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Monitoring Soil Degradation by Remote Sensing

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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 December 2021) | Viewed by 40648

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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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Dr. Asa Gholizadeh

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Faculty of Agrobiology, Food and Natural Resources, Department of Soil Science and Soil Protection, Czech University of Life Sciences Prague, 16500 Prague, Czech Republic
Interests: proximal and remote sensing
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Special Issue Information

Dear Colleagues,

Soil degradation includes a number of processes, ranging from soil erosion to soil contamination, which reduce the capability of soil to be a medium for plants to grow. Soil can be degraded chemically and physically. Chemical processes connect to parameters of the soil that tie to soil chemical components and their reactions, including salinity, fertility decline, and contamination, whereas physical processes describe alterations in particle size, soil structure, compaction status, and soil aggregation. Both chemical and physical processes can bring water loss and soil toxicity as well as other effects including erosion, deposition, and soil swelling that together lead to a reduction in soil productivity and fertility in space and time. Due to the dynamic nature of these effects, early monitoring can allow suppressive interventions before severe and irreversible soil problems arise. Methods to quantify soil degradation on a large scale with a proper domain are needed and must be studied, developed, and adopted. Various proximal and remote sensing disciplines such as laboratory and field sensors, unmanned aerial vehicles, and airborne and spaceborne sensors are essential tools, well-suited for surveying large areas and monitoring soil degradation at a high temporal and spatial interval.

This Special Issue focuses on “Monitoring Soil Degradation using Proximal and Remote Sensing Techniques”. We seek articles that utilize remotely sensed data for degradation monitoring, including but not limited to the following:

Innovative applications and methods in remote sensing of soil degradation, significant case studies;
Novel data analytics for soil degradation modeling applications at different geographic scales;
Multi-sensors and multi-resolution data analysis for degradation monitoring;
Passive (optical and thermal) remote sensing for soil degradation monitoring;
Active (mm and microwaves) remote sensing for soil degradation monitoring;
Potential of the new generation of hyper and superspectral sensors in soil degradation monitoring;
Soil contamination (e.g., natural gas, petroleum hydrocarbons, plastic, and potentially toxic elements) mapping and monitoring.

Prof. Eyal Ben-Dor
Dr. Asa Gholizadeh
Guest Editors

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Keywords

Soil degradation monitoring
Soil modeling and mapping
Soil contamination
Active and passive remote sensing
Optical and thermal remote sensing

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Published Papers (9 papers)

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Research

24 pages, 8675 KiB

Open AccessArticle

Estimation of Heavy Metals in Agricultural Soils Using Vis-NIR Spectroscopy with Fractional-Order Derivative and Generalized Regression Neural Network

by Xitong Xu, Shengbo Chen, Liguo Ren, Cheng Han, Donglin Lv, Yufeng Zhang and Fukai Ai

Remote Sens. 2021, 13(14), 2718; https://doi.org/10.3390/rs13142718 - 10 Jul 2021

Cited by 36 | Viewed by 3822

Abstract

With the development of industrialization and urbanization, heavy metal contamination in agricultural soils tends to accumulate rapidly and harm human health. Visible and near-infrared (Vis-NIR) spectroscopy provides the feasibility of fast monitoring of the variation of heavy metals. This study explored the potential of fractional-order derivative (FOD), the optimal band combination algorithm and different mathematical models in estimating soil heavy metals with Vis-NIR spectroscopy. A total of 80 soil samples were collected from an agriculture area in Suzi river basin, Liaoning Province, China. The spectra for mercury (Hg), chromium (Cr), and copper (Cu) of the samples were obtained in the laboratory. For spectral preprocessing, FODs were allowed to vary from 0 to 2 with an increment of 0.2 at each step, and the optimal band combination algorithm was applied to the spectra after FOD. Then, four mathematical models, namely, partial least squares regression (PLSR), adaptive neural fuzzy inference system (ANFIS), random forest (RF) and generalized regression neural network (GRNN), were used to estimate the concentration of Hg, Cr and Cu. Results showed that high-order FOD had an excellent effect in highlighting hidden information and separating minor absorbing peaks, and the optimal band combination algorithm could remove the influence of spectral noise caused by high-order FOD. The incorporation of the optimal band combination algorithm and FOD is able to further mine spectral information. Furthermore, GRNN made an obvious improvement to the estimation accuracy of all studied heavy metals compared to ANFIS, PLSR, and RF. In summary, our results provided more feasibility for the rapid estimation of Hg, Cr, Cu and other heavy metal pollution areas in agricultural soils. Full article

