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Remote Sensing of Soil Salinity: Detection and Quantification

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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 (1 December 2023) | Viewed by 15707

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Dr. Bifeng Hu

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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; pedometrics; spatio-temporal variation
Special Issues, Collections and Topics in MDPI journals

Dr. Songchao Chen

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Guest Editor

1. ZJU-Hangzhou Global Scientific and Technological Innovation Center, Hangzhou 311200, China
2. Institute of Agricultural Remote Sensing and Information Technology Application, College of Environmental and Resource Sciences, Zhejiang University, Hangzhou 310058, China
Interests: pedometrics; digital soil mapping; proximal soil sensing; soil spectroscopy; spatial predictive modelling; soil biogeochemical modelling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a current global issue, soil salinization is critically aﬀecting our limited soil resource and deteriorating the ecosystem health. It poses a great threat to biodiversity, food security and the quality of the environment. To meet the rapidly increasing demand for food, saline soils have been reclaimed for agricultural operations to release extraordinary pressure on existing degraded land resources, which may accelerate the degradation of saline soils. Thus, dynamic detection of soil salinization is an urgent demand to provide more quantitative information for land reclamation since soil salinity has a high spatio-temporal variability. Traditional measurements of soil salinization using laboratory-based methods are expensive and time consuming and thus it can not to meet the increasing demand for accurate information of spatio-temporal of soil salinity. The development of remote sensing technology provides a new solution to fill this gap. Remote sensing technology has great advantages in monitoring soil conditions at a broad scale at high temporal resolution, which enables to map the spatio-temporal variation of soil salinity over a large area.

In this Special Issue, we are seeking original scientific research or manuscript that addresses detection and quantification of soil salinity using passive and/or active remote sensors and platforms (e.g., multi- and hyperspectral domain, SAR, LiDAR, RADAR). The Special Issue welcomes a wide range of contributions from methodological to applied, multidisciplinary research, and aims to provide new implications for connecting researchers working in related field and thus to make a better contribution in dealing with the increasing soil salinization around the world.

Dr. Bifeng Hu
Dr. Songchao Chen
Guest Editors

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Keywords

soil salinity
remote sensing
digital soil mapping
machine learning
artificial intelligence
multi-sensor fusion
spatio-temporal modelling

Published Papers (6 papers)

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Research

13 pages, 4702 KiB

Open AccessArticle

Global Soil Salinity Prediction by Open Soil Vis-NIR Spectral Library

by Yin Zhou, Songchao Chen, Bifeng Hu, Wenjun Ji, Shuo Li, Yongsheng Hong, Hanyi Xu, Nan Wang, Jie Xue, Xianglin Zhang, Yi Xiao and Zhou Shi

Remote Sens. 2022, 14(21), 5627; https://doi.org/10.3390/rs14215627 - 7 Nov 2022

Cited by 14 | Viewed by 4024

Abstract

Soil salinization is one of the major degradation processes threatening food security and sustainable development. Detailed soil salinity information is increasingly needed to tackle this global challenge for improving soil management. Soil-visible and near-infrared (Vis-NIR) spectroscopy has been proven to be a potential solution for estimating soil-salinity-related information (i.e., electrical conductivity, EC) rapidly and cost-effectively. However, previous studies were mainly conducted at the field, regional, or national scale, so the potential application of Vis-NIR spectroscopy at a global scale needs further investigation. Based on an extensive open global soil spectral library (61,486 samples with both EC and Vis-NIR spectra), we compared four spectral predictive models (PLSR, Cubist, Random Forests, and XGBoost) in estimating EC. Our results indicated that XGBoost had the best model performance (R² of 0.59, RMSE of 1.96 dS m⁻¹) in predicting EC at a global scale, whereas PLSR had a relatively limited ability (R² of 0.39, RMSE of 2.41 dS m⁻¹). The results also showed that auxiliary environmental covariates (i.e., coordinates, elevation, climatic variables) could greatly improve EC prediction accuracy by the four models, and the XGBoost performed best (R² of 0.71, RMSE of 1.65 dS m⁻¹). The outcomes of this study provide a valuable reference for improving broad-scale soil salinity prediction by the coupling of the spectroscopic technique and easily obtainable environmental covariates. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Salinity: Detection and Quantification)

