Evaluation of the spatial pattern of surface soil water content of a karst hillslope in Southwest China using a state-space approach. Issue 13 (10th November 2017)

• Record Type: Journal Article
• Authors: Wang, Sheng; Chen, Hongsong; Fu, Zhiyong; Wang, Kelin
• Is Part Of: Archives of agronomy and soil science. Volume 63:Issue 13(2017)
• Page Start: 1800
• Page End: 1813
• Publication Date: 2017-11-10
• Subjects: Spatial heterogeneity -- state-space models -- soil texture -- regression analysis -- vegetation restoration; Horticulture -- Periodicals; Soils -- Periodicals; 630.5
• DOI: 10.1080/03650340.2017.1307507
• Languages: English
• ISSNs: 0365-0340

Abstract

ABSTRACT: This study aims to evaluate the effects of soil physicochemical properties and environmental factors on the spatial patterns of surface soil water content (SWC) based on the state-space approach and linear regression analysis. For this purpose, based on a grid sampling scheme (10 m × 10 m) applied to a 90 m × 120 m plot located on a karst hillslope of Southwest China, the SWC at 0–16 cm depth was measured 3 times across 130 sampling points, and soil texture, bulk density (BD), saturated hydraulic conductivity ( Ks ), organic carbon (SOC), and rock fragment content as well as site elevation (SE) were also measured at these locations. Results showed that the distribution pattern of SWC could be more successfully predicted by the first-order state-space models ( R 2 = 67.5–99.9% and RMSE = 0.01–0.14) than the classic linear regression models ( R 2 = 10.8–79.3% and RMSE = 0.11–0.24). The input combination containing silt content (Silt), Ks, and SOC produced the best state-space model, explaining 99.9% of the variation in SWC. And Silt was identified as the first-order controlling factor that explained 98.7% of the variation. In contrast, the best linear regression model using all of the variables only explained 79.3% of variation.