Journal of Range and Watershed Managment

Estimating Topsoil Salinity from LANDST Data: A Comparison between Classic and Spatial Statistics

Document Type : Research Paper

Authors

Ali Akbar Noroozi 1
Mahdi Homaee 2
Abbas Farshad 3

1 PhD Graduated student, Department of Soil Science, Tarbiat Modares University; Tehran, Iran

2 Professor, Department of Soil Science, Tarbiat Modares University; Tehran, Iran

3 Assistant Professor at ITC, Twente University, Enschede, Netherlands

https://doi.org/10.22059/jrwm.2014.50034
Abstract
Soil salinity is a limiting factor for plant growth and a serious cause of land degradation. Field sampling and statistical analysis for estimating soil salinity is expensive and time consuming. Estimating soil salinity by spatial statistical models and Geographic Information System (GIS) is recommended, because it saves labor and time. This study was conducted to evaluate the performance of spatial statistics with ordinary least square (OLS) incorporation with LANDSAT data to predict soil salinity. The electrical conductivity (EC) of 236 soil samples were collected from Garmsar plain at south east Tehran, Iran and were measured and correlated to 27 variables derived from LANDSAT images, including vegetation indices, salinity indices, bands 1 to 7, principal component analysis and tasseled cap indices. Using factor analysis and similarity index, these variables were divided into three components. Furthermore, two models for soil salinity estimation were derived, using the best correlation correlation coefficient (0.58 and 0.60) method. Simultaneously, soil salinity map was produced in ArcGIS by spatial statistics model ordinary least square (OLS) followed by derivation of the error map, calculated using Moran's index. The error map indicated that the spatial statistics models are superior to classic statistics methods, due to high accuracy in estimation and the fact that it doesn't require exchange information between different software programs

Keywords

Ordinary Least Square (OLS)
Morans' Index
Classic Statistics
spatial statistics
Journal of Range and Watershed Managment
Volume 66, Issue 4 - Serial Number 4
Winter 2014
Pages 609-620

  • Receive Date 30 August 2011
  • Revise Date 11 January 2014
  • Accept Date 30 January 2013
  • Publish Date 20 February 2014

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