanisms that affect spatial heterogeneity. This study is designed to identify the spatial heterogeneity and the driving factors of two PSM datasets; one is preprocessed from a satellite product (European Space Agency Climate Change Initiative, ESA CCI), and the other is simulated from a distributed hydrological model (the DEM-based distributed rainfall-runoff model, DDRM). Three catchments with different climate conditions were chosen as the study area. By considering the scale dependence of spatial heterogeneity, the profile saturation degree (PSD) datasets from different sources (shown as ESA CCI PSD and DDRM PSD, respectively) during 2017 that are matched in terms of spatial scale and physical properties were acquired first based on the calibration data from 2014–2016, and then the spatial heterogeneity of the PSD from different sources was identified by using spatial statistical analysis and the semi-variogram method, followed by the geographic detector method, to investigate the driving factors. The results indicate that (1) ESA CCI and DDRM PSD are similar for seasonal changes and are overall consistent and locally different in terms of the spatial variations in catchment with different climate conditions; (2) based on spatial statistical analysis, the spatial heterogeneity of PSD reduces after spatial rescaling; at the same spatial scale, DDRM PSD shows higher spatial heterogeneity than ESA CCI PSD, and the low-flow period shows higher spatial heterogeneity than the high-flow period; (3) based on the semi-variogram method, both ESA CCI and DDRM PSD show strong spatial heterogeneity in most cases, in which the proportion of C/(C0 + C) is higher than 0.75, and the spatial data in the low-flow period mostly show larger spatial heterogeneity, in which the proportion is higher than 0.9; the spatial heterogeneity of PSD is higher in the semi-arid catchment; (4) the first three driving factors of the spatial heterogeneity of both ESA CCI and DDRM PSD are DEM, precipitation, and soil type in most cases, contributing more than 50% to spatial heterogeneity; (5) precipitation contributes most to ESA CCI PSD in the low-flow period, and there is no obvious high contribution of precipitation to DDRM PSD. The research provides insights into the spatial heterogeneity of PSM, which helps develop refined modeling and spatial management strategies for soil moisture in ecological, agricultural, and hydrological fields. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Science)
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22 pages, 23546 KiB  
Article
Uncertainty Quantification of Soil Organic Carbon Estimation from Remote Sensing Data with Conformal Prediction
by Nafiseh Kakhani, Setareh Alamdar, Ndiye Michael Kebonye, Meisam Amani and Thomas Scholten
Remote Sens. 2024, 16(3), 438; https://doi.org/10.3390/rs16030438 - 23 Jan 2024
Viewed by 1683
Abstract
Soil organic carbon (SOC) contents and stocks provide valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. Given this, remote sensing data coupled with advanced machine learning (ML) techniques have eased SOC level estimation while revealing its patterns [...] Read more.
Soil organic carbon (SOC) contents and stocks provide valuable insights into soil health, nutrient cycling, greenhouse gas emissions, and overall ecosystem productivity. Given this, remote sensing data coupled with advanced machine learning (ML) techniques have eased SOC level estimation while revealing its patterns across different ecosystems. However, despite these advances, the intricacies of training reliable and yet certain SOC models for specific end-users remain a great challenge. To address this, we need robust SOC uncertainty quantification techniques. Here, we introduce a methodology that leverages conformal prediction to address the uncertainty in estimating SOC contents while using remote sensing data. Conformal prediction generates statistically reliable uncertainty intervals for predictions made by ML models. Our analysis, performed on the LUCAS dataset in Europe and incorporating a suite of relevant environmental covariates, underscores the efficacy of integrating conformal prediction with another ML model, specifically random forest. In addition, we conducted a comparative assessment of our results against prevalent uncertainty quantification methods for SOC prediction, employing different evaluation metrics to assess both model uncertainty and accuracy. Our methodology showcases the utility of the generated prediction sets as informative indicators of uncertainty. These sets accurately identify samples that pose prediction challenges, providing valuable insights for end-users seeking reliable predictions in the complexities of SOC estimation. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Science)
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17 pages, 5799 KiB  
Communication
Preliminary Results in Innovative Solutions for Soil Carbon Estimation: Integrating Remote Sensing, Machine Learning, and Proximal Sensing Spectroscopy
by Tong Li, Anquan Xia, Timothy I. McLaren, Rajiv Pandey, Zhihong Xu, Hongdou Liu, Sean Manning, Oli Madgett, Sam Duncan, Peter Rasmussen, Florian Ruhnke, Onur Yüzügüllü, Noura Fajraoui, Deeksha Beniwal, Scott Chapman, Georgios Tsiminis, Chaya Smith, Ram C. Dalal and Yash P. Dang
Remote Sens. 2023, 15(23), 5571; https://doi.org/10.3390/rs15235571 - 30 Nov 2023
Cited by 1 | Viewed by 1720
Abstract
This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining [...] Read more.
