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Remote Sensing of Soil Salinity

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

Deadline for manuscript submissions: closed (31 December 2020) | Viewed by 10284

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Prof. Dr. Abderrazak Bannari

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Guest Editor

Retired, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Interests: remote sensing satellite and UAV (multispectral, hyperspectral and radar); geomatic; natural resources; natural hazard; precision agriculture; land degradation; soil salinity; climate change; environmental impact assessment; optical sensor calibration
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Dr. Dimitrios D. Alexakis

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Guest Editor

Institute for Mediterranean Studies, Foundation for Research and Technology Hellas (FORTH), 74100 Rethymno, Crete, Greece
Interests: remote sensing; GIS; geomorphology; landscape ecology; landscape archaeology; soil erosion; land cover/land use change; natural hazards monitoring
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Prof. Dr. Weicheng Wu

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Guest Editor

Key Laboratory of Digital Land and Resources, East China University of Technology, Nanchang 330013, China
Interests: environmental remote sensing; land resource mapping; land degradation; multi-biome biomass; natural hazard risk zoning and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Soil resources are fundamental to life on Earth and are crucial to sustainable development. They have critical relevance to global issues such as food and water security, biodiversity protection, terrestrial ecosystem services, climate regulation, and human health. Unfortunately, soil salinity seriously threatens this security. In this context, there are methods available either to mitigate or to slow down the processes and, sometimes, even reverse them in landscapes vulnerable to the salinization phenomenon. However, remedial actions require reliable information to help to set priorities and to choose the type of action that is most appropriate for a specific location. In salt-affected areas, farmers, soil managers, scientists, and agricultural engineers need accurate and reliable information on the nature, extent, magnitude, severity, and spatial distribution of the salinity in order to take appropriate measures. Obviously, remote sensing (science and technology) can bridge economic, scientific, and practical considerations to extract accurate and relevant information not only for the appropriate remedial actions to be taken, but also for the monitoring of the effectiveness of any ongoing remediation or preventative measures, which facilitate management and decision making.

The aim of this Special Issue is to collect original manuscripts on innovative research using state-of-the-art remote sensing sciences and technologies to assess the impact of soil salinity (or salinization) in different environments (semi-arid, arid, etc.) on agricultural land, land degradation, vegetation resilience in marginal environments, etc. In addition, the Special Issue aims to assess the impact of climate change, sea level rise, microtopography, water-table, irrigation and agricultural management, etc. on soil salinization at local, regional, and/or global scales. Remote sensing offers several innovative technologies (multispectral, hyperspectral, thermal, and radar), approaches (field and laboratory spectroscopic measurements, simulations, satellite, and UAVs), and image processing methods (indices, models, artificial intelligence, data mining, unmixing, etc.) that will be investigated for their potential and contribution on modeling, mapping, and monitoring the soil salinity phenomenon in space and time. Authors are encouraged to submit articles on, but not limited to, the following subjects:

Multisensors onboard satellites or UAVs: multispectral, hyperspectral, radar, and thermal;
Innovations in soil salinity diagnostics technologies;
Image processing methods;
Field and laboratory spectral analysis;
Modeling, mapping, and monitoring;
Global warming, climate change, and SLR intrusion;
Global, regional, and local soil salinity issues;
Land degradation and vegetation resilience in marginal environments;
Agricultural land management;
Environmental impacts of seawater desalination;
GIS, determinist, and stochastic modeling and mapping approaches;
Impact of topographic attributes and groundwater table;
etc.

Prof. Abderrazak Bannari
Dr. Dimitrios D. Alexakis
Prof. Weicheng Wu
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.

Published Papers (2 papers)

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Research

26 pages, 6044 KiB

Open AccessArticle

Validation and Comparison of Physical Models for Soil Salinity Mapping over an Arid Landscape Using Spectral Reflectance Measurements and Landsat-OLI Data

by Z. M. Al-Ali, A. Bannari, H. Rhinane, A. El-Battay, S. A. Shahid and N. Hameid

