scholarly journals Capability of Sentinel-2 MSI data for monitoring and mapping of soil salinity in dry and wet seasons in the Ebinur Lake region, Xinjiang, China

Geoderma ◽  
2019 ◽  
Vol 353 ◽  
pp. 172-187 ◽  
Author(s):  
Jingzhe Wang ◽  
Jianli Ding ◽  
Danlin Yu ◽  
Xuankai Ma ◽  
Zipeng Zhang ◽  
...  
Keyword(s):  
Soil Salinity ◽  
Lake Region ◽  
Ebinur Lake ◽  
Sentinel 2

Impact of Saline Dust on the Physiological Properties of Plants in the Ebinur Lake Region

2010 ◽  
Vol 26 (4) ◽  
pp. 533-542 ◽  
Author(s):  
Tuerhong Tuerxun· ◽  
Abuduwaili Jilili· ◽  
Yilahong Aikebaier· ◽  
Dong-wei LIU
Keyword(s):  
Lake Region ◽  
Ebinur Lake ◽  
Physiological Properties

Evaluation of land desertification from 1990 to 2010 and its causes in Ebinur Lake region, Xinjiang China

2014 ◽  
Vol 73 (9) ◽  
pp. 5731-5745 ◽  
Author(s):  
Fei Zhang ◽  
Tashpolat Tiyip ◽  
Verner C. Johnson ◽  
Hsiangte Kung ◽  
Jianli Ding ◽  
...  
Keyword(s):  
Lake Region ◽  
Ebinur Lake ◽  
Land Desertification

scholarly journals A PLSR model to predict soil salinity using Sentinel-2 MSI data

2021 ◽  
Vol 13 (1) ◽  
pp. 977-987
Author(s):  
Ghada Sahbeni
Keyword(s):  
Soil Salinity ◽  
Spectral Characteristics ◽  
Management Strategies ◽  
Salt Content ◽  
Soil Surface ◽  
Coefficient Of Determination ◽  
Least Squares Regression ◽  
Remotely Sensed Data ◽  
Sampling Campaign ◽  
Sentinel 2

Abstract Salinization is one of the most widespread environmental threats in arid and semi-arid regions that occur either naturally or artificially within the soil. When exceeding the thresholds, salinity becomes a severe danger, damaging agricultural production, water and soil quality, biodiversity, and infrastructures. This study used spectral indices, including salinity and vegetation indices, Sentinel-2 MSI original bands, and DEM, to model soil salinity in the Great Hungarian Plain. Eighty-one soil samples in the upper 30 cm of the soil surface were collected from vegetated and nonvegetated areas by the Research Institute for Soil Sciences and Agricultural Chemistry (RISSAC). The sampling campaign of salinity monitoring was performed in the dry season to enhance salt spectral characteristics during its accumulation in the subsoil. Hence, applying a partial least squares regression (PLSR) between salt content (g/kg) and remotely sensed data manifested a highly moderate correlation with a coefficient of determination R 2 of 0.68, a p-value of 0.000017, and a root mean square error of 0.22. The final model can be deployed to highlight soil salinity levels in the study area and assist in understanding the efficacy of land management strategies.

scholarly journals Retrieval of soil salinity from Sentinel-2 multispectral imagery

2019 ◽  
Vol 52 (1) ◽  
pp. 138-154 ◽  
Author(s):  
Mohammad Mahdi Taghadosi ◽  
Mahdi Hasanlou ◽  
Kamran Eftekhari
Keyword(s):  
Soil Salinity ◽  
Multispectral Imagery ◽  
Sentinel 2

Performance of random forest and buffer analysis of Sentinel-2 data for modelling soil salinity in the Lower-Cheliff plain (Algeria)

2020 ◽  
Vol 42 (1) ◽  
pp. 148-171
Author(s):  
Ibrahim Yahiaoui ◽  
Abdelhamid Bradaï ◽  
Abdelkader Douaoui ◽  
Mohamed Amine Abdennour
Keyword(s):  
Random Forest ◽  
Soil Salinity ◽  
Buffer Analysis ◽  
Sentinel 2

scholarly journals Quantitative estimation of soil salinity by means of different modeling methods and visible-near infrared (VIS–NIR) spectroscopy, Ebinur Lake Wetland, Northwest China

PeerJ ◽  
2018 ◽  
Vol 6 ◽  
pp. e4703 ◽  
Author(s):  
Jingzhe Wang ◽  
Jianli Ding ◽  
Aerzuna Abulimiti ◽  
Lianghong Cai
Keyword(s):  
Fractional Derivative ◽  
Soil Salinity ◽  
Spectral Reflectance ◽  
Near Infrared ◽  
Quantitative Estimation ◽  
Spectral Characteristics ◽  
Nir Spectroscopy ◽  
Soil Parameters ◽  
Modeling Methods ◽  
Ebinur Lake

Soil salinization is one of the most common forms of land degradation. The detection and assessment of soil salinity is critical for the prevention of environmental deterioration especially in arid and semi-arid areas. This study introduced the fractional derivative in the pretreatment of visible and near infrared (VIS–NIR) spectroscopy. The soil samples (n = 400) collected from the Ebinur Lake Wetland, Xinjiang Uyghur Autonomous Region (XUAR), China, were used as the dataset. After measuring the spectral reflectance and salinity in the laboratory, the raw spectral reflectance was preprocessed by means of the absorbance and the fractional derivative order in the range of 0.0–2.0 order with an interval of 0.1. Two different modeling methods, namely, partial least squares regression (PLSR) and random forest (RF) with preprocessed reflectance were used for quantifying soil salinity. The results showed that more spectral characteristics were refined for the spectrum reflectance treated via fractional derivative. The validation accuracies showed that RF models performed better than those of PLSR. The most effective model was established based on RF with the 1.5 order derivative of absorbance with the optimal values of R2 (0.93), RMSE (4.57 dS m−1), and RPD (2.78 ≥ 2.50). The developed RF model was stable and accurate in the application of spectral reflectance for determining the soil salinity of the Ebinur Lake wetland. The pretreatment of fractional derivative could be useful for monitoring multiple soil parameters with higher accuracy, which could effectively help to analyze the soil salinity.

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