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Remote Sensing
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Soil Properties Using Imaging Spectroscopy
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Soil Properties Using Imaging Spectroscopy

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

Deadline for manuscript submissions: closed (15 December 2022) | Viewed by 9341

Special Issue Editor

Prof. Dr. Eyal Ben-Dor

E-Mail Website
Guest Editor
The Remote Sensing Laboratory, Tel-Aviv University, Tel-Aviv, Israel
Interests: soil spectroscopy; hyperspectral remote sensing; remote sensing of the environment
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Spectral reflectance of soils has been proven to hold quantitative information on many soil properties by many workers. Consequently, a large number of soil spectral libraries (SSLs) have emerged over the years to hold data that serve as a proxy for the wet chemistry information. The proxy methodology has been extended to the field via advances in electro-optics technology that have made portable spectrometers available worldwide. The need to cover large areas in order to generate spectral information for every point has led users to adopt the new evolving hyperspectral remote sensing (HSR) technology, also known as spectral imaging or imaging spectroscopy (IS). While many remote-sensing groups today have one or more portable spectrometers to monitor soils in the laboratory and in the field, mostly for enlarging and validating their SSLs, others who understand the capability of HSR-IS sensors have begun to adopt this technology for soil mapping and monitoring. HSR-IS technology gained popularity when NASA’s first HSR-IS sensor (AIS) proved its remarkable capability to distinguish between several minerals from airborne domains in 1983. In general ,HSR- IS bridges spectral and spatial information and as such, can theoretically adopt spectral-based proxy models to assess soil attributes from a distance. Nonetheless, unlike laboratory or field spectroscopy, HSR-IS faces some challenges, such as atmospheric attenuation, the low signal-to-noise ratio of airborne sensors, sealed surfaces that are not represented by the SSL samples, interspersed vegetation and litter, moisture interference, high-cost operation, and more. Today, many sensors—in sizes ranging from grams to kilograms, and with platforms ranging from UAVs to satellites—are available and easy to use. Thus, users are exploiting HSR-IS information in many disciplines in which soil is a very important aspect. Proximal soil mapping, digital soil mapping, precision agriculture and environmental applications are only a few of the areas in which HSR-IS of soils can provide significant advances. Accordingly, important space agencies such as ESA and NASA, who are planning to mount HSR- IS sensors in orbit  (CHIME, and EMIT, respectively), have selected soils as one the major focus of their missions. The aim of this special issue is to gather all kinds of papers dealing with HSR-IS technology dedicated to assessing soil in a more quantitative way than other sensors and more specifically, to extracting soil attributes(from the spectral domain. This special volume is open to all scientific work dealing with HSR-IS and soils from all spectral domains (VIS, NIR, SWIR, MWIR, LWIR) and from all platforms (laboratory, field, airborne manned and unmanned, and space). Real case studies, data and sensor simulations, standards and protocols for the application of HSR-IS technology to the soil system and upscaling of SSL information to field conditions are a few of the relevant topics, while we strongly encourage work with new, original and innovative approaches.

Prof. 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

  • Performance of ground HSR-IS sensors for quantitative assessment of soil attributes
  • Upscaling of SSLs to the HSR-IS data cube
  • Soil spectral analysis and spatial mapping
  • Problems and solutions in exploiting HSR-IS of soils
  • Ground-truth methodologies for HSR-IS of soils
  • UAV platform and HSR-IS sensors to assess soils
  • Data simulation of HSR-IS images of soils
  • Software and interfaces to use HSR-IS data for soils
  • LWIR and MWIR HSR sensors for soils
  • Standards and protocols for HSR-IS acquisition of soil data
  • Spatial resolution and its effect on soil mapping

Published Papers (2 papers)

