Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam

Nguyen, Kim Anh; Liou, Yuei An; Tran, Ha Phuong; Hoang, Phi Phung; Nguyễn, Thành Hưng

doi:10.1186/s40645-019-0311-0

Cited by 105 publications

(56 citation statements)

References 57 publications

(50 reference statements)

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“…Knowledge of land-use/land-cover (LULC) change is essential in a number of fields based on the use of Earth observations, such as urban and regional planning [1,2], environmental vulnerability and impact assessment [3][4][5][6][7], natural disasters and hazards monitoring [8][9][10][11][12][13] and estimation of soil erosion and salinity, etc. [14][15][16][17]. Quantitative assessment and prediction of LULC dynamics are the most efficient means to manage and understand the landscape transformation [18].…”

Section: Introductionmentioning

confidence: 99%

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

et al. 2020

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Rapid and uncontrolled population growth along with economic and industrial development, especially in developing countries during the late twentieth and early twenty-first centuries, have increased the rate of land-use/land-cover (LULC) change many times. Since quantitative assessment of changes in LULC is one of the most efficient means to understand and manage the land transformation, there is a need to examine the accuracy of different algorithms for LULC mapping in order to identify the best classifier for further applications of earth observations. In this article, six machine-learning algorithms, namely random forest (RF), support vector machine (SVM), artificial neural network (ANN), fuzzy adaptive resonance theory-supervised predictive mapping (Fuzzy ARTMAP), spectral angle mapper (SAM) and Mahalanobis distance (MD) were examined. Accuracy assessment was performed by using Kappa coefficient, receiver operational curve (RoC), index-based validation and root mean square error (RMSE). Results of Kappa coefficient show that all the classifiers have a similar accuracy level with minor variation, but the RF algorithm has the highest accuracy of 0.89 and the MD algorithm (parametric classifier) has the least accuracy of 0.82. In addition, the index-based LULC and visual cross-validation show that the RF algorithm (correlations between RF and normalised differentiation water index, normalised differentiation vegetation index and normalised differentiation built-up index are 0.96, 0.99 and 1, respectively, at 0.05 level of significance) has the highest accuracy level in comparison to the other classifiers adopted. Findings from the literature also proved that ANN and RF algorithms are the best LULC classifiers, although a non-parametric classifier like SAM (Kappa coefficient 0.84; area under curve (AUC) 0.85) has a better and consistent accuracy level than the other machine-learning algorithms. Finally, this review concludes that the RF algorithm is the best machine-learning LULC classifier, among the six examined algorithms although it is necessary to further test the RF algorithm in different morphoclimatic conditions in the future.

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Section: Introductionmentioning

confidence: 99%

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

et al. 2020

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“…The relationship is more likely due to the soil moisture, surface roughness and organic carbon content [38,46]. Further, high-salinity soil samples with lower reflectance in the NIR were reported in the Mekong delta by Nguyen, Liou, Tran, Hoang and Nguyen [34], where the hot and dry climate of the study areas was attributed to the lower reflectance trend.…”

Section: Discussionmentioning

confidence: 81%

“…The accuracy of the model's ability to estimate soil salinity is limited by other soil properties, such as soil moisture, soil clay content, soil organic content and vegetation [56]. We observed during the soil sample collection period that most of the paddy lands were ploughed with organic matter, which could have influenced the actual salt content prediction of the soil [34]. Further, another limitation of soil mapping also comes with the uncertainty of the relationship between the salt content of the soil and remote sensing data.…”

Section: Discussionmentioning

confidence: 94%

“…The common methods for soil salinity detection involve using direct soil reflectance (salinity indices) [18,20] either alone or along with vegetation reflectance (vegetation indices) in regions of thickly vegetated soils [34,35]. However, when vegetation is not present, the different levels of salt in the soils result in different levels of reflectance, and these differences in soil reflectance could be used in determining levels of salinity.…”

Section: Selection Of the Optimal Spectral Index For Estimating Soil mentioning

confidence: 99%

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Modeling and Mapping of Soil Salinity and its Impact on Paddy Lands in Jaffna Peninsula, Sri Lanka

Gopalakrishnan

Kumar

2020

Sustainability

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Soil salinity is a major threat to land productivity, water resources and agriculture in coastal areas and arid and semi-arid regions of the world. This has a significantly negative effect on the land and causes desertification. Monitoring salt accumulation in the soil is crucial for the prevention of land degradation in such environments. This study attempted to estimate and map soil salinity in Jaffna Peninsula, a semi-arid region of Sri Lanka. A Partial Least Squares Regression (PLSR) model was constructed using Sentinel 2A satellite imagery and field-measured soil electrical conductivity (EC) values. The results showed that satisfactory prediction of the soil salinity could be made based on the PLSR model coupled with Sentinel 2A satellite imagery (R2 = 0.69, RMSE = 0.4830). Overall, 32.8% of the land and 45% of paddy lands in Jaffna Peninsula are affected by salt. The findings of this study indicate that PLSR is suitable for the soil salinity mapping, especially in semi-arid regions like Jaffna Peninsula. The results underpin the importance of building adaptive capacity and implementing suitable preventive strategies for sustainable land and agricultural management.

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“…The Normalized Difference Water Index (NDWI) is sensitive to changes in plant water content [42]. The Normalized Difference Salinity Index (NDSI) highlights soil salinity levels [44,45], which can aid in identifying saltcedar since these plants excrete salt through their leaves, which then drop to the ground and salinate the soil. The Soil Adjusted Vegetation Index (SAVI) is similar to NDVI but performs better in areas with sparse vegetation coverage.…”

Section: Vegetation Indicesmentioning

confidence: 99%

An Integrated Spectral–Structural Workflow for Invasive Vegetation Mapping in an Arid Region Using Drones

Kedia

Kapos

Liao

et al. 2021

Drones

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Mapping invasive vegetation species in arid regions is a critical task for managing water resources and understanding threats to ecosystem services. Traditional remote sensing platforms, such as Landsat and MODIS, are ill-suited for distinguishing native and non-native vegetation species in arid regions due to their large pixels compared to plant sizes. Unmanned aircraft systems, or UAS, offer the potential to capture the high spatial resolution imagery needed to differentiate species. However, in order to extract the most benefits from these platforms, there is a need to develop more efficient and effective workflows. This paper presents an integrated spectral–structural workflow for classifying invasive vegetation species in the Lower Salt River region of Arizona, which has been the site of fires and flooding, leading to a proliferation of invasive vegetation species. Visible (RGB) and multispectral images were captured and processed following a typical structure from motion workflow, and the derived datasets were used as inputs in two machine learning classifications—one incorporating only spectral information and one utilizing both spectral data and structural layers (e.g., digital terrain model (DTM) and canopy height model (CHM)). Results show that including structural layers in the classification improved overall accuracy from 80% to 93% compared to the spectral-only model. The most important features for classification were the CHM and DTM, with the blue band and two spectral indices (normalized difference water index (NDWI) and normalized difference salinity index (NDSI)) contributing important spectral information to both models.

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Soil salinity assessment by using near-infrared channel and Vegetation Soil Salinity Index derived from Landsat 8 OLI data: a case study in the Tra Vinh Province, Mekong Delta, Vietnam

Cited by 105 publications

References 57 publications

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations—A Review

Modeling and Mapping of Soil Salinity and its Impact on Paddy Lands in Jaffna Peninsula, Sri Lanka

An Integrated Spectral–Structural Workflow for Invasive Vegetation Mapping in an Arid Region Using Drones

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