Mapping soil organic carbon using auxiliary environmental covariates in a typical watershed in the Loess Plateau of China: a comparative study based on three kriging methods and a soil land inference model (SoLIM)

Abstract

Detailed maps of regional spatial distribution of soil organic carbon (SOC) are needed to guide sustainable soil uses and management decisions. Interpolation methods based on spatial auto-correlations, environmental covariates, or hybrid methods are commonly used to predict SOC maps. Many of these methods perform well for gentle terrains. However, it is unknown how these methods perform to capture SOC variations in complex terrains, especially areas of which land uses are interrupted by human activities, such as the Loess Plateau of China. This study compared four interpolations or predictive methods including ordinary kriging (OK), regression kriging, ordinary kriging integrated with land-use type (OK_LU) and a soil land inference model (SoLIM). The purpose of this study is to find appropriate methods, which are suitable to the complex terrain in Loess Plateau region of China. The study area was a typical watershed in Loess Plateau with complex hilly-gully terrain and various land-use types. A field sampling dataset of 200 points was partitioned into 1/2 for model building and 1/2 for accuracy validation in a random way. Nine environmental covariates were selected: land-use types, digital elevation model, solar radiation, slope degree, slope aspect, plan curvature, profile curvature, surface area ratio, and topographic wetness index. The mean absolute percentage error, root mean square error, and goodness-of-prediction statistic value were selected to evaluate mapping results. The results showed that the use of easily obtained environmental covariates, land-use types and terrain variables improved accuracies of SOC interpolation, which will be of interests

Spatial variability analysis of soil quality parameters in a watershed of Sub-Himalayan Landscape - A case study

“…Outlier values at both depths were also identified using the inter quartile range (IQR) relationship. These results are in agreement with the findings of various researchers who reported higher SOC contents at the surface soil in hilly watershed (Wang et al, 2010;Wen et al, 2015), mountainous landscape (Liu et al, 2015), terraced rice fields (Li et al, 2015), erosion affected landscape (Jague et al, 2016), as well as an altitudinal gradient in the mountainous region (Parras-Alcántara et al, 2015). Similar variation of soil organic carbon with depth, has also been reported by Bera et al (2016), under corn production systems with addition of various organic amendments.…”

Spatial distribution of soil chemical properties in an organic farm in Croatia

“…The use of auxiliary variables is advantageous to estimate the variable of interest during the analysis because it allows the analyst to determine if the spatial distribution of a determined variable is dependent upon other(s). These analyses are carried out using hybrid methods, such as co-kriging (Wen et al, 2015). The use of auxiliary variables normally increases the accuracy of the spatial predictions as observed in previous works (Stein and Corsten, 1991;Zhang et al, 1992;Yang et al, 2016b;Ceddia et al, 2015;Chen et al, 2016).…”

Ecosystem Health Assessment at County-Scale Using the Pressure-State-Response Framework on the Loess Plateau, China

“…Additionally, severe soil erosion in this region has resulted in a series of eco-environmental problems (mainly including degradation of ecosystem functions, loss of topsoil and agricultural productivity, food security, pollution and sedimentation of downstream rivers, etc.). Fortunately, many researchers have studied these problems from different aspects, and have made a necessary contribution to the eco-environmental restoration on the Loess Plateau [ 16 , 17 , 18 ]. However, there is very limited literature to study the problems from the point of view of ecosystem health.…”