Remote Sens. 2021, 13(3), 494; https://doi.org/10.3390/rs13030494 - 30 Jan 2021

Cited by 14 | Viewed by 3346

Abstract

The present study focuses on the validation and comparison of eight different physical models for soil salinity mapping in an arid landscape using two independent Landsat-Operational Land Imager (OLI) datasets: simulated and image data. The examined and compared models were previously developed for different semi-arid and arid geographic regions around the world, i.e., Latino-America, the Middle East, North and East Africa and Asia. These models integrate different spectral bands and unlike mathematical functions in their conceptualization. To achieve the objectives of the study, four main steps were completed. For simulated data, a field survey was organized, and 100 soil samples were collected with various degrees of salinity levels. The bidirectional reflectance factor was measured above each soil sample in a goniometric laboratory using an analytical spectral device (ASD) FieldSpec-4 Hi-Res spectroradiometer. These measurements were resampled and convolved in the solar-reflective bands of the Operational Land Imager (OLI) sensor using a radiative transfer code and the relative spectral response profiles characterizing the filters of the OLI sensor. Then, they were converted in terms of the considered models. Moreover, the OLI image acquired simultaneously with the field survey was radiometrically preprocessed, and the models were implemented to derive soil salinity maps. The laboratory analyses were performed to derive electrical conductivity (EC_-Lab) from each soil sample for validation and comparison purposes. These steps were undertaken between predicted salinity (EC_-Predicted) and the measured ground truth (EC_-Lab) in the same way for simulated and image data using regression analysis (p ˂ 0.05), coefficient of determination (R²), and root mean square error (RMSE). Moreover, the derived maps were visually interpreted and validated by comparison with observations from the field visit, ancillary data (soil, geology, geomorphology and water table maps) and soil laboratory analyses. Regardless of data sources (simulated or image) or the validation mode, the results obtained show that the predictive models based on visible- and near-infrared (VNIR) bands and vegetation indices are inadequate for soil salinity prediction in an arid landscape due to serious signals confusion between the salt crust and soil optical properties in these spectral bands. The statistical tests revealed insignificant fits (R² ≤ 0.41) with very high prediction errors (RMSE ≥ 0.65), while the model based on the second-order polynomial function and integrating the shortwave infrared (SWIR) bands provided the results of best fit, with the field observations (EC_-Lab), yielding an R² of 0.97 and a low overall RMSE of 0.13. These findings were corroborated by visual interpretation of derived maps and their validation by comparison with the ground truthing. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Salinity)

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32 pages, 5963 KiB

Open AccessArticle

Assessing Climate Change Impact on Soil Salinity Dynamics between 1987–2017 in Arid Landscape Using Landsat TM, ETM+ and OLI Data

by Abderrazak Bannari and Zahra M. Al-Ali

Remote Sens. 2020, 12(17), 2794; https://doi.org/10.3390/rs12172794 - 28 Aug 2020

Cited by 54 | Viewed by 5746

Abstract

This paper examines the climate change impact on the spatiotemporal soil salinity dynamics during the last 30 years (1987–2017) in the arid landscape. The state of Kuwait, located at the northwest Arabian Peninsula, was selected as a pilot study area. To achieve this, a Landsat- Operational Land Imager (OLI) image acquired thereabouts simultaneously to a field survey was preprocessed and processed to derive a soil salinity map using a previously developed semi-empirical predictive model (SEPM). During the field survey, 100 geo-referenced soil samples were collected representing different soil salinity classes (non-saline, low, moderate, high, very high and extreme salinity). The laboratory analysis of soil samples was accomplished to measure the electrical conductivity (EC_-Lab) to validate the selected and used SEPM. The results are statistically analyzed (p ˂ 0.05) to determine whether the differences are significant between the predicted salinity (EC_-Predicted) and the measured ground truth (EC_-Lab). Subsequently, the Landsat serial time’s datasets acquired over the study area with the Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+) and OLI sensors during the last three decades over the intervals (1987, 1992, 1998, 2000, 2002, 2006, 2009, 2013, 2016 and 2017) were radiometrically calibrated. Likewise, the datasets were atmospherically and spectrally normalized by applying a semi-empirical line approach (SELA) based on the pseudo-invariant targets. Afterwards, a series of soil salinity maps were derived through the application of the SEPM on the images sequence. The trend of salinity changes was statistically tested according to climatic variables (temperatures and precipitations). The results revealed that the EC_-Predicted validation display a best fits in comparison to the EC_-Lab by indicating a good index of agreement (D = 0.84), an excellent correlation coefficient (R² = 0.97) and low overall root mean square error (RMSE) (13%). This also demonstrates the validity of SEPM to be applicable to the other images acquired multi-temporally. For cross-calibration among the Landsat serial time’s datasets, the SELA performed significantly with an RMSE ≤ ± 5% between all homologous spectral reflectances bands of the considered sensors. This accuracy is considered suitable and fits well the calibration standards of TM, ETM+ and OLI sensors for multi-temporal studies. Moreover, remarkable changes of soil salinity were observed in response to changes in climate that have warmed by more than 1.1 °C with a drastic decrease in precipitations during the last 30 years over the study area. Thus, salinized soils have expanded continuously in space and time and significantly correlated to precipitation rates (R² = 0.73 and D = 0.85). Full article

(This article belongs to the Special Issue Remote Sensing of Soil Salinity)

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