An improved similarity-based approach to predicting and mapping soil organic carbon and soil total nitrogen in a coastal region of northeastern China

Abstract

Soil organic carbon (SOC) and soil total nitrogen (STN) are major soil indicators for soil quality and fertility. Accurate mapping SOC and STN in soils would help both managed and natural soils and ecosystem management. This study developed an improved similarity-based approach (ISA) to predicting and mapping topsoil (0–20 cm soil depth) SOC and STN in a coastal region of northeastern China. Six environmental variables including elevation, slope gradient, topographic wetness index, the mean annual temperature, the mean annual temperature, and normalized difference vegetation index were used as predictors. Soil survey data in 2012 was designed based on the clustering of the study area into six climatic vegetation landscape units. In each landscape unit, 20–25 sampling points were determined at different landform positions considering local climate, soil type, elevation and other environmental factors, and finally 126 sampling points were obtained. Soil sampling from the depth of 0–20 cm were used for model prediction and validation. The ISA model performance was compared with the geographically weighted regression (GWR), regression kriging (RK), boosted regression trees (BRT) considering mean absolute prediction error (MAE), root mean square error (RMSE), coefficient of determination (R2), and maximum relative difference (RD) indices. We found that the ISA method performed best with the highest R2 and lowest MAE, RMSE compared to GWR, RK, and BRT methods. The ISA method could explain 76% and 83% of the total SOC and STN variability, respectively, 12–40% higher than other models in the study area. Elevation had the largest influence on SOC and STN distribution. We conclude that the developed ISA model is robust and effective in mapping SOC and STN, particularly in the areas with complex vegetation-landscape when limited samples are available. The method needs to be tested for other regions in our future research.

Zoning Prediction and Mapping of Three-Dimensional Forest Soil Organic Carbon: A Case Study of Subtropical Forests in Southern China

“…Many studies of zonal models have focused on topsoil (0-30 cm). The maximum improvement in precision (R 2 ) of our zonal model was smaller (0.07) than that of Wang et al [24], probably because our validation sample points were 31 times more numerous than theirs. In addition, the present study obtained slightly higher zoning accuracy than other studies [14,18,44].…”

“…Moreover, simply mosaicking the region predictions can lead to boundary artifacts and discontinuous patterns [24]. This phenomenon is due to the method of dividing homogeneous zones, which leads to abrupt changes in the boundaries of different landscapes [25].…”