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Advances of Proximal and Remote Sensing in Soil Salinity Mapping

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: closed (20 December 2021) | Viewed by 29872

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Dr. Asim Biswas

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Guest Editor

School of Environmental Sciences, University of Guelph, Guelph, ON N1G 2W1, Canada
Interests: soil physics; vadose zone hydrology; precision agriculture; proximal soil sensing; digital soil mapping; soil spatial variability
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Hongyi Li

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Guest Editor

School of Tourism and Urban Management, Jiangxi University of Finance and Economics, Nanchang 330013, China
Interests: proximal soil sensing; remote sensing; digital soil mapping

Special Issue Information

Dear Colleagues,

Today, more people are living on less land and in a climate that is more uncertain than ever before. Moreover, growing food, feed, fuel and fiber for the ever-increasing population has come at a great cost to our limited land resources through the degradation of soil, air and water quality. Soil salinization, a widespread soil degradation process, is constantly threatening agricultural production, environmental health and the functioning of our ecosystem. The changing climate, land use, agricultural activities and land management are increasing salinization. Thus, only the proper management of land resources can make the system sustainable. However, proper management is only possible through the better measurement and modelling of saline soils. While traditional measurements using laboratory-based methods are expensive and time consuming, the introduction of proximal and remote sensing sensors and platforms has greatly supported the way we can measure and model soil salinity and map them in a larger area using digital soil mapping techniques.

In this Special Issue, we are soliciting research or manuscripts advancing soil salinity measurement, modelling and mapping using proximal soil sensing and remote sensing sensors and platform and digital soil mapping. This Special Issue aims to bring together research from around the world on the advances in soil salinity measurement, mapping and modelling using various proximal and remote sensing sensors and platforms and to help connect researchers working in a similar area to tackle the globally critical issue and enhance soil security.

You may choose our Joint Special Issue in Land.

Dr. Asim Biswas
Dr. Hongyi Li
Guest Editors

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Keywords

soil salinity
proximal soil sensing
remote sensing
electrical conductivity
electromagnetic induction
digital soil mapping
inverse modelling
machine learning
artificial intelligence
arid and semi-arid climate

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Published Papers (5 papers)

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Research

21 pages, 4215 KiB

Open AccessArticle

Ground Observations and Environmental Covariates Integration for Mapping of Soil Salinity: A Machine Learning-Based Approach

by Salman Naimi, Shamsollah Ayoubi, Mojtaba Zeraatpisheh and Jose Alexandre Melo Dematte

Remote Sens. 2021, 13(23), 4825; https://doi.org/10.3390/rs13234825 - 27 Nov 2021

Cited by 33 | Viewed by 5084

Abstract

Soil salinization is a severe danger to agricultural activity in arid and semi-arid areas, reducing crop production and contributing to land destruction. This investigation aimed to utilize machine learning algorithms to predict spatial soil salinity (dS m⁻¹) by combining environmental covariates derived from remotely sensed (RS) data, a digital elevation model (DEM), and proximal sensing (PS). The study is located in an arid region, southern Iran (52°51′–53°02′E; 28°16′–28°29′N), in which we collected 300 surface soil samples and acquired the spectral data with RS (Sentinel-2) and PS (electromagnetic induction instrument (EMI) and portable X-ray fluorescence (pXRF)). Afterward, we analyzed the data using five machine learning methods as follows: random forest—RF, k-nearest neighbors—kNN, support vector machines—SVM, partial least squares regression—PLSR, artificial neural networks—ANN, and the ensemble of individual models. To estimate the electrical conductivity of the saturated paste extract (ECe), we built three scenarios, including Scenario (1): Synthetic Soil Image (SySI) bands and salinity indices derived from it; Scenario (2): RS data, PS data, topographic attributes, and geology and geomorphology maps; and Scenario (3): the combination of Scenarios (1) and (2). The best prediction accuracy was obtained for the RF model in Scenario (3) (R² = 0.48 and RMSE = 2.49), followed by Scenario (2) (RF model, R² = 0.47 and RMSE = 2.50) and Scenario (1) for the SVM model (R² = 0.26 and RMSE = 2.97). According to ensemble modeling, a combined strategy with the five models exceeded the performance of all the single ones and predicted soil salinity in all scenarios. The results revealed that the ensemble modeling method had higher reliability and more accurate predictive soil salinity than the individual approach. Relative improvement (RI%) showed that the R² index in the ensemble model improved compared to the most precise prediction for the Scenarios (1), (2), and (3) with 120.95%, 56.82%, and 66.71%, respectively. We applied the best model in each scenario for mapping the soil salinity in the selected area, which indicated that ECe tended to increase from the northwestern to south and southeastern regions. The area with high ECe was located in the regions that mainly had low elevations and playa. The areas with low ECe were located in the higher elevations with steeper slopes and alluvial fans, and thus, relief had great importance. This study provides a precise, cost-effective, and scientific base prediction for decision-making purposes to map soil salinity in arid regions. Full article

