A Quantifying Approach to Soil Salinity Based on a Radar Feature Space Model Using ALOS PALSAR-2 Data

Muhetaer, Nuerbiye; Nurmemet, Ilyas; Abulaiti, Adilai; Xiao, Sentian; Jing, Zhao

doi:10.3390/rs14020363

Cited by 15 publications

(11 citation statements)

References 68 publications

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“…The SNR of each polarization decomposition feature component was calculated, and then the SNR values of the three components of the same polarization decomposition were compared. Since the higher the image SNR, the better the image quality and de-noising effect [ 83 ], the components with the largest SNR were selected. The feature component with the highest SNR was selected from the three feature components that were extracted by each of the polarization decomposition methods, and seven feature components obtained by the seven polarization decomposition methods were selected, including Pauli_surf_b, Freeman_vol_g, Freeman Durden_vol_g, Cloude_surf_b, Sinclair_vol_g, VanZyl_vol_g, and Yamaguchi_dbl_r.…”

Section: Methodsmentioning

confidence: 99%

An Efficient Approach for Inverting the Soil Salinity in Keriya Oasis, Northwestern China, Based on the Optical-Radar Feature-Space Model

Muhetaer

Nurmemet

Abulaiti

et al. 2022

Sensors

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Soil salinity has been a major factor affecting agricultural production in the Keriya Oasis. It has a destructive effect on soil fertility and could destroy the soil structure of local land. Therefore, the timely monitoring of salt-affected areas is crucial to prevent land degradation and sustainable soil management. In this study, a typical salinized area in the Keriya Oasis was selected as a study area. Using Landsat 8 OLI optical data and ALOS PALSAR-2 SAR data, the optical remote sensing indexes NDVI, SAVI, NDSI, SI, were combined with the optimal radar polarized target decomposition feature component (VanZyl_vol_g) on the basis of feature space theory in order to construct an optical-radar two-dimensional feature space. The optical-radar salinity detection index (ORSDI) model was constructed to inverse the distribution of soil salinity in Keriya Oasis. The prediction ability of the ORSDI model was validated by a test on 40 measured salinity values. The test results show that the ORSDI model is highly correlated with soil surface salinity. The index ORSDI3 (R2 = 0.656) shows the highest correlation, and it is followed by indexes ORSDI1 (R2 = 0.642), ORSDI4 (R2 = 0.628), and ORSDI2 (R2 = 0.631). The results demonstrated the potential of the ORSDI model in the inversion of soil salinization in arid and semi-arid areas.

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Section: Methodsmentioning

confidence: 99%

An Efficient Approach for Inverting the Soil Salinity in Keriya Oasis, Northwestern China, Based on the Optical-Radar Feature-Space Model

Muhetaer

Nurmemet

Abulaiti

et al. 2022

Sensors

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“…When random noise is added, the accuracy of OOB data decreases significantly, that is, errOOB2 increases, indicating that this feature has a certain impact on the prediction results of the sample; thus, its importance is relatively high. In this work, to remove the redundancy and to reduce random interference between features, the importance of each feature was calculated by Equation (10), and calculated importance values were arranged in descending order. After the deletion ratio was determined, the features with relatively low importance were deleted from the original feature set, and a new feature subset was obtained.…”

Section: Machine Learning Algorithms Used Feature Selection From Pals...mentioning

confidence: 99%

“…China has been one of the countries with severe salinization problems [6]. China alone is covered by 10% of the world's salinized land area [7,8], and Xinjiang is one of the largest saline soil distribution areas [9], accounting for 36.8% of the country's saline-alkali land area, mainly distributed in the oasis-desert ecosystem in southern Xinjiang (nearly 50%) [10]. Therefore, realizing large-scale, high-precision soil salinization monitoring and scientific prediction of salinization risks and hazards are fundamental to alleviating environmental pressure.…”