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16 pages, 2251 KiB

Open AccessArticle

Desert Soil Salinity Inversion Models Based on Field In Situ Spectroscopy in Southern Xinjiang, China

by Yu Wang, Modong Xie, Bifeng Hu, Qingsong Jiang, Zhou Shi, Yinfeng He and Jie Peng

Remote Sens. 2022, 14(19), 4962; https://doi.org/10.3390/rs14194962 - 5 Oct 2022

Cited by 12 | Viewed by 2479

Abstract

Soil salinization is prominent environmental issue in arid and semi-arid regions, such as Xinjiang in Northwest China. Salinization severely restricts economic and agricultural development and would lead to ecosystem degradation. Finding a method of rapidly and accurately determining soil salinity (SS) is one of the main challenges in salinity evaluation, saline soil development, and utilization. In situ visible and near infrared (Vis-NIR) spectroscopy has proven to be a promising technique for detecting soil properties since it can realize real-time, rapid detection of SS. However, it still remains challenging whether Vis-NIR in situ spectroscopy can invert SS with high accuracy due to the interference of environmental factors (e.g., light, water vapor, solar altitude angle, etc.) on the spectral in the field. To fill this knowledge gap, we collected Vis-NIR in situ spectral and lab-measured SS data from 135 surface soil samples in the Kongterik Pasture Nature Reserve (KPNR) in the desert oasis ecotone of southern Xinjiang, China. We used genetic algorithm (GA), particle swarm optimization (PSO), and simulated annealing (SA) algorithms to select the feature bands of SS. Subsequently, we combined extreme learning machines (ELM), back-propagation neural networks (BPNN), and convolutional neural networks (CNN) to build inversion models of SS. The results showed that different feature bands selection methods could improve the Vis-NIR in situ spectral prediction model accuracy. Either SS inversion models were built using full-band spectral data or feature-band spectral data. Compared with the full-band (401–2400 nm) spectral modeling, the validation set R² of ELM, BPNN, and CNN models built selected feature bands selected by PSO, GA, and SA, respectively, were improved by more than 0.06. The accuracy of predicting SS varied widely among modeling methods. The accuracy of CNN model was obviously higher than that of BPNN and ELM models. The optimal hybrid model for predicting SS constructed in this study is SA-CNN model (R² = 0.79, RMSE = 9.41 g kg⁻¹, RPD = 1.81, RPIQ = 2.37). This study showed that the spectral feature bands selection methods can reduce the influence of environmental factors on in situ spectroscopy and significantly enhance the inversion accuracy of SS. The present study provided that estimating SS using in situ Vis-NIR spectral is feasible. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Salinity: Detection and Quantification)

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10 pages, 5504 KiB

Open AccessArticle

Proximal Soil Sensing of Low Salinity in Southern Xinjiang, China

by Jie Peng, Shuo Li, Randa S. Makar, Hongyi Li, Chunhui Feng, Defang Luo, Jiali Shen, Ying Wang, Qingsong Jiang and Linchuan Fang

Remote Sens. 2022, 14(18), 4448; https://doi.org/10.3390/rs14184448 - 6 Sep 2022

Cited by 5 | Viewed by 1543

Abstract

Measuring the soil salinity using visible and near-infrared (vis–NIR) reflectance spectra is considered a fast and cost-effective method. For monitoring purposes, estimating soils with low salinity measured as electrical conductivity (EC) using vis–NIR spectra is still understudied. In this research, 399 legacy soil samples from six regions of Southern Xinjiang, China with low EC values were used. Reflectance spectra were measured in the laboratory on dried and ground soil samples using a portable vis–NIR spectrometer. By using 10-fold cross-validation, three algorithms–partial least-squares regression (PLSR), random forest (RF), and Cubist–were employed to develop statistical models of EC. The model performance evaluation was obtained by the relative importance of variants. In terms of accuracy assessment of soil EC prediction, the results demonstrated that the Cubist model performed better (