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Research

24 pages, 6807 KiB  
Article
Characterizing and Modeling Tropical Sandy Soils through VisNIR-SWIR, MIR Spectroscopy, and X-ray Fluorescence
by Luis Augusto Di Loreto Di Raimo, Eduardo Guimarães Couto, Danilo Cesar de Mello, José Alexandre Mello Demattê, Ricardo Santos Silva Amorim, Gilmar Nunes Torres, Edwaldo Dias Bocuti, Gustavo Vieira Veloso, Raul Roberto Poppiel, Márcio Rocha Francelino and Elpídio Inácio Fernandes-Filho
Remote Sens. 2022, 14(19), 4823; https://0-doi-org.brum.beds.ac.uk/10.3390/rs14194823 - 27 Sep 2022
Cited by 2 | Viewed by 1625
Abstract
Despite occupying a large area of the globe and being the next agricultural frontier, sandy soils are seldom explored in scientific studies. Considering the high capacity of remote sensing in soil characterization, this work aimed to: (i) characterize sandy soils’ profiles from proximal [...] Read more.
Despite occupying a large area of the globe and being the next agricultural frontier, sandy soils are seldom explored in scientific studies. Considering the high capacity of remote sensing in soil characterization, this work aimed to: (i) characterize sandy soils’ profiles from proximal sensing; (ii) assess the ability of visible, near, and short-wave infrared (Vis-NIR-SWIR) as well as mid-infrared (MIR) spectroscopy to distinguish soil classes of highly sandy content; (iii) quantify physical and chemical attributes of sandy soil profiles from Vis-NIR-SWIR and MIR spectroscopy as well as X-ray fluorescence (pXRF). Samples were described and collected from 29 sandy soil profiles. The 127 samples went under Vis-NIR-SWIR and MIR spectroscopy, X-ray fluorescence, and chemical and physical analyses. The spectra were analyzed based on “Morphological Interpretation of Reflectance Spectrum” (MIRS), Principal Components Analysis (PCA), and cluster methodology to characterize soils. The integration of different information obtained by remote sensors, such as Vis-NIR-SWIR, MIR, and Portable X-ray Fluorescence (pXRF), allows for pedologically complex characterizations and conclusions in a short period and with low investment in analysis and reagents. The application of MIRS concepts in the VNS spectra of sandy soils showed high potential for distinguishing pedological classes of sandy soils. The MIR spectra did not show distinct patterns in the general shapes of the curves and reflectance intensities between sandy soil classes. However, even so, this region showed potential for identifying mineralogical constitution, texture, and OM contents, assuming high importance for the complementation of soil pedometric characterizations using VNS spectroscopy. The VNS and MIR data, combined or isolated, showed excellent predictive performance for the estimation of sandy soil attributes (R2 > 0.8). Sandy soil color indices, which are very important for soil classification, can be predicted with excellent accuracy (R2 from 0.74 to 0.99) using VNS spectroscopy or the combination of VNS + MIR. Full article
(This article belongs to the Special Issue Soil Properties Using Imaging Spectroscopy)
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26 pages, 8531 KiB  
Article
Employing a Multi-Input Deep Convolutional Neural Network to Derive Soil Clay Content from a Synergy of Multi-Temporal Optical and Radar Imagery Data
by Nikolaos Tziolas, Nikolaos Tsakiridis, Eyal Ben-Dor, John Theocharis and George Zalidis
Remote Sens. 2020, 12(9), 1389; https://0-doi-org.brum.beds.ac.uk/10.3390/rs12091389 - 28 Apr 2020
Cited by 39 | Viewed by 6659
Abstract
Earth observation (EO) has an immense potential as being an enabling tool for mapping spatial characteristics of the topsoil layer. Recently, deep learning based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the processing of EO data. [...] Read more.
Earth observation (EO) has an immense potential as being an enabling tool for mapping spatial characteristics of the topsoil layer. Recently, deep learning based algorithms and cloud computing infrastructure have become available with a great potential to revolutionize the processing of EO data. This paper aims to present a novel EO-based soil monitoring approach leveraging open-access Copernicus Sentinel data and Google Earth Engine platform. Building on key results from existing data mining approaches to extract bare soil reflectance values the current study delivers valuable insights on the synergistic use of open access optical and radar images. The proposed framework is driven by the need to eliminate the influence of ambient factors and evaluate the efficiency of a convolutional neural network (CNN) to effectively combine the complimentary information contained in the pool of both optical and radar spectral information and those form auxiliary geographical coordinates mainly for soil. We developed and calibrated our multi-input CNN model based on soil samples (calibration = 80% and validation 20%) of the LUCAS database and then applied this approach to predict soil clay content. A promising prediction performance (R2 = 0.60, ratio of performance to the interquartile range (RPIQ) = 2.02, n = 6136) was achieved by the inclusion of both types (synthetic aperture radar (SAR) and laboratory visible near infrared–short wave infrared (VNIR-SWIR) multispectral) of observations using the CNN model, demonstrating an improvement of more than 5.5% in RMSE using the multi-year median optical composite and current state-of-the-art non linear machine learning methods such as random forest (RF; R2 = 0.55, RPIQ = 1.91, n = 6136) and artificial neural network (ANN; R2 = 0.44, RPIQ = 1.71, n = 6136). Moreover, we examined post-hoc techniques to interpret the CNN model and thus acquire an understanding of the relationships between spectral information and the soil target identified by the model. Looking to the future, the proposed approach can be adopted on the forthcoming hyperspectral orbital sensors to expand the current capabilities of the EO component by estimating more soil attributes with higher predictive performance. Full article
(This article belongs to the Special Issue Soil Properties Using Imaging Spectroscopy)
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