Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

Bai, Lin; Wang, Cuizhen; Zang, Shuying; Wu, Changshan; Luo, Jinming; Wu, Yuexiang

doi:10.3390/s18113855

Cited by 31 publications

(15 citation statements)

References 47 publications

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“…This finding is consistent with that of Nawar, Buddenbaum, Hill and Kozak [38], Li, Ren, Zhao and Liang [39], Abuelgasim and Ammad [40]. 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: Discussionsupporting

confidence: 88%

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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Section: Discussionsupporting

confidence: 88%

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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“…Additionally, there are many salt-tolerant plants, so that the NDVI is not smaller in some areas with more severe salinization in the Yellow River Delta (Weng et al 2010; Guo et al 2019a). Ferric oxide is a primary dyeing material in the soil that can greatly affect the spectral characteristics of the soil (Bai et al 2018;Yu et al 2018). Many absorption characteristics of salinized soil in the visible light band are caused by iron oxides, and the presence of iron oxides will lead to a decrease in reflectivity of soil in the whole visible band range (Douaoui et al 2006;Peng et al 2013).…”

Section: Resultsmentioning

confidence: 99%

Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image

Guo

Zang

Luo

et al. 2020

Geomatics, Natural Hazards and Risk

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The Yellow River Delta, with the most typical new wetland system in warm temperate zone of China, is suffering from increasingly serious salinization. The purpose of this study is to utilize five typical surface parameters, including Albedo (the surface Albedo), NDVI (vegetation index), SI (salinity index),WI (humidity index), and I Fe2O3 (Iron oxide index), to construct 10 different feature spaces and, then, propose two different kinds of monitoring models (point-to-point model and point to line model) of soil salinization. The results showed that the inversion accuracy of the I Fe2O3 feature space detection index based on the pointto-point model was the highest with R 2 ¼0.86. However, the inversion accuracy of Albedo-NDVI feature space detection index based on the point-to-point model is the lowest with R 2 ¼0.72. This is due to the fact that NDVI is not sensitive enough to indicate the status of vegetation grown in the region with low (disturbance of soil background) and high (influenced by the saturation effect) vegetation coverage. The chemical weathering is also a primary cause of soil salinization, during which Fe 2 O 3 is formed by the reaction of oxygen present in the atmosphere with primary Fe 2þ minerals in the soil .Therefore, the AlbedoÀI Fe2O3 feature space detection index based on the point-to-point model has a stronger applicability to monitor the information of soil salinization in the Yellow River Delta. This above point-to-point detection model can be utilized as a better approach to provide data and decision support for the development of agriculture, construction of reservoirs, and protection of natural ecological system in the Yellow River Delta.

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“…Additionally, the driving factors and formation mechanism of soil salinization must be elucidated to prevent this problem [61,62]. Furthermore, the universality of the time and space of monitoring should be expanded [63,64].…”

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confidence: 99%

A Harmonious Satellite-Unmanned Aerial Vehicle-Ground Measurement Inversion Method for Monitoring Salinity in Coastal Saline Soil

Zhang¹,

Zhao²

2019

Remote Sensing

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Soil salinization adversely impacts crop growth and production, especially in coastal areas which experience serious soil salinization. Therefore, rapid and accurate monitoring of the salinity and distribution of coastal saline soil is crucial. Representative areas of the Yellow River Delta (YRD)—the Hekou District (the core test area with 140 sampling points) and the Kenli District (the verification area with 69 sampling points)—were investigated. Ground measurement data, unmanned aerial vehicle (UAV) multispectral imagery and Sentinel-2A multispectral imagery were used as the data sources and a satellite-UAV-ground integrated inversion of the coastal soil salinity was performed. Correlation analyses and multiple regression methods were used to construct an accurate model. Then, a UAV-based inversion model was applied to the satellite imagery with reflectance normalization. Finally, the spatial and temporal universality of the UAV-based inversion model was verified and the soil salinity inversion results were obtained. The results showed that the green, red, red-edge and near-infrared bands were significantly correlated with soil salinity and the spectral parameters significantly improved this correlation; hence, the model is more effective upon combining spectral parameters with sensitive bands, with modeling precision and verification precision of the best model being 0.743 and 0.809, respectively. The reflectance normalization yielded good results. These findings proved that applying the UAV-based model to reflectance normalized Sentinel-2A images produces results that are consistent with the actual situation. Moreover, the inversion results effectively reflect the distributions characteristic of the soil salinity in the core test area and the study area. This study integrated the advantages of satellite, UAV and ground methods and then proposed a method for the inversion of the salinity of coastal saline soils at different scales, which is of great value for real-time, rapid and accurate soil salinity monitoring applications.

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Mapping Soil Alkalinity and Salinity in Northern Songnen Plain, China with the HJ-1 Hyperspectral Imager Data and Partial Least Squares Regression

Cited by 31 publications

References 47 publications

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

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

Detection model of soil salinization information in the Yellow River Delta based on feature space models with typical surface parameters derived from Landsat8 OLI image

A Harmonious Satellite-Unmanned Aerial Vehicle-Ground Measurement Inversion Method for Monitoring Salinity in Coastal Saline Soil

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