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Selected Papers from the First "Symposia of Remote Sensing Applied to Soil Science", as part of the "21st World Congress of Soil Science, 2018"

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A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: closed (31 December 2019) | Viewed by 52512

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Prof. Dr. Eyal Ben-Dor

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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
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Prof. Dr. José A. M. Demattê

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Soil Science Department, Luiz de Queiroz College of Agriculture, University of Piracicaba, São Paulo 13418-900, SP, Brazil
Interests: remote and proximal sensing applied to soils from all platforms; soil attribute and pedological mapping; digital soil mapping; precision agriculture
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Prof. Dr. Marcos Rafael Nanni

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Full Professor and Researcher at the Department of Agronomy, University State of Maringá – UEM, Maringá, PR, Brazil
Interests: data fusion and processing; machine learning; multispectral and hyperspectral sensors; remote sensing; precision agriculture; UAV
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Dr. André Carnieletto Dotto

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ESALQ-University of São Paulo, Ave. Pádua Dias, 11 Department of Soil Science, CEP 13418-900, Piracicaba-SP, Brazil
Interests: soil spectroscopy; proximal soil sensing; VIS-NIR spectroscopy; hyperspectral data applied to soil

Special Issue Information

Dear Colleagues:

We are glad to invite you to participate of the FIRST Symposia of Remote Sensing (RS) as part of the World Congress of Soil Science, to be held in Rio de Janeiro, 12-17 august, Brazil 2018, composed by key speeches, selected oral and poster presentations. The importance of RS as a ´partner´ on soil science is not new, and dates from more than 100 years (i.e., aerial photographs). Since then, hundreds of equipment have been developed, and from a simple tool, RS became an innovative scientific discipline. The symposia will have the presence of important references on the area such as Prof. Eyal Ben-Dor who was a precursor of the Near Infrared Reflectance Analysis of soils and was the first scientist and a pioneer to apply the Hyper Spectral Remote Sensing technology to soils. Prof. José Alexandre Demattê, also in coordination, was the first full professor in RS and soil spectroscopy in Brazil. In addition to the above two scholars, we have the honor to present Prof. Marcos Rafael Nanni as the third candidate for organizing this session. Prof Nanni is an expert in proximal and remote sensing of soil, and has worked in RS with drones and the AISA-Fenix airborne hyperspectral sensors. Today, he is the head of one of the most powerful and equipped group on proximal and remote sensing of soils in Brazil. Despite the organizers short presentation, we will have two important keynote speeches, Prof G. Zalidis: "Promoting remote sensing applications for optimizing soil and water management supporting climate smart agriculture in the Balkan region" and Prof. J. Cierniewski: “Remote sensing as a tool to study soils and their impact on the Earth’s climate. This is a great opportunity to improve Remote Sensing Status in the Soil Science Discipline and be part of history.

Despite the publication of abstracts in the Congress, selected papers will be invited to prepare a complete article for this Special Issue in the Remote Sensing.

DEADLINE FOR SUBMISSION IN THE CONGRESS: 20 January 2018

Directions for submission: To make an adequate submission to the session, access the 21st World Congress of Soil Science at www.21wcss.org, Go to Registration process. In the Participant Area go to Send Abstract, Select Division 1: Soil in Space and Time. Click on Symposia, Select C1.2 - Soil geography, Select C1.2.2 - Remote sensing applied to soil science. Fill in your application.

Interest in submission/objectives and poster/oral presentations

In commemoration to the event, we also encourage all researchers and users to make their inscription and afterwards be part of this special issue.

Papers related to remote sensing and soils of any specific area (erosion, physics, fertility, chemistry, microbiology, mineralogy, soil classification, mapping, pollution, soil management) will be welcome. In addition, other topics, such as (a) updating the advance remote sensing technology for Soil Science applications; (b) soil remote sensing data analyses by chemometric methods; (c) integration of the multi and hyperspectral sensors data for soil science; (e) use of the remote sensing data in digital soil mapping, (f) precision agriculture, (g) soil attributes prediction, land use, soil monitoring and soil environment impact; (h) available platforms and data bases for soil remote sensing studies; (i) integration of remote sensing with soil science, (j) data-mining soils in remote images; (k) integration of proximal with remote sensing soil data, (l) reviewing the new technology and missions for the future soil science; (m) proximal sensing papers well be welcome sense related with remote sensors; (n) all platforms, satellite, airborne, Unmanned Aerial Vehicle (UAV); (o) sensors from any part of the spectrum (i.e., gamma, x-ray, ultraviolet, optical, middle infrared, radar, others) are welcome.

Prof. Dr. Eyal Ben-Dor
Prof. Dr. José Demattê
Prof. Dr. Marcos Rafael Nanni
Dr. André Carnieletto Dotto
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.