(This article belongs to the Special Issue Monitoring Soil Degradation by Remote Sensing)

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20 pages, 7325 KiB

Open AccessFeature PaperArticle

Satellite Imagery for Monitoring and Mapping Soil Chromium Pollution in a Mine Waste Dump

by Vahid Khosravi, Faramarz Doulati Ardejani, Asa Gholizadeh and Mohammadmehdi Saberioon

Remote Sens. 2021, 13(7), 1277; https://doi.org/10.3390/rs13071277 - 27 Mar 2021

Cited by 18 | Viewed by 4994

Abstract

Weathering and oxidation of sulphide minerals in mine wastes release toxic elements in surrounding environments. As an alternative to traditional sampling and chemical analysis methods, the capability of proximal and remote sensing techniques was investigated in this study to predict Chromium (Cr) concentration in 120 soil samples collected from a dumpsite in Sarcheshmeh copper mine, Iran. The samples’ mineralogy and Cr concentration were determined and were then subjected to laboratory reflectance spectroscopy in the range of Visible–Near Infrared–Shortwave Infrared (VNIR–SWIR: 350–2500 nm). The raw spectra were pre-processed using Savitzky-Golay First-Derivative (SG-FD) and Savitzky-Golay Second-Derivative (SG-SD) algorithms. The important wavelengths were determined using Partial Least Squares Regression (PLSR) coefficients and Genetic Algorithm (GA). Artificial Neural Networks (ANN), Stepwise Multiple Linear Regression (SMLR) and PLSR data mining methods were applied to the selected spectral variables to assess Cr concentration. The developed models were then applied to the selected bands of Aster, Hyperion, Sentinel-2A, and Landsat 8-OLI satellite images of the area. Afterwards, rasters obtained from the best prediction model were segmented using a binary fitness function. According to the outputs of the laboratory reflectance spectroscopy, the highest prediction accuracy was obtained using ANN applied to the SD pre-processed spectra with

R^{2}

= 0.91, RMSE = 8.73 mg/kg and RPD = 2.76. SD-ANN also showed an acceptable performance on mapping the spatial distribution of Cr using the ordinary kriging technique. Using satellite images, SD-SMLR provided the best prediction models with

R^{2}

values of 0.61 and 0.53 for Hyperion and Sentinel-2A, respectively. This led to the higher visual similarity of the segmented Hyperion and Sentinel-2A images with the Cr distribution map. This study’s findings indicated that applying the best prediction models obtained by spectroscopy to the selected wavebands of Hyperion and Sentinel-2A satellite imagery could be considered a promising technique for rapid, cost-effective and eco-friendly assessment of Cr concentration in highly heterogeneous mining areas. Full article

(This article belongs to the Special Issue Monitoring Soil Degradation by Remote Sensing)

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28 pages, 12819 KiB

Open AccessArticle

The Use of Deep Machine Learning for the Automated Selection of Remote Sensing Data for the Determination of Areas of Arable Land Degradation Processes Distribution

by Dmitry I. Rukhovich, Polina V. Koroleva, Danila D. Rukhovich and Natalia V. Kalinina

