Monitoring properties of the salt-affected soils by multivariate analysis of the visible and near-infrared hyperspectral data

Mahajan, Gopal Ramdas; Das, Bappa; Gaikwad, Bhaskar; Murgaonkar, Dayesh; Desai, A. R.; Morajkar, Shaiesh; Patel, Kiran Puna; Kulkarni, Rahul M.

doi:10.1016/j.catena.2020.105041

Cited by 32 publications

(18 citation statements)

References 93 publications

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“…Vegetation indices show better performance when corrected using soil indices, a finding that demonstrates both the effect of soil on the spectral response of the surface and the presence of a discontinuous or weaklydeveloped cover (Metternicht and Zinck, 2003). For this reason, multivariate (PCA, PLS) and computational methods (Support Vector Machine, Artificial Neural Networks) for predicting soil salinity from several remote sensing indices, both pedological and vegetal, are currently considered to be the best approach for salinity mapping (Scudiero et al, 2014;Fan et al, 2015;Taghadosi et al, 2019;Wang et al, 2019;Mahajan et al, 2020).…”

Section: Remote Sensing Methodsmentioning

confidence: 99%

Soil Salinity and Sodicity in Drylands: A Review of Causes, Effects, Monitoring, and Restoration Measures

Stavi

Thevs

Priori

2021

Front. Environ. Sci.

181

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Soil salinization and sodification are common processes that particularly characterize drylands. These processes can be attributed either to natural conditions or anthropogenic activities. While natural causes include factors such as climate, lithology, topography, and pedology, human causes are mostly related to agricultural land-use, and specifically, to irrigated agriculture. The objective of this study was to thoroughly review this topic, while highlighting the major challenges and related opportunities. Over time, the extent of saline, sodic, and saline-sodic croplands has increased, resulting in accelerated land degradation and desertification, decreased agricultural productivity, and consequently jeopardizing environmental and food security. Mapping and monitoring saline soils is an important management tool, aimed at determining the extent and severity of salinization processes. Recent developments in advanced remote sensing methods have improved the efficacy of mapping and monitoring saline soils. Knowledge on prevention, mitigation, and recovery of soil salinity and sodicity has substantially grown over time. This knowledge includes advanced measures for salt flushing and leaching, water-saving irrigation technologies, precision fertilizer systems, chemical restoration, organic and microbial remediation, and phytoremediation of affected lands. Of a particular interest is the development of forestry-related means, with afforestation, reforestation, agroforestry, and silvopasture practices for the recovery of salt-affected soils. The forecasted expansion of drylands and aggravated drying of existing drylands due to climatic change emphasize the importance of this topic.

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Section: Remote Sensing Methodsmentioning

confidence: 99%

Soil Salinity and Sodicity in Drylands: A Review of Causes, Effects, Monitoring, and Restoration Measures

Stavi

Thevs

Priori

2021

Front. Environ. Sci.

181

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“…The spectral reflectance curves have similar trend, increasing the wavelength (λ) increased the reflectance. The reflectance in visible region (400 -700 nm) was lowest that can be explained according to Mahajan et al (2021) by the presence of minerals that contain iron oxide which absorb short wavelengths (less than 0.54 µm). The variations amongst the different studied soil salinity were minor.…”

Section: Resultsmentioning

confidence: 99%

“…During the last two eras several researchers have studied the capabilities of remote sensing data for monitoring and estimating various soil properties in various countries (Poggio and Gimona 2017, Xu et al 2018, Angelopoulou et al 2020, Mahajan et al 2021.…”

Section: Introductionmentioning

confidence: 99%

Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity

Saleh

Abdelwahed

Metwally

et al. 2021

Arab Universities Journal of Agricultural Sciences

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The objectives of the current study were to investigate the opportunity of estimating soil salinity from hyperspectral data and identifying the most informative spectral zones for estimation. Electrical conductivity (EC) measurements of ninety topsoil samples (0-30 cm) collected fromToshka, Egypt, were used as data set. Analytical spectral device was employed to collect the reflectance spectral signatures of soil samples. Both linear regression and HSD Tukey's analyses displayed that the SWIR1 and SWIR2 zones are the most suitable for soil salinity prediction while, blue, green and NIR were the wickedest. Moreover, EC estimation was better in case of lower soil salinity (0-2 dS m -1 ) than higher levels (8

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“…Gopal Ramdas Mahajan et al compared five modelling methods-namely, linear PCR, PLSR, non-linear multiple adaptive regression splines (MARS), RF, and support vector regression (SVR)-and found that the SVR method performed the best based on 372 sites in 17 coastal areas along the west coast of India. PLSR and PCR were found to be better prediction methods than MARS and RF [14]. E.S.…”

Section: Introductionmentioning

confidence: 93%

“…With the rise of remote sensing technology, the application of hyperspectral remote sensing techniques to monitor soil salinity not only saves time and effort, but also exhibits the characteristics of rapid deployment, macroscopic and dynamic features, and a superior performance that cannot be achieved by other means. In recent years, numerous scholars have studied soil salinity estimation for different soil types based on visible-near-infrared (NIR) spectroscopy [13][14][15][16][17][18][19][20][21]. The background, environment, measuring instruments, etc.…”

Section: Introductionmentioning

confidence: 99%

Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

Liu

Bao

et al. 2021

Minerals

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To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model’s accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect.

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Monitoring properties of the salt-affected soils by multivariate analysis of the visible and near-infrared hyperspectral data

Cited by 32 publications

References 93 publications

Soil Salinity and Sodicity in Drylands: A Review of Causes, Effects, Monitoring, and Restoration Measures

Soil Salinity and Sodicity in Drylands: A Review of Causes, Effects, Monitoring, and Restoration Measures

Capabilities of Hyperspectral Remote Sensing Data to Detect Soil Salinity

Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data

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