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Published Papers (5 papers)

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Research

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20 pages, 6761 KiB

Open AccessArticle

Monitoring Soil Surface Mineralogy at Different Moisture Conditions Using Visible Near-Infrared Spectroscopy Data

by Irena Ymeti, Dhruba Pikha Shrestha and Freek van der Meer

Remote Sens. 2019, 11(21), 2526; https://doi.org/10.3390/rs11212526 - 29 Oct 2019

Cited by 2 | Viewed by 3495

Abstract

The soil minerals determine essential soil properties such as the cation exchange capacity, texture, structure, and their capacity to form bonds with organic matter. Any alteration of these organo-mineral interactions due to the soil moisture variations needs attention. Visible near-infrared imaging spectroscopy is capable of assessing spectral soil constituents that are responsible for the organo-mineral interactions. In this study, we hypothesized that the alterations of the surface soil mineralogy occur due to the moisture variations. For eight weeks, under laboratory conditions, imaging spectroscopy data were collected on a 72 h basis for three Silty Loam soils varying in the organic matter (no, low and high) placed at the drying-field capacity, field capacity and waterlogging-field capacity treatments. Using the Spectral Information Divergence image classifier, the image area occupied by the Mg-clinochlore, goethite, quartz coated 50% by goethite, hematite dimorphous with maghemite was detected and quantified (percentage). Our results showed these minerals behaved differently, depending on the soil type and soil treatment. While for the soils with organic matter, the mineralogical alterations were evident at the field capacity state, for the one with no organic matter, these changes were insignificant. Using imaging spectroscopy data on the Silty Loam soil, we showed that the surface mineralogy changes over time due to the moisture conditions. Full article

(This article belongs to the Special Issue Selected Papers from the First "Symposia of Remote Sensing Applied to Soil Science", as part of the "21st World Congress of Soil Science, 2018")

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18 pages, 2682 KiB

Open AccessArticle

Integrating SEBAL with in-Field Crop Water Status Measurement for Precision Irrigation Applications—A Case Study

by Stefano Gobbo, Stefano Lo Presti, Marco Martello, Lorenza Panunzi, Antonio Berti and Francesco Morari

Remote Sens. 2019, 11(17), 2069; https://doi.org/10.3390/rs11172069 - 3 Sep 2019

Cited by 24 | Viewed by 5195

Abstract

The surface energy balance algorithm for land (SEBAL) has been demonstrated to provide accurate estimates of crop evapotranspiration (ET) and yield at different spatial scales even under highly heterogeneous conditions. However, validation of the SEBAL using in-field direct and indirect measurements of plant water status is a necessary step before deploying the algorithm as an irrigation scheduling tool. To this end, a study was conducted in a maize field located near the Venice Lagoon area in Italy. The experimental area was irrigated using a 274 m long variable rate irrigation (VRI) system with 25-m sections. Three irrigation management zones (IMZs; high, medium and low irrigation requirement zones) were defined combining soil texture and normalized difference vegetation index (NDVI) data. Soil moisture sensors were installed in the different IMZs and used to schedule irrigation. In addition, SEBAL-based actual evapotranspiration (ET_r) and biomass estimates were calculated throughout the season. VRI management allowed crop water demand to be matched, saving up to 42 mm (−16%) of water when compared to uniform irrigation rates. The high irrigation amounts applied during the growing season to avoid water stress resulted in no significant differences among the IMZs. SEBAL-based biomass estimates agreed with in-season measurements at 72, 105 and 112 days after planting (DAP; r² = 0.87). Seasonal ET matched the spatial variability observed in the measured yield map at harvest. Moreover, the SEBAL-derived yield map largely agreed with the measured yield map with relative errors of 0.3% among the IMZs and of 1% (0.21 t ha⁻¹) for the whole field. While the FAO method-based stress coefficient (K_s) never dropped below the optimum condition (K_s = 1) for all the IMZs and the uniform zone, SEBAL K_s was sensitive to changes in water status and remained below 1 during most of the growing season. Using SEBAL to capture the daily spatial variation in crop water needs and growth would enable the definition of transient, dynamic IMZs. This allows farmers to apply proper irrigation amounts increasing water use efficiency. Full article

(This article belongs to the Special Issue Selected Papers from the First "Symposia of Remote Sensing Applied to Soil Science", as part of the "21st World Congress of Soil Science, 2018")

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21 pages, 4246 KiB

Open AccessArticle

Multi-Temporal Satellite Images on Topsoil Attribute Quantification and the Relationship with Soil Classes and Geology

by Bruna C. Gallo, José A. M. Demattê, Rodnei Rizzo, José L. Safanelli, Wanderson De S. Mendes, Igo F. Lepsch, Marcus V. Sato, Danilo J. Romero and Marilusa P. C. Lacerda