Remote Sens. 2021, 13(1), 155; https://doi.org/10.3390/rs13010155 - 5 Jan 2021

Cited by 28 | Viewed by 5970

Abstract

Soil degradation processes are widespread on agricultural land. Ground-based methods for detecting degradation require a lot of labor and time. Remote methods based on the analysis of vegetation indices can significantly reduce the volume of ground surveys. Currently, machine learning methods are increasingly being used to analyze remote sensing data. In this paper, the task is set to apply deep machine learning methods and methods of vegetation indices calculation to automate the detection of areas of soil degradation development on arable land. In the course of the work, a method was developed for determining the location of degraded areas of soil cover on arable fields. The method is based on the use of multi-temporal remote sensing data. The selection of suitable remote sensing data scenes is based on deep machine learning. Deep machine learning was based on an analysis of 1028 scenes of Landsats 4, 5, 7 and 8 on 530 agricultural fields. Landsat data from 1984 to 2019 was analyzed. Dataset was created manually for each pair of “Landsat scene”/“agricultural field number”(for each agricultural field, the suitability of each Landsat scene was assessed). Areas of soil degradation were calculated based on the frequency of occurrence of low NDVI values over 35 years. Low NDVI values were calculated separately for each suitable fragment of the satellite image within the boundaries of each agricultural field. NDVI values of one-third of the field area and lower than the other two-thirds were considered low. During testing, the method gave 12.5% of type I errors (false positive) and 3.8% of type II errors (false negative). Independent verification of the method was carried out on six agricultural fields on an area of 713.3 hectares. Humus content and thickness of the humus horizon were determined in 42 ground-based points. In arable land degradation areas identified by the proposed method, the probability of detecting soil degradation by field methods was 87.5%. The probability of detecting soil degradation by ground-based methods outside the predicted regions was 3.8%. The results indicate that deep machine learning is feasible for remote sensing data selection based on a binary dataset. This eliminates the need for intermediate filtering systems in the selection of satellite imagery (determination of clouds, shadows from clouds, open soil surface, etc.). Direct selection of Landsat scenes suitable for calculations has been made. It allows automating the process of constructing soil degradation maps. Full article

(This article belongs to the Special Issue Monitoring Soil Degradation by Remote Sensing)

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26 pages, 4026 KiB

Open AccessArticle

Using Apparent Electrical Conductivity as Indicator for Investigating Potential Spatial Variation of Soil Salinity across Seven Oases along Tarim River in Southern Xinjiang, China

by Jianli Ding, Shengtian Yang, Qian Shi, Yang Wei and Fei Wang

Remote Sens. 2020, 12(16), 2601; https://doi.org/10.3390/rs12162601 - 12 Aug 2020

Cited by 32 | Viewed by 4852

Abstract

Soil salinization is a major soil health issue globally. Over the past 40 years, extreme weather and increasing human activity have profoundly changed the spatial distribution of land use and water resources across seven oases in southern Xinjiang, China. However, knowledge of the spatial distribution of soil salinization in this region has not been updated since a land survey in the 1970s to 1980s (the harmonized world soil database, HWSD) due to scarce observational data. Now, given the uncertainty raised by near future climate change, it is important to develop quick, reliable and accurate estimates of soil salinity at larger scales for a better manage strategy to the local fragile ecosystem that with limited land and water resources. This study collected electromagnetic induction (EMI) readings near surface soil to update on the spatial distribution and changes of water and salt in the region and to map apparent electrical conductivity (ECa, mS·m⁻¹), in four coil configurations: vertical dipole in 1.50 m (ECav01) and 0.75 m (ECav05), so as the horizontal dipole in 0.75 m (ECah01) and 0.37 m (ECah05), then all the ECa coil configurations were modeled with random forest algorithm. The validation results showed an R² range of 0.77–0.84 and an RMSE range of 115.17–142.76 mS·m⁻¹. The validation accuracy of deep ECa dipole (ECah01, ECav05, and ECav01) was greater than that of shallow ECa (ECah05), as the former integrated a thicker portion of the subsurface. The range of EC spatial variability that can be explained by ECa is 0.19–0.36 (farmland, mean value is 0.28), grassland is 0.16–0.49 (shrub/grassland, mean value is 0.34), and bare land is 0.28–0.70 (bare land, mean value is 0.56). Among them, ECav01 has the best predictive ability. As the depth increased, the influence of soil-related variables decreased, and the contribution of climate-related variables increased. The main factor affecting ECa variation was climate-related variables, followed by vegetation-related variables and soil-related variables. Scatter plot show ECa was significantly correlated with ECe_HWSD_030 (0–30 cm, r = 0.482, p < 0.01) and ECe_HWSD_30100 (30–100 cm, r = 0.556, p < 0.01). The predicted spatial ECa maps were similar to the ECe values from HWSD, but also implies that the distribution of soil water and salt has undergone tremendous changes since 1980s. The study demonstrates that EMI data provide a reliable and cost-effective tool for obtaining high-resolution soil maps that can be used for better land evaluation and soil improvement at larger scales. Full article

(This article belongs to the Special Issue Monitoring Soil Degradation by Remote Sensing)

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20 pages, 17419 KiB

Open AccessArticle

The Capability of Integrating Optical and Microwave Data for Detecting Soil Moisture in an Oasis Region

by Shuai Huang, Jianli Ding, Bohua Liu, Xiangyu Ge, Jinjie Wang, Jie Zou and Junyong Zhang

