Spatial Distribution of Soil Organic Carbon and Total Nitrogen Based on GIS and Geostatistics in a Small Watershed in a Hilly Area of Northern China

Abstract

The spatial variability of soil organic carbon (SOC) and total nitrogen (STN) levels is important in both global carbon-nitrogen cycle and climate change research. There has been little research on the spatial distribution of SOC and STN at the watershed scale based on geographic information systems (GIS) and geostatistics. Ninety-seven soil samples taken at depths of 0–20 cm were collected during October 2010 and 2011 from the Matiyu small watershed (4.2 km2) of a hilly area in Shandong Province, northern China. The impacts of different land use types, elevation, vegetation coverage and other factors on SOC and STN spatial distributions were examined using GIS and a geostatistical method, regression-kriging. The results show that the concentration variations of SOC and STN in the Matiyu small watershed were moderate variation based on the mean, median, minimum and maximum, and the coefficients of variation (CV). Residual values of SOC and STN had moderate spatial autocorrelations, and the Nugget/Sill were 0.2% and 0.1%, respectively. Distribution maps of regression-kriging revealed that both SOC and STN concentrations in the Matiyu watershed decreased from southeast to northwest. This result was similar to the watershed DEM trend and significantly correlated with land use type, elevation and aspect. SOC and STN predictions with the regression-kriging method were more accurate than those obtained using ordinary kriging. This research indicates that geostatistical characteristics of SOC and STN concentrations in the watershed were closely related to both land-use type and spatial topographic structure and that regression-kriging is suitable for investigating the spatial distributions of SOC and STN in the complex topography of the watershed.

Application of Ordinary Kriging and Regression Kriging Method for Soil Properties Mapping in Hilly Region of Central Vietnam

“…We used R I to compare the OK and RK methods and to improve the prediction accuracy index. If R I is positive, the accuracy prediction of RK is higher than that of OK and vice-versa [16].…”

“…There are various methods for interpolation of spatial distribution of SOC, TN and soil pH, such as inverse distance weighting (IDW) and ordinary kriging (OK) [9][10][11][12][13]. In recent years, researchers have suggested a combination of regression and spatial interpolation, called regression kriging (RK), to determine the spatial distribution of soil characteristics [14][15][16][17][18][19][20][21]. For this method, the selection of auxiliary variables is essential and remotely sensed images are typically the first choice [22].…”

“…For soil characteristic mapping based on environmental variables, researchers [27] often use terrain characteristics as independent variables [19,27,28]. Some researchers use the topographic wetness index (TWI), a local scale index to quantitatively indicate the balance between water accumulation and drainage conditions, as an environmental variable for SOC mapping [29][30][31] or TN mapping [16,27].…”

Recreation and terrain effect on the spatial variation of the apparent soil electrical conductivity in an urban park

“…The efficiency of predicting spatial variability of soil properties was proposed to be improved by a combination of regression and spatial interpolation (Hengl et al, 2004). This approach was called regression-kriging (Kumar et al, 2012;Peng et al, 2013;Mondal et al, 2017). Regression-kriging is one of the most popular, practical and robust hybrid spatial interpolation techniques for modeling of the soil distribution patterns at multiple scales in space and time (Keskin & Grunwald, 2018).…”

No Silver Bullet for Digital Soil Mapping: Country-specific Soil Organic Carbon Estimates across Latin America

“…Data mining techniques have been successfully used to model and predict the spatial variability of soil properties (Rossel and Behrens, 2010;Hengl et al, 2017;Shangguan et al, 2017) and generate country-specific SOC maps (Viscarra Rossel et al, 2014;Adhikari et al, 2014). The combination of regression modeling approaches with geostatistics of model residuals (i.e., regression Kriging) is a combined strategy that 30 has been widely used to map SOC (Hengl et al, 2004;Mishra et al, 2009;Marchetti et al, 2012;Kumar et al, 2012;Peng et al, 2013;Adhikari et al, 2014;Yigini and Panagos, 2016;Nussbaum et al, 2014;Mondal et al, 2017). Machine learning algorithms such as random forests or support vector machines have also been used to increase statistical accuracy of soil 3 SOIL Discuss., https://doi.org /10.5194/soil-2017-40 Manuscript under review for journal SOIL Discussion started: 25 January 2018 c Author(s) 2018.…”