Remote Sens. 2018, 10(10), 1571; https://doi.org/10.3390/rs10101571 - 1 Oct 2018

Cited by 67 | Viewed by 7425

Abstract

The mapping of soil attributes provides support to agricultural planning and land use monitoring, which consequently aids the improvement of soil quality and food production. Landsat 5 Thematic Mapper (TM) images are often used to estimate a given soil attribute (i.e., clay), but have the potential to model many other attributes, providing input for soil mapping applications. In this paper, we aim to evaluate a Bare Soil Composite Image (BSCI) from the state of São Paulo, Brazil, calculated from a multi-temporal dataset, and study its relationship with topsoil properties, such as soil class and geology. The method presented detects bare soil in satellite images in a time series of 16 years, based on Landsat 5 TM observations. The compilation derived a BSCI for the agricultural sites (242,000 hectare area) characterized by very complex geology. Soil properties were analyzed to calibrate prediction models using 740 soil samples (0–20 cm) collected of the area. Partial least squares regression (PLSR) based on the BSCI spectral dataset was performed to quantify soil attributes. The method identified that a single image represents 7 to 20% of bare soil while the compilation of the multi-temporal dataset increases to 53%. Clay content had the best soil attribute prediction estimates (R² = 0.75, root mean square error (RMSE) = 89.84 g kg⁻¹, and accuracy = 74%). Soil organic matter, cation exchange capacity and sandy soils also achieved moderate predictions. The BSCI demonstrates a strong relationship with legacy geological maps detecting variations in soils. From a single composite image, it was possible to use spectroscopy to evaluate several environmental parameters. This technique could greatly improve soil mapping and consequently aid several applications, such as land use planning, environmental monitoring, and prevention of land degradation, updating legacy surveys and digital soil mapping. Full article

(This article belongs to the Special Issue Selected Papers from the First "Symposia of Remote Sensing Applied to Soil Science", as part of the "21st World Congress of Soil Science, 2018")

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21 pages, 5626 KiB

Open AccessFeature PaperArticle

Improvement of Clay and Sand Quantification Based on a Novel Approach with a Focus on Multispectral Satellite Images

by Caio T. Fongaro, José A. M. Demattê, Rodnei Rizzo, José Lucas Safanelli, Wanderson De Sousa Mendes, André Carnieletto Dotto, Luiz Eduardo Vicente, Marston H. D. Franceschini and Susan L. Ustin

Remote Sens. 2018, 10(10), 1555; https://doi.org/10.3390/rs10101555 - 27 Sep 2018

Cited by 51 | Viewed by 7123

Abstract

Soil mapping demands large-scale surveys that are costly and time consuming. It is necessary to identify strategies with reduced costs to obtain detailed information for soil mapping. We aimed to compare multispectral satellite image and relief parameters for the quantification and mapping of clay and sand contents. The Temporal Synthetic Spectral (TESS) reflectance and Synthetic Soil Image (SYSI) approaches were used to identify and characterize texture spectral signatures at the image level. Soil samples were collected (0–20 cm depth, 919 points) from an area of 14,614 km² in Brazil for reference and model calibration. We compared different prediction approaches: (a) TESS and SYSI; (b) Relief-Derived Covariates (RDC); and (c) SYSI plus RDC. The TESS method produced highly similar behavior to the laboratory convolved data. The sandy textural class showed a greater increase in average spectral reflectance from Band 1 to 7 compared with the clayey class. The prediction using SYSI produced a better result for clay (R² = 0.83; RMSE = 65.0 g kg⁻¹) and sand (R² = 0.86; RMSE = 79.9 g kg⁻¹). Multispectral satellite images were more stable for the identification of soil properties than relief parameters. Full article

(This article belongs to the Special Issue Selected Papers from the First "Symposia of Remote Sensing Applied to Soil Science", as part of the "21st World Congress of Soil Science, 2018")

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18 pages, 1219 KiB

Open AccessReview

Remote Sensing Techniques for Soil Organic Carbon Estimation: A Review

by Theodora Angelopoulou, Nikolaos Tziolas, Athanasios Balafoutis, George Zalidis and Dionysis Bochtis

Remote Sens. 2019, 11(6), 676; https://doi.org/10.3390/rs11060676 - 21 Mar 2019

Cited by 235 | Viewed by 27787

Abstract

Towards the need for sustainable development, remote sensing (RS) techniques in the Visible-Near Infrared–Shortwave Infrared (VNIR–SWIR, 400–2500 nm) region could assist in a more direct, cost-effective and rapid manner to estimate important indicators for soil monitoring purposes. Soil reflectance spectroscopy has been applied in various domains apart from laboratory conditions, e.g., sensors mounted on satellites, aircrafts and Unmanned Aerial Systems. The aim of this review is to illustrate the research made for soil organic carbon estimation, with the use of RS techniques, reporting the methodology and results of each study. It also aims to provide a comprehensive introduction in soil spectroscopy for those who are less conversant with the subject. In total, 28 journal articles were selected and further analysed. It was observed that prediction accuracy reduces from Unmanned Aerial Systems (UASs) to satellite platforms, though advances in machine learning techniques could further assist in the generation of better calibration models. There are some challenges concerning atmospheric, radiometric and geometric corrections, vegetation cover, soil moisture and roughness that still need to be addressed. The advantages and disadvantages of each approach are highlighted and future considerations are also discussed at the end. Full article

(This article belongs to the Special Issue Selected Papers from the First "Symposia of Remote Sensing Applied to Soil Science", as part of the "21st World Congress of Soil Science, 2018")

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Selected Papers from the First "Symposia of Remote Sensing Applied to Soil Science", as part of the "21st World Congress of Soil Science, 2018"

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