Remote Sens. 2020, 12(9), 1358; https://doi.org/10.3390/rs12091358 - 25 Apr 2020

Cited by 12 | Viewed by 3257

Abstract

In the earth ecosystem, surface soil moisture is an important factor in the process of energy exchange between land and atmosphere, which has a strong control effect on land surface evapotranspiration, water migration, and carbon cycle. Soil moisture is particularly important in an oasis region because of its fragile ecological environment. Accordingly, a soil moisture retrieval model was conducted based on Dubois model and ratio model. Based on the Dubois model, the in situ soil roughness was used to simulate the backscattering coefficient of bare soil, and the empirical relationship was established with the measured soil moisture. The ratio model was used to eliminate the backscattering contribution of vegetation, in which three vegetation indices were used to characterize vegetation growth. The results were as follows: (1) the Dubois model was used to calibrate the unknown parameters of the ratio model and verified the feasibility of the ratio model to simulate the backscattering coefficient. (2) All three vegetation indices (Normalized Difference Vegetation Index (NDVI), Vegetation Water Content (VWC), and Enhanced Vegetation Index (EVI)) can represent the scattering characteristics of vegetation in an oasis region, but the VWC vegetation index is more suitable than the others. (3) Based on the Dubois model and ratio model, the soil moisture retrieval model was conducted, and the in situ soil moisture was used to analyze the accuracy of the simulated soil moisture, which found that the soil moisture retrieval accuracy is the highest under VWC vegetation index, and the coefficient of determination is 0.76. The results show that the soil moisture retrieval model conducted on the Dubois model and ratio model is feasible. Full article

(This article belongs to the Special Issue Monitoring Soil Degradation by Remote Sensing)

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15 pages, 5005 KiB

Open AccessArticle

Limitations in Validating Derived Soil Water Content from Thermal/Optical Measurements Using the Simplified Triangle Method

by Abba Aliyu Kasim, Toby Nahum Carlson and Haruna Shehu Usman

Remote Sens. 2020, 12(7), 1155; https://doi.org/10.3390/rs12071155 - 4 Apr 2020

Cited by 9 | Viewed by 3142

Abstract

We assess the validity of the surface moisture availability parameter (M_o) derived from satellite-based optical/thermal measurements using the simplified triangle method. First, we show that M_o values obtained from the simplified triangle method agree closely with those generated from a soil/vegetation/atmosphere/transfer (SVAT) model for scenes over a field site at the Allahabad district, India. Next, we compared M_o values from the simplified triangle method for these same overpass scenes with surface soil water content measured at depths of 5 and 15 cm at this field site. Although a very weak correlation exists between remotely sensed values of M_o for the full scenes and measured soil water content measured at both depths, correlations increasingly improve for the 5 cm samples (but not for the 15 cm samples) as pixels were limited to increasingly smaller vegetation fractions. We conclude that the simplified triangle method would yield reasonable values of M_o and demonstrate good agreement with ground measurements, provided that validation is limited to pixels with little or no vegetation and to soil depths of 5 cm or less. Full article

(This article belongs to the Special Issue Monitoring Soil Degradation by Remote Sensing)

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19 pages, 4273 KiB

Open AccessArticle

Toward Quantifying Oil Contamination in Vegetated Areas Using Very High Spatial and Spectral Resolution Imagery

by Guillaume Lassalle, Arnaud Elger, Anthony Credoz, Rémy Hédacq, Georges Bertoni, Dominique Dubucq and Sophie Fabre

Remote Sens. 2019, 11(19), 2241; https://doi.org/10.3390/rs11192241 - 26 Sep 2019

Cited by 18 | Viewed by 3504

Abstract

Recent remote sensing studies have suggested exploiting vegetation optical properties for assessing oil contamination, especially total petroleum hydrocarbons (TPH) in vegetated areas. Methods based on the tracking of alterations in leaf biochemistry have been proposed for detecting and quantifying TPH under controlled and field conditions. In this study, we expand their use to airborne imagery, in order to monitor oil contamination at a larger scale. Airborne hyperspectral images with very high spatial and spectral resolutions were acquired over an industrial site with oil-contamination (mud pits) and control sites both colonized by Rubus fruticosus L. The method of oil detection exploiting 14 vegetation indices succeeded in classifying the sites in the case of high TPH contamination (overall accuracy ≥ 91.8%). Two methods, based on either the PROSAIL (PROSPECT + SAIL) radiative transfer model or elastic net multiple regression, were also developed for quantifying TPH. Both methods were tested on reflectance measurements in the field, at leaf and canopy scales, and on the image, and achieved accurate predictions of TPH concentrations (RMSE ≤ 3.28 g/kg⁻¹ and RPD ≥ 1.90). The methods were validated on additional sites and open up promising perspectives of operational application for oil and gas companies, with the emergence of new hyperspectral satellite sensors. Full article

