Assessing soil quality dynamics in northern Iran: a spatial modeling approach to predict and project future trends

The soil in Northern Iran exhibits significant variability, showing notable responses to variations in climatic and environmental conditions. In a representative area covering 936.7 km 2 , we measured 18 soil properties at 73 sampling locations. These properties were then screened using Principal Co...

Full description

Saved in:
Bibliographic Details
Published in:Environmental earth sciences 2024-10, Vol.83 (19), p.576-576, Article 576
Main Authors: Aghalari, Fatemeh, Chavoshi, Elham, Borujeni, Sattar Chavoshi
Format: Article
Language:English
Subjects:
Agriculture
Biogeosciences
Climate change
Correlation analysis
Decision making
Earth and Environmental Science
Earth science
Earth Sciences
Eigenvalues
Environmental conditions
Environmental Science and Engineering
Forest management
Forests
Geochemistry
Geology
Hydrology/Water Resources
Iran
Land surface temperature
MODIS
normalized difference vegetation index
Normalized difference vegetative index
Original Article
Plant cover
principal component analysis
Principal components analysis
Regression analysis
Regression models
soil
Soil analysis
Soil properties
Soil quality
Soil sciences
Surface temperature
Terrestrial Pollution
Trends
Vegetation
Vegetation cover
Vegetation index
Citations: Items that this one cites
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:The soil in Northern Iran exhibits significant variability, showing notable responses to variations in climatic and environmental conditions. In a representative area covering 936.7 km 2 , we measured 18 soil properties at 73 sampling locations. These properties were then screened using Principal Component Analysis (PCA) which identified five PCs with eigenvalues greater than 1.0 and correlation analysis to construct the Nemro Soil Quality Index (SQI) with a mean of 0.27 ± 0.04. The predictability of SQI was modeled using the Generalized Additive Model (R 2  = 0.669, explained deviance = 69.7%), indicating that elevated Normalized Difference Vegetation Index (NDVI; p value = 0.000) and daytime Land Surface Temperature (LST; p value = 0.000) enhance SQI, while higher slopes ( p value = 0.020) have a diminishing effect. The model was also utilized to illustrate potential future alterations in SQI for the year 2040. For this purpose, the 2040 MODIS data (NDVI and LST) were projected using various regression models (0.09 
ISSN:1866-6280
1866-6299
DOI:10.1007/s12665-024-11862-1