This paper explores the application and advantages of remote sensing, machine learning, and mid-infrared spectroscopy (MIR) as a popular proximal sensing spectroscopy tool in the estimation of soil organic carbon (SOC). It underscores the practical implications and benefits of the integrated approach combining machine learning, remote sensing, and proximal sensing for SOC estimation and prediction across a range of applications, including comprehensive soil health mapping and carbon credit assessment. These advanced technologies offer a promising pathway, reducing costs and resource utilization while improving the precision of SOC estimation. We conducted a comparative analysis between MIR-predicted SOC values and laboratory-measured SOC values using 36 soil samples. The results demonstrate a strong fit (R² = 0.83), underscoring the potential of this integrated approach. While acknowledging that our analysis is based on a limited sample size, these initial findings offer promise and serve as a foundation for future research. We will be providing updates when we obtain more data. Furthermore, this paper explores the potential for commercialising these technologies in Australia, with the aim of helping farmers harness the advantages of carbon markets. Based on our study’s findings, coupled with insights from the existing literature, we suggest that adopting this integrated SOC measurement approach could significantly benefit local economies, enhance farmers’ ability to monitor changes in soil health, and promote sustainable agricultural practices. These outcomes align with global climate change mitigation efforts. Furthermore, our study’s approach, supported by other research, offers a potential template for regions worldwide seeking similar solutions. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Science)
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25 pages, 37397 KiB  
Article
Soil Data Cube and Artificial Intelligence Techniques for Generating National-Scale Topsoil Thematic Maps: A Case Study in Lithuanian Croplands
by Nikiforos Samarinas, Nikolaos L. Tsakiridis, Stylianos Kokkas, Eleni Kalopesa and George C. Zalidis
Remote Sens. 2023, 15(22), 5304; https://doi.org/10.3390/rs15225304 - 9 Nov 2023
Cited by 1 | Viewed by 1426
Abstract
There is a growing realization among policymakers that in order to pave the way for the development of evidence-based conservation recommendations for policy, it is essential to improve the capacity for soil-health monitoring by adopting multidimensional and integrated approaches. However, the existing ready-to-use [...] Read more.
There is a growing realization among policymakers that in order to pave the way for the development of evidence-based conservation recommendations for policy, it is essential to improve the capacity for soil-health monitoring by adopting multidimensional and integrated approaches. However, the existing ready-to-use maps are characterized mainly by a coarse spatial resolution (>200 m) and information that is not up to date, making their use insufficient for the EU’s policy requirements, such as the common agricultural policy. This work, by utilizing the Soil Data Cube, which is a self-hosted custom tool, provides yearly estimations of soil thematic maps (e.g., exposed soil, soil organic carbon, clay content) covering all the agricultural area in Lithuania. The pipeline exploits various Earth observation data such as a time series of Sentinel-2 satellite imagery (2018–2022), the LUCAS (Land Use/Cover Area Frame Statistical Survey) topsoil database, the European Integrated Administration and Control System (IACS) and artificial intelligence (AI) architectures to improve the prediction accuracy as well as the spatial resolution (10 m), enabling discrimination at the parcel level. Five different prediction models were tested with the convolutional neural network (CNN) model to achieve the best accuracy for both targeted indicators (SOC and clay) related to the R2 metric (0.51 for SOC and 0.57 for clay). The model predictions supported by the prediction uncertainties based on the PIR formula (average PIR 0.48 for SOC and 0.61 for clay) provide valuable information on the model’s interpretation and stability. The model application and the final predictions of the soil indicators were carried out based on national bare-soil-reflectance composite layers, generated by employing a pixel-based composite approach to the overlaid annual bare-soil maps and by using a combination of a series of vegetation indices such as NDVI, NBR2, and SCL. The findings of this work provide new insights for the generation of soil thematic maps on a large scale, leading to more efficient and sustainable soil management, supporting policymakers and the agri-food private sector. Full article
(This article belongs to the Special Issue Recent Advances in Remote Sensing of Soil Science)
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