(This article belongs to the Special Issue Advances of Proximal and Remote Sensing in Soil Salinity Mapping)

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16 pages, 10809 KiB

Open AccessArticle

Spatial and Temporal Variability of Soil Salinity in the Yangtze River Estuary Using Electromagnetic Induction

by Wenping Xie, Jingsong Yang, Rongjiang Yao and Xiangping Wang

Remote Sens. 2021, 13(10), 1875; https://doi.org/10.3390/rs13101875 - 11 May 2021

Cited by 22 | Viewed by 3061

Abstract

Soil salt-water dynamics in the Yangtze River Estuary (YRE) is complex and soil salinity is an obstacle to regional agricultural production and the ecological environment in the YRE. Runoff into the sea is reduced during the impoundment period as the result of the water-storing process of the Three Gorges Reservoir (TGR) in the upper reaches of the Yangtze River, which causes serious seawater intrusion. Soil salinity is a problem due to shallow and saline groundwater under serious seawater intrusion in the YRE. In this research, we focused on the temporal variation and spatial distribution characteristics of soil salinity in the YRE using geostatistics combined with proximally sensed information obtained by an electromagnetic induction (EM) survey method in typical years under the impoundment of the TGR. The EM survey with proximal sensing method was applied to perform soil salinity survey in field in the Yangtze River Estuary, allowing quick determination and quantitative assessment of spatial and temporal variation of soil salinity from 2006 to 2017. We developed regional soil salinity survey and mapping by coupling limited laboratory data with proximal sensed data obtained from EM. We interpreted the soil electrical conductivity by constructing a linear model between the apparent electrical conductivity data measured by an EM 38 device and the soil electrical conductivity (EC) of soil samples measured in laboratory. Then, soil electrical conductivity was converted to soil salt content (soil salinity g kg⁻¹) through established linear regression model based on the laboratory data of soil salinity and soil EC. Semivariograms of regional soil salinity in the survey years were fitted and ordinary kriging interpolation was applied in interpolation and mapping of regional soil salinity. The cross-validation results showed that the prediction results were acceptable. The soil salinity distribution under different survey years was presented and the area of salt affected soil was calculated using geostatistics method. The results of spatial distribution of soil salinity showed that soil salinity near the riverbanks and coastlines was higher than that of inland. The spatial distribution of groundwater depth and salinity revealed that shallow groundwater and high groundwater salinity influenced the spatial distribution characteristics of soil salinity. Under long-term impoundment of the Three Gorges Reservoir, the variation of soil salinity in different hydrological years was analyzed. Results showed that the area affected by soil salinity gradually increased in different hydrological year types under the impoundment of the TGR. Full article

(This article belongs to the Special Issue Advances of Proximal and Remote Sensing in Soil Salinity Mapping)

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14 pages, 4066 KiB

Open AccessArticle

Soil Salinity Mapping Using Machine Learning Algorithms with the Sentinel-2 MSI in Arid Areas, China

by Jiaqiang Wang, Jie Peng, Hongyi Li, Caiyun Yin, Weiyang Liu, Tianwei Wang and Huaping Zhang

Remote Sens. 2021, 13(2), 305; https://doi.org/10.3390/rs13020305 - 17 Jan 2021

Cited by 89 | Viewed by 9905

Abstract

Accurate monitoring of soil salinization plays a key role in the ecological security and sustainable agricultural development of arid regions. As a branch of artificial intelligence, machine learning acquires new knowledge through self-learning and continuously improves its own performance. The purpose of this study is to combine Sentinel-2 Multispectral Imager (MSI) data and MSI-derived covariates with measured soil salinity data and to apply three machine learning algorithms in modeling to estimate and map the soil salinity in the study sample area. According to the convenient transportation conditions, the study area and sampling quadrat were set up, and the 5-point method was used to collect the soil mixed samples, and 160 soil mixed samples were collected. Kennard–Stone (K–S) algorithm was used for sample classification, 70% for modeling and 30% for verification. The machine learning algorithm uses Support Vector Machines (SVM), Artificial Neural Network (ANN), and Random Forest (RF). The results showed that (1) the average reflectance of each band of the MSI data ranged from 0.21–0.28. According to the spectral characteristics corresponding to different soil electrical conductivity (EC) levels (1.07–79.6 dS m⁻¹), the spectral reflectance of salinized soil in the MSI data ranged from 0.09–0.35. (2) The correlation coefficient between the MSI data and MSI-derived covariates and soil EC was moderate, and the correlation between certain MSI data sets and soil EC was not significant. (3) The SVM soil EC estimation model established with the MSI data set attained a higher performance and accuracy (R² = 0.88, root mean square error (RMSE) = 4.89 dS m⁻¹, and ratio of the performance to the interquartile range (RPIQ) = 1.96, standard error of the laboratory measurements to the standard error of the predictions (SEL/SEP) = 1.11) than those attained with the soil EC estimation models established with the RF and ANN models. (4) We applied the SVM soil EC estimation model to map the soil salinity in the study area, which showed that the farmland with higher altitudes discharged a large amount of salt to the surroundings due to long-term irrigation, and the secondary salinization of the farmland also caused a large amount of salt accumulation. This research provides a scientific basis for the simulation of soil salinization scenarios in arid areas in the future. Full article