R^{2}

= 0.67, RMSE = 0.16 mS/cm, RPIQ = 2.28) than both PLSR and RF. Despite similar variants for modelling, the RF model performed somewhat better than that of the PLSR. Additionally, the 610 nm and 790 nm wavelengths only demonstrated significant promise for predicting low soil EC values when used in the Cubist mode. The current research recommends the use of Cubist to estimate the low soil salinity using the vis–NIR reflectance spectra. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Salinity: Detection and Quantification)

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20 pages, 7102 KiB

Open AccessArticle

Depth-Specific Soil Electrical Conductivity and NDVI Elucidate Salinity Effects on Crop Development in Reclaimed Marsh Soils

by José Luis Gómez Flores, Mario Ramos Rodríguez, Alfonso González Jiménez, Mohammad Farzamian, Juan Francisco Herencia Galán, Benito Salvatierra Bellido, Pedro Cermeño Sacristan and Karl Vanderlinden

Remote Sens. 2022, 14(14), 3389; https://doi.org/10.3390/rs14143389 - 14 Jul 2022

Cited by 10 | Viewed by 1860

Abstract

Agricultural management decision-making in salinization-prone environments requires efficient soil salinity monitoring methods. This is the case in the B-XII irrigation district in SW Spain, a heavy clay reclaimed marsh area where a shallow saline water table and intensively irrigated agriculture create a fragile balance between salt accumulation and leaching in the root zone, which might be disrupted by the introduction of new crops and increasing climate variability. We evaluated the potential of electromagnetic induction (EMI) tomography for field-scale soil salinity assessment in this hyper-conductive environment, using EMI and limited analytical soil data measured in 2017 and 2020 under a processing tomato–cotton–sugar beet crop rotation. Salinity effects on crop development were assessed by comparing Sentinel 2 NDVI imagery with inverted depth-specific electrical conductivity (EC). Average apparent electrical conductivity (ECa) for the 1-m depth signal was 20% smaller in 2020 than in 2017, although the spatial ECa pattern was similar for both years. Inverted depth-specific EC showed a strong correlation (R ≈ 0.90) with saturated paste extract EC (ECe), [Na⁺] and sodium absorption ratio (SAR), resulting in linear calibration equations with R² ≈ 0.8 for both years and leave-one-out cross validation Nash–Sutcliffe Efficiency Coefficient, ranging from 0.57 to 0.74. Overall, the chemical parameter estimation improved with depth and soil wetness (2017), yielding 0.83 < R <0.98 at 0.9 m. The observed spatial EC distributions showed a steadily increasing inverse correlation with NDVI during the growing season, particularly for processing tomato and cotton, reaching R values of −0.71 and −0.85, respectively. These results confirm the potential of EMI tomography for mapping and monitoring soil salinity in the B-XII irrigation district, while it allows, in combination with NDVI imagery, a detailed spatial assessment of soil salinity impacts on crop development throughout the growing season. Contrary to the popular belief among farmers in the area, and despite non-saline topsoil conditions, spatial EC and subsoil salinity patterns were found to affect crop development negatively in the studied field. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Salinity: Detection and Quantification)

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24 pages, 13166 KiB

Open AccessArticle

The Role of Soil Salinization in Shaping the Spatio-Temporal Patterns of Soil Organic Carbon Stock

by Wenli Zhang, Wei Zhang, Yubing Liu, Jutao Zhang, Linshan Yang, Zengru Wang, Zhongchao Mao, Shi Qi, Chengqi Zhang and Zhenliang Yin

