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Remote Sensing
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Remote Sensing for Soil Environments
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Remote Sensing for Soil Environments

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

Deadline for manuscript submissions: closed (31 August 2024) | Viewed by 3597

Special Issue Editor

Prof. Dr. Eyal Ben-Dor

E-Mail Website
Guest Editor
Remote Sensing Laboratory, Geography Department, Porter School of the Environment and Earth Sciences, Faculty of Exact Sciences, Tel Aviv University, Tel Aviv 699780, Israel
Interests: imaging spectroscopy of soil; spectral libraries; soil proxy; hyperspectral RS of soils rocks and minerals; sensor calibration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims at advancing soil monitoring through remote sensing while exploring new dimensions and environmental impacts. Soil, comprising more than 25% of global biodiversity, plays a critical role in sustaining life and supporting ecosystems. It serves as the foundation of food chains, nourishing both humans and aboveground biodiversity. With the global population projected to reach nearly 10 billion by 2050, the demand for food and clean drinking water will increase substantially. To address these challenges, remote sensing techniques offer a powerful tool for studying soils at local and regional scales, providing valuable insights into various soil properties. The advent of new sensors with diverse resolutions, coupled with detailed ground measurements and innovative AI approaches, has opened up new avenues for soil monitoring. The applicability of remote sensing to that end encompasses a range of methods for detecting and characterizing soils. It leverages different resolutions, such as spectral, spatial, and temporal, to capture essential information about the soil body. By employing various sensors, remote sensing can contribute to the monitoring of carbon sequestration, soil health, soil moisture, compaction, erosion, and dust production. These examples represent just a fraction of the potential applications of remote sensing in studying the soil environment.

This Special Issue aims to gather original research on the remote sensing of soils, employing all available means and platforms, from ground-based to satellite-based observations. We invite contributions that explore the behavior of soil as a source of life, its interactions with the environment, and its role as an ecological pillar. This Special Issue will specifically emphasize the use of passive and active remote sensing sensors, encompassing the optical, thermal, and micro regions. By highlighting the impact of these sensors on the environment, both individually and collectively, we seek to advance our understanding of soil monitoring and its broader implications.

Prof. Dr. Eyal Ben-Dor
Guest Editor

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 spectroscopy
  • interaction of soil with the environment
  • active and passive sensors
  • high-resolution sensors
  • proximal remote sensing

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Published Papers (1 paper)

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Research

21 pages, 20183 KiB  
Article
Predicting Particle Size and Soil Organic Carbon of Soil Profiles Using VIS-NIR-SWIR Hyperspectral Imaging and Machine Learning Models
by Karym Mayara de Oliveira, João Vitor Ferreira Gonçalves, Renato Herrig Furlanetto, Caio Almeida de Oliveira, Weslei Augusto Mendonça, Daiane de Fatima da Silva Haubert, Luís Guilherme Teixeira Crusiol, Renan Falcioni, Roney Berti de Oliveira, Amanda Silveira Reis, Arney Eduardo do Amaral Ecker and Marcos Rafael Nanni
Remote Sens. 2024, 16(16), 2869; https://doi.org/10.3390/rs16162869 - 6 Aug 2024
Cited by 5 | Viewed by 2746
Abstract
Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) [...] Read more.
Modeling spectral reflectance data using machine learning algorithms presents a promising approach for estimating soil attributes. Nevertheless, a comprehensive investigation of the most effective models, parameters, wavelengths, and data acquisition techniques is essential to ensure optimal predictive accuracy. This work aimed to (a) explore the potential of the soil spectral signature obtained in different spectral bands (VIS-NIR, SWIR, and VIS-NIR-SWIR) and, by using hyperspectral imaging and non-imaging sensors, in the predictive modeling of soil attributes; and (b) analyze the accuracy of different ML models in predicting particle size and soil organic carbon (SOC) applied to the spectral signature of different spectral bands. Six soil monoliths, located in the central north region of Parana, Brazil, were collected and scanned via hyperspectral cameras (VIS-NIR camera and SWIR camera) and spectroradiometer (VIS-NIR-SWIR) in the laboratory. The spectral signature of the soils was analyzed and subsequently applied to ML models to predict particle size and SOC. Each set of data obtained by the different sensors was evaluated separately. The algorithms used were k-nearest neighbors (KNN), support vector machine (SVM), random forest (RF), linear regression (LR), artificial neural network (NN), and partial least square regression (PLSR). The most promising predictive performance was observed for the complete VIS-NIR-SWIR spectrum, followed by SWIR and VIS-NIR. Meanwhile, KNN, RF, and NN models were the most promising algorithms in estimating soil attributes for the dataset obtained from both sensors. The general mean R2 (determination coefficient) values obtained using these models, considering the different spectral bands evaluated, were around 0.99, 0.98, and 0.97 for sand prediction, and around 0.99, 0.98, and 0.96 for clay prediction. The lower performances, obtained for the datasets from both sensors, were observed for silt and SOC, with R2 results between 0.40 and 0.59 for these models. KNN demonstrated the best predictive performance. Integrating effective ML models with robust sample databases, obtained by advanced hyperspectral imaging and spectroradiometers, can enhance the accuracy and efficiency of soil attribute prediction. Full article
(This article belongs to the Special Issue Remote Sensing for Soil Environments)
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