Section: Introductionmentioning

confidence: 99%

Monitoring of Soil Salinization in the Keriya Oasis Based on Deep Learning with PALSAR-2 and Landsat-8 Datasets

et al. 2022

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Currently, soil salinization is one of the main forms of land degradation and desertification. Soil salinization not only seriously restricts the development of agriculture and the economy, but also poses a threat to the ecological environment. The main purpose of this study is to map soil salinity in Keriya Oasis, northwestern China using the PALSAR-2 fully polarized synthetic aperture radar (PolSAR) L-band data and Landsat8-OLI (OLI) optical data combined with deep learning (DL) methods. A field survey is conducted, and soil samples are collected from 20 April 2015 to 1 May 2015. To mine the hidden information in the PALSAR-2 data, multiple polarimetric decomposition methods are implemented, and a wide range of polarimetric parameters and synthetic aperture radar discriminators are derived. The radar vegetation index (RVI) is calculated using PALSAR-2 data, while the normalized difference vegetation index (NDVI) and salinity index (SI) are calculated using OLI data. The random forest (RF)-integrated learning algorithm is used to select the optimal feature subset composed of eight polarimetric elements. The RF, support vector machine, and DL methods are used to extract different degrees of salinized soil. The results show that the OLI+PALSAR-2 image classification result of the DL classification was relatively good, having the highest overall accuracy of 91.86% and a kappa coefficient of 0.90. This method is helpful to understand and monitor the spatial distribution of soil salinity more effectively to achieve sustainable agricultural development and ecological stability.

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“…More than 3% of the world's soil resources are affected by salinity [1]. It is estimated that by 2050, more than 50% of the world's arable land will be affected by salinization [2,3], posing a serious environmental problem that threatens the development of sustainable agriculture, forestry, and future food security. Therefore, the efficient utilization of saline soil has become an urgent issue to be resolved [4].…”

Section: Introductionmentioning

confidence: 99%

Effects of Myo-Inositol on NaCl Stress in Tamarix ramosissima: Insights from Transcriptomics and Metabolomics

Li,

Fan,

Zhang

et al. 2023

Forests

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NaCl stress adversely affects plant growth. Tamarix ramosissima Ledeb (T. ramosissima), a halophyte, thrives in saline-alkali areas. Myo-inositol, a lipid-soluble compound, is crucial for stress response, but its role in mitigating NaCl damage remains underexplored. We analyzed transcriptome sequencing and metabolites in T. ramosissima roots under NaCl stress at various intervals (0 h, 48 h, and 168 h). We identified ten Myo-inositol oxygenase-related genes. Nine of these genes, linked to metabolic pathways involving Myo-inositol, showed differential expression. Myo-inositol accumulation increased over time, suggesting its role as an osmotic regulator and reactive oxygen species (ROS) scavenger. This accumulation likely shields T. ramosissima from NaCl-induced osmotic and oxidative damage. Notably, Unigene0002140 and Unigene0095980, associated with Myo-inositol oxygenase, appear to regulate Myo-inositol accumulation and correlate significantly with its levels. We hypothesize they are key genes in controlling Myo-inositol levels, warranting further study. This research illuminates the role of Myo-inositol oxygenase-related genes in T. ramosissima roots combating NaCl stress, offering insights for selecting salt-tolerant tree species.

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A Quantifying Approach to Soil Salinity Based on a Radar Feature Space Model Using ALOS PALSAR-2 Data

Cited by 15 publications

References 68 publications

An Efficient Approach for Inverting the Soil Salinity in Keriya Oasis, Northwestern China, Based on the Optical-Radar Feature-Space Model

An Efficient Approach for Inverting the Soil Salinity in Keriya Oasis, Northwestern China, Based on the Optical-Radar Feature-Space Model

Monitoring of Soil Salinization in the Keriya Oasis Based on Deep Learning with PALSAR-2 and Landsat-8 Datasets

Effects of Myo-Inositol on NaCl Stress in Tamarix ramosissima: Insights from Transcriptomics and Metabolomics

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