<abstract xmlns="http://www.w3.org/1999/xhtml"><p>Salt accumulation within the soil is one of the subtle ecological issues around the world. An integrated of remote sensing with different statistical techniques has indicated accomplishment for creating soil quality forecasting models. The objective of this research was to unveil the degree and location of the salt affected soils as it has a severe effect on the agricultural crop yield of the Gautam Buddha Nagar (GBN) district. To assess spatial variation of the salt-affected soil a simulation model integrating satellite observation data, artificial neural network (ANN) and multiple linear regression (MLR) was used. The statistical correlation amongst ground-truth data and Landsat original bands and band ratios showed that all the bands and ratios showed a non-significant correlation with SAR. While four optical bands and eleven band ratios showed high correlation with all the soil quality parameters. Combining all the remotely sensed variables into models resulted in the finest fit with the R<sup>2</sup> value equal to 0.84, 0.69, 0.59 and 0.85 for EC, pH, ESP and TSS, respectively. The soil quality parameter maps generated using selected models revealed that most of the part of the agricultural land of the study area lies in the range of moderately saline and moderately sodic soil. Further Analytical Hierarchy Process (AHP) was applied to generate overall soil degradation probability map of the district, with respect to salt accumulation. The result revealed that the major portion of the entire agricultural field of the study area lie between low (32.74 %) to moderate (29.53 %) probability zones of salt susceptibility.</p></abstract>

Salt accumulation within the soil is one of the subtle ecological issues around the world. An integrated of remote sensing with different statistical techniques has indicated accomplishment for creating soil quality forecasting models. The objective of this research was to unveil the degree and location of the salt affected soils as it has a severe effect on the agricultural crop yield of the Gautam Buddha Nagar (GBN) district. To assess spatial variation of the salt-affected soil a simulation model integrating satellite observation data, artificial neural network (ANN) and multiple linear regression (MLR) was used. The statistical correlation amongst ground-truth data and Landsat original bands and band ratios showed that all the bands and ratios showed a non-significant correlation with SAR. While four optical bands and eleven band ratios showed high correlation with all the soil quality parameters. Combining all the remotely sensed variables into models resulted in the finest fit with the R2 value equal to 0.84, 0.69, 0.59 and 0.85 for EC, pH, ESP and TSS, respectively. The soil quality parameter maps generated using selected models revealed that most of the part of the agricultural land of the study area lies in the range of moderately saline and moderately sodic soil. Further Analytical Hierarchy Process (AHP) was applied to generate overall soil degradation probability map of the district, with respect to salt accumulation. The result revealed that the major portion of the entire agricultural field of the study area lie between low (32.74 %) to moderate (29.53 %) probability zones of salt susceptibility.

Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators

Department of Civil, Geo and Environmental Engineering

Amity School of Natural Resources and Sustainable Development

Department of Civil Engineering, Amity School of Engineering and Technology

Journal of Landscape Ecology

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Salt accumulation within the soil is one of the subtle ecological issues around the world. An integrated of remote sensing with different statistical...

Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators

Publicado en línea: 26 may 2020

Páginas: 61 - 84

Recibido: 09 ene 2020

Aceptado: 02 abr 2020

DOI: https://doi.org/10.2478/jlecol-2020-0005

Palabras clave
Soil quality parameters, Landsat OLI & TIRS, Correlation, Artificial Neural Network (ANN), Multiple Linear Regression (MLR), Analytical Hierarchy Process (AHP) and Weighted Index Overlay (WIO)

© 2020 Shivangi S. Somvanshi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Unveiling Spatial Variation in Salt Affected Soil of Gautam Buddha Nagar District Based on Remote Sensing Indicators

Publicado en línea: 26 may 2020

Páginas: 61 - 84

Recibido: 09 ene 2020

Aceptado: 02 abr 2020

DOI: https://doi.org/10.2478/jlecol-2020-0005

Palabras claveSoil quality parameters, Landsat OLI & TIRS, Correlation, Artificial Neural Network (ANN), Multiple Linear Regression (MLR), Analytical Hierarchy Process (AHP) and Weighted Index Overlay (WIO)

© 2020 Shivangi S. Somvanshi et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Palabras clave
Soil quality parameters, Landsat OLI & TIRS, Correlation, Artificial Neural Network (ANN), Multiple Linear Regression (MLR), Analytical Hierarchy Process (AHP) and Weighted Index Overlay (WIO)