Remote Sens. 2022, 14(13), 3204; https://doi.org/10.3390/rs14133204 - 4 Jul 2022

Cited by 5 | Viewed by 1979

Abstract

Soil salinization is closely related to land degradation, and it is supposed to exert a significant negative effect on soil organic carbon (SOC) stock dynamics. This effect and its mechanism have been examined at site and transect scales in previous studies while over a large spatial extent, the salinity-induced changes in SOC stock over space and time have been less quantified, especially by machine learning and remote sensing techniques. The main focus of this study is to answer the following question: to what extent can soil salinity exert an additional effect on SOC stock over time at a larger spatial scale? Thus, we employed the extreme gradient boosting models (XGBoost) combined with field site-level measurements from 433 sites and 41 static and time-varying environmental covariates to construct methods capable of quantifying the salinity-induced SOC changes in a typical inland river basin of China between the 1990s and 2020s. Results showed that the XGBoost models performed well in predicting the soil electrical conductivity (EC) and SOC stock at 0–20 cm, with the R² value reaching 0.85 and 0.81, respectively. SOC stock was found to vary significantly with increasing soil salinity following an exponential decay function (R² = 0.27), and salinity sensitivity analysis showed that soils in oasis were expected to experience the largest carbon loss (−137.78 g m⁻²), which was about 4.84, 14.37, and 25.95 times higher than that in the saline, bare, and sandy land, respectively, if the soil salinity increased by 100%. In addition, the decrease in the soil salinity (−0.32 dS m⁻¹) from the 1990s to the 2020s was estimated to enhance the SOC stock by 0.015 kg m⁻², which contributed an additional 10% increase to the total SOC stock enhancement. Overall, the proposed methods can be applied for quantification of the direction and size of the salinity effect on SOC stock changes in other salt-affected regions. Our results also suggest that the role of soil salinization should not be neglected in SOC changes projection, and soil salinization control measures should be further taken into practice to enhance soil carbon sequestration in arid inland river basins. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Salinity: Detection and Quantification)

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17 pages, 2798 KiB

Open AccessArticle

Mapping the Levels of Soil Salination and Alkalization by Integrating Machining Learning Methods and Soil-Forming Factors

by Yang Yan, Kader Kayem, Ye Hao, Zhou Shi, Chao Zhang, Jie Peng, Weiyang Liu, Qiang Zuo, Wenjun Ji and Baoguo Li

Remote Sens. 2022, 14(13), 3020; https://doi.org/10.3390/rs14133020 - 23 Jun 2022

Cited by 4 | Viewed by 2419

Abstract

Accurate updating of soil salination and alkalization maps based on remote sensing images and machining learning methods plays an essential role in food security, biodiversity, and desertification. However, there is still a lack of research on using machine learning, especially one-dimensional convolutional neural networks (CNN)s, and soil-forming factors to classify the salinization and alkalization degree. As a case study, the study estimated the soil salination and alkalization by Random forests (RF) and CNN based on the 88 observations and 16 environmental covariates in Da’an city, China. The results show that: the RF model (accuracy = 0.67, precision = 0.67 for soil salination) with the synthetic minority oversampling technique performed better than CNN. Salinity and vegetation spectral indexes played the most crucial roles in soil salinization and alkalinization estimation in Songnen Plain. The spatial distribution derived from the RF model shows that from the 1980s to 2021, soil salinization and alkalization areas increased at an annual rate of 1.40% and 0.86%, respectively, and the size of very high salinization and alkalization was expanding. The degree and change rate of soil salinization and alkalization under various land-use types followed mash > salinate soil > grassland > dry land and forest. This study provides a reference for rapid mapping, evaluating, and managing soil salinization and alkalization in arid areas. Full article

(This article belongs to the Special Issue Remote Sensing of Soil Salinity: Detection and Quantification)

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