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Journal of Applied Sciences
  Year: 2008 | Volume: 8 | Issue: 6 | Page No.: 1000-1006
DOI: 10.3923/jas.2008.1000.1006
 
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Spatial Prediction of Surface Soil Properties Using Terrain and Remote Sensing Data

Jafar Yasrebi, Mahboub Saffari, Hamed Fathi, Mostafa Emadi, Majid Baghernejad, Abdol-Majid Ronaghi and Mehdi Emadi

Abstract:
The main objective of this research is to enhance prediction of soil properties such as Electrical Conductivity (ECe), Exchangeable Sodium Percentage (ESP), available Phosphorus (P), Organic Matter (OM), Total Nitrogen (TN) and pH by making use of the ancillary variables as covariates. Methods that was used for this purpose may be divided into two groups: (i) those that use only a single variable in the prediction process Simple Linear Regression (SLR), Ordinary Kriging (OK)) and (ii) another that make use of additional variables as a part of prediction Simple Kriging with a Locally Varying Mean (SKLVM)). LISS-III data from Indian remoter sensing satellite (IRS-P6) were used as secondary data with SKLVM method. Mean Square Error (MSE) was used to evaluate the performance of the map prediction quality. It was concluded that SKLVM method provided the most accurate predictions based on the summary statistics of prediction errors from cross-validation for mapping OM, pH and ECe. Maps from these kriged estimates showed that a combination of geostatistical techniques and digital data from LISS-III receiver could improve the prediction of quality soil management zones, which is the first step for site-specific soil management.
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How to cite this article:

Jafar Yasrebi, Mahboub Saffari, Hamed Fathi, Mostafa Emadi, Majid Baghernejad, Abdol-Majid Ronaghi and Mehdi Emadi, 2008. Spatial Prediction of Surface Soil Properties Using Terrain and Remote Sensing Data. Journal of Applied Sciences, 8: 1000-1006.

DOI: 10.3923/jas.2008.1000.1006

URL: https://scialert.net/abstract/?doi=jas.2008.1000.1006

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