Abstract

Accurate soil organic matter (SOM) estimation could provide critical information to understand soil organic carbon sequestration, soil fertility, and the global carbon cycle. A nearest-neighbourhood autoregressive moving average (NN-ARMA) modelling technique along with linear regression has been used to predict localized soil SOM variation based on topographical characteristics and vegetation indices in semi-arid region of Saudi Arabia. Seven topographic variables derived using DEM, and twelve vegetation indices obtained from Landsat 8 used in the model. The best NN-ARMA model showed seven significant variables explaining 96.4% of the total variation of SOM, whereas the best linear regression model could explain 78.8% of the total variation of SOM. The results showed that NN-ARMA model gave better results compared to the linear regression model. Our research gave a better understanding of the possibility of accurate estimation of SOM using the NN-ARMA approach using topographical characteristics and vegetation indices easily acquired by RS sensors.

摘要

准确的土壤有机质 (SOM) 估算可为了解土壤有机碳固存、土壤肥力和全球碳循环提供关键信息。最近邻自回归移动平均 (NN-ARMA) 建模技术和线性回归已被用于根据沙特阿拉伯半干旱地区的地形特征和植被指数预测局部土壤 SOM 变化。使用 DEM 得出的七个地形变量，以及从模型中使用的 Landsat 8 获得的十二个植被指数。最好的 NN-ARMA 模型显示 7 个显着变量，解释了 SOM 总变异的 96.4%，而最佳线性回归模型可以解释了 SOM 总变异的 78.8%。结果表明，与线性回归模型相比，NN-ARMA 模型给出了更好的结果。