(This article belongs to the Special Issue Monitoring Soil Degradation by Remote Sensing)

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18 pages, 4750 KiB

Open AccessArticle

Quantitative Analysis of Spectral Response to Soda Saline-AlkaliSoil after Cracking Process: A Laboratory Procedure to Improve Soil Property Estimation

by Jianhua Ren, Xiaojie Li, Sijia Li, Honglei Zhu and Kai Zhao

Remote Sens. 2019, 11(12), 1406; https://doi.org/10.3390/rs11121406 - 13 Jun 2019

Cited by 4 | Viewed by 3399

Abstract

Cracking on the surface of soda saline-alkali soil is very common. In most previous studies, spectral prediction models of soil salinity were less accurate since spectral measurements were usually performed on 2 mm soil samples which cannot represent true soil surface condition very well. The objective of our research is to provide a procedure to improve soil property estimation of soda saline-alkali soil based on spectral measurement considering the texture feature of the soil surface with cracks. To achieve this objective, a cracking test was performed with 57 soil samples from Songnen Plain of China, the contrast (CON) texture feature of crack images of soil samples was then extracted from grey level co-occurrence matrix (GLCM). The original reflectance was then measured and the mixed reflectance considering the CON texture feature was also calculated from both the block soil samples (soil blocks separated by crack regions) and the comparison soil samples (soil powders with 2 mm particle size). The results of analysis between spectra and the main soil properties indicate that surface cracks can reduce the overall reflectivity of the soda saline-alkali soil and thus increasing the spectral difference among the block soil samples with different salinity levels. The results also show that both univariate and multivariate linear regression models considering the CON texture feature can greatly improve the prediction accuracy of main soil properties of soda saline-alkali soils, such as Na⁺, EC and salinity, which also can reduce the intensity of field spectral measurements under natural condition. Full article

(This article belongs to the Special Issue Monitoring Soil Degradation by Remote Sensing)

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17 pages, 8026 KiB

Open AccessArticle

Analysis of the Development of an Erosion Gully in an Open-Pit Coal Mine Dump During a Winter Freeze-Thaw Cycle by Using Low-Cost UAVs

by Chuangang Gong, Shaogang Lei, Zhengfu Bian, Ying Liu, Zhouai Zhang and Wei Cheng

Remote Sens. 2019, 11(11), 1356; https://doi.org/10.3390/rs11111356 - 5 Jun 2019

Cited by 74 | Viewed by 5411

Abstract

Open-pit coal mine dumps in semi-arid areas in northern China are affected by serious soil erosion problems. The conventional field investigation method cannot ensure a fine spatial analysis of gully erosion. With recent technological and algorithmic developments in high-resolution terrain measurement, Unmanned Aerial Vehicles (UAVs) and Structure from Motion (SfM) technology have become powerful tools to capture high-resolution terrain data. In this study, two UAV Photogrammetry surveys and modeling were performed at one opencast coal mine dump gully before and after a freeze-thaw cycle. Finally, a three-dimensional digital model of the slope of the drainage field was established, and a centimeter-level-resolution Digital Orthophoto Map (DOM) and a Digital Elevation Model (DEM) were created. Moreover, the development process of the erosion zone of the open-pit mine dump during a freeze-thaw cycle was studied by UAVs. The results show that there are clear soil erosion phenomena in the erosion gully of the dump during a freeze-thaw cycle. The erosion degree was different across regions, with the highest erosion occurring in high-slope areas at the upper edge of the bank. Moreover, the phenomenon of flake erosion and “crumble” was recorded. At the same time, the NE-E-SE slope and the high-sunshine radiation zone were seriously eroded. Finally, the relationship between the development process of the erosion gully and micro-topography factors was analyzed, providing managers with a sound scientific basis to implement land restoration. Full article

(This article belongs to the Special Issue Monitoring Soil Degradation by Remote Sensing)

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