Aghalari F, Chavoshi E, Borujeni SC. Indexing and segment-level mapping of soil quality in a spatially complex watershed in northern Iran.
Environ Monit Assess 2023;
196:51. [PMID:
38110732 DOI:
10.1007/s10661-023-12212-7]
[Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/17/2023] [Accepted: 11/30/2023] [Indexed: 12/20/2023]
Abstract
Soil quality (SQ) modeling and mapping is a leading research field aiming to provide reproducible and cost-effective yet accurate SQ predictions at the landscape level. This endeavor was conducted in a complex watershed in northern Iran. We classified the region into spectrally and topographically homogenous land units (average area of 48 ± 23 ha) using object-based segmentation analysis. Following the physicochemical analysis of soil samples from 98 stations, the Nemoro soil quality index (SQIn) was produced using the minimum dataset procedure and a non-linear sigmoid scoring function. SQIn values averaged 0.21 ± 0.06 and differed statistically between major land uses. To predict and map SQIn for each land unit, the best-performing regression model (F(3, 84) = 45.57, p = 0.00, R2 = 0.62) was built based on the positive contribution of the mean Landsat 8-OLI band 5, and negative influence of land surface temperature retrieved from Landsat 8-OLI band 10 and surface slope (t-test p-values < 0.01). Results showed that dense-canopy woodlands located in low-slope land units exhibit higher SQIn while regions characterized by either low-vegetation or steep-sloped land units had SQ deficits. This study provides insights into SQ prediction and mapping across spatially complex large-scale landscapes.
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