基于辅助环境变量的土壤有机碳空间插值——以黄土丘陵区小流域为例

Soil organic carbon interpolation based on auxiliary environmental covariates: a case study at small watershed scale in Loess Hilly region

利用普通克里格法(OK)、反距离加权法(IDW)、径向基函数法(RBF)、基于土地利用类型修正的普通克里格法(OK_LU)4种插值方法,对黄土丘陵羊圈沟小流域的土壤有机碳含量进行空间插值。预测结果的准确性通过Pearson相关系数(R),平均绝对误差(MAE),均方根误差(RMSE),准确度(AC)来评价。研究结果表明:(1)在前3种常规空间插值方法中,OK对刻画区域土壤有机碳的空间分布趋势效果最佳,其预测MAE值和RMSE值均为最小,Pearson相关系数(R)和准确度(AC)最大,说明其预测结果的准确性最好、预测的极端误差也最小;其次为RBF;IDW预测的效果最差。(2)OK_LU在空间特征表达方面能够更好地反映复杂地形区的局部变异,其插值结果的精度相比OK有一定程度的提高,其平均绝对误差(MAE)从0.900%降到了0.567%,均方根误差(RMSE)从1.101%降到了0.777%,Pearson相关系数(R)从0.4026提高到0.5589,准确度(AC)从0.9081提高到0.9505。综合比较,在黄土丘陵地区,OK_LU能使插值结果的精度有较大提高,是土壤有机碳空间制图的有效途径。

Maps of regional spatial distribution of soil organic carbon （SOC） in detail are needed to guide sustainable soil uses and management decisions. Spatial interpolation methods include deterministic methods and geo-statistical methods. Deterministic method is based on mathematical functions, whichsearch for the spatial similarities within a certain geographical area, such as inverse distance weighting method（IDW）, or create prediction surface by the known samples based on smoothness such as radial-based function method（RBF）; Geo-statistical method is one of the important means for soil mapping supported by geographical and ecological parameters. It can achieve unbiased optimal estimation of regionalized variables by using the statistical property of the known samples, and on the basis of theoretical analysis of semi-variogram, such as ordinary kriging method（OK）. Many of these methods perform well in areas with gentle terrains. However it is uncertain how these methods perform to capture SOC variations in complex terrains, especially in those areas where land uses are highly influenced by human activities, such as in the Loess Plateau of China. Therefore, four methods were applied to predict SOC content spatial explicitly at typical small watershed in the Loess Plateau of China including Ordinary Kriging method（OK）, Inverse Distance Weighted （IDW） method, Radial-Based Function method（RBF）, and Ordinary kriging modified with land-use type method（OK_LU） in the present research to evaluate their usability.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 188 points was divided into two parts randomly：75% for model building and 25% for accuracy validation. The accuracy validation points were used to compare the predicted value with measured value to check the similarity at each point. Prediction results were validated using Pearson＇s Correlation Coefficient（R）, Mean Absolute Error （MAE）, Root Mean Square Error（RMSE） and Accuracy （AC）.The model with higher R and lower MAE and RMSE, while the AC value is closer to 1 was regarded as more relevant, less biased and more accurate. Results showed that： （1） The values of MAE and RMSE produced by OK method were the lowest, while the values of R and AC were the largest, which indicated OK＇s highest effectiveness in SOC interpolation to yield the most accurate results among the three ordinary methods. IDW method ranked behind OK and followed by RBF method in interpolation effectiveness. （2） OK_LU was the best method for predicting SOC in this study. The prediction by OK_LU had more details of depicting the local variation under land-use types. MAE by OK_LU decreased from 0.900% to 0.567%, RMSE decreased from 1.101% to 0.777%, R increased from 0.4026 to 0.5589, and AC increased from 0.9081 to 0.9505 comparing with OK. In conclusion, OK_LU gave more precise evaluation which was critical for obtaining accurate spatial distribution of SOC in Loess Hilly regions. This will be of interests for related research in similar environments.