کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
8876326 | 1623724 | 2018 | 13 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Comparison of GIS-based interpolation methods for spatial distribution of soil organic carbon (SOC)
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کلمات کلیدی
موضوعات مرتبط
علوم زیستی و بیوفناوری
علوم کشاورزی و بیولوژیک
علوم کشاورزی و بیولوژیک (عمومی)
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
The ecological, economical, and agricultural benefits of accurate interpolation of spatial distribution patterns of soil organic carbon (SOC) are well recognized. In the present study, different interpolation techniques in a geographical information system (GIS) environment are analyzed and compared for estimating the spatial variation of SOC at three different soil depths (0-20Â cm, 20-40Â cm and 40-100Â cm) in Medinipur Block, West Bengal, India. Stratified random samples of total 98 soils were collected from different landuse sites including agriculture, scrubland, forest, grassland, and fallow land of the study area. A portable global positioning system (GPS) was used to collect coordinates of each sample site. Five interpolation methods such as inverse distance weighting (IDW), local polynomial interpolation (LPI), radial basis function (RBF), ordinary kriging (OK) and Empirical Bayes kriging (EBK) are used to generate spatial distribution of SOC. SOC is concentrated in forest land and less SOC is observed in bare land. The cross validation is applied to evaluate the accuracy of interpolation methods through coefficient of determination (R2) and root mean square error (RMSE). The results indicate that OK is superior method with the least RMSE and highest R2 value for interpolation of SOC spatial distribution.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Journal of the Saudi Society of Agricultural Sciences - Volume 17, Issue 2, April 2018, Pages 114-126
Journal: Journal of the Saudi Society of Agricultural Sciences - Volume 17, Issue 2, April 2018, Pages 114-126
نویسندگان
Gouri Sankar Bhunia, Pravat Kumar Shit, Ramkrishna Maiti,