(This article belongs to the Special Issue Advances of Proximal and Remote Sensing in Soil Salinity Mapping)

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21 pages, 4210 KiB

Open AccessArticle

Integrating Remote Sensing and Landscape Characteristics to Estimate Soil Salinity Using Machine Learning Methods: A Case Study from Southern Xinjiang, China

by Nan Wang, Jie Xue, Jie Peng, Asim Biswas, Yong He and Zhou Shi

Remote Sens. 2020, 12(24), 4118; https://doi.org/10.3390/rs12244118 - 16 Dec 2020

Cited by 79 | Viewed by 5877

Abstract

Soil salinization, one of the most severe global land degradation problems, leads to the loss of arable land and declines in crop yields. Monitoring the distribution of salinized soil and degree of salinization is critical for management, remediation, and utilization of salinized soil; however, there is a lack of thorough assessment of various data sources including remote sensing and landscape characteristics for estimating soil salinity in arid and semi-arid areas. The overall goal of this study was to develop a framework for estimating soil salinity in diverse landscapes by fusing information from satellite images, landscape characteristics, and appropriate machine learning models. To explore the spatial distribution of soil salinity in southern Xinjiang, China, as a case study, we obtained 151 soil samples in a field campaign, which were analyzed in laboratory for soil electrical conductivity. A total of 35 indices including remote sensing classifiers (11), terrain attributes (3), vegetation spectral indices (8), and salinity spectral indices (13) were calculated or derived and correlated with soil salinity. Nine were used to model and estimate soil salinity using four predictive modelling approaches: partial least squares regression (PLSR), convolutional neural network (CNN), support vector machine (SVM) learning, and random forest (RF). Testing datasets were divided into vegetation-covered and bare soil samples and were used for accuracy assessment. The RF model was the best regression model in this study, with R² = 0.75, and was most effective in revealing the spatial characteristics of salt distribution. Importance analysis and path modeling of independent variables indicated that environmental factors and soil salinity indices including digital elevation model (DEM), B10, and green atmospherically resistant vegetation index (GARI) showed the strongest contribution in soil salinity estimation. This showed a great promise in the measurement and monitoring of soil salinity in arid and semi-arid areas from the integration of remote sensing, landscape characteristics, and using machine learning model. Full article

(This article belongs to the Special Issue Advances of Proximal and Remote Sensing in Soil Salinity Mapping)

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17 pages, 3923 KiB

Open AccessArticle

Field-Scale Characterization of Spatio-Temporal Variability of Soil Salinity in Three Dimensions

by Hongyi Li, Xinlu Liu, Bifeng Hu, Asim Biswas, Qingsong Jiang, Weiyang Liu, Nan Wang and Jie Peng

Remote Sens. 2020, 12(24), 4043; https://doi.org/10.3390/rs12244043 - 10 Dec 2020

Cited by 26 | Viewed by 4477

Abstract

Information on spatial, temporal, and depth variability of soil salinity at field and landscape scales is important for a variety of agronomic and environment concerns including irrigation in arid and semi-arid areas. However, challenges remain in characterizing and monitoring soil secondary salinity as it can largely be impacted by managements including irrigation and mulching in addition to natural factors. The objective of this study is to evaluate apparent electrical conductivity (EC_a)-directed soil sampling as a basis for monitoring management-induced spatio-temporal change in soil salinity in three dimensions. A field experiment was conducted on an 18-ha saline-sodic field from Alar’s Agricultural Science and Technology Park, China between March, and November 2018. Soil EC_a was measured using an electromagnetic induction (EMI) sensor for four times over the growing season and soil core samples were collected from 18 locations (each time) selected using EMI survey data as a-priori information. A multi-variate regression-based predictive relationship between EC_a and laboratory-measured electrical conductivity (EC_e) was used to predict EC with confidence (R² between 0.82 and 0.99). A three-dimensional inverse distance weighing (3D-IDW) interpolation clearly showed a strong variability in space and time and with depths within the study field which were mainly attributed to the human management factors including irrigation, mulching, and uncovering of soils and natural factors including air temperature, evaporation, and groundwater level. This study lays a foundation of characterizing secondary salinity at a field scale for precision and sustainable management of agricultural lands in arid and semi-arid areas. Full article

(This article belongs to the Special Issue Advances of Proximal and Remote Sensing in Soil Salinity Mapping)

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