Analysis of Spatial Variability Characteristics and Correlation of Soil Organic Matter and Total Nitrogen in Saline-Alkali Land—The Case Study of the Yellow River Island
以黄河三角洲地区典型的盐碱地分布地——黄河岛为研究区，结合传统统计学、地统计学及GIS技术，研究盐碱地不同土层土壤有机质和全氮的空间变异特征，并探讨两者的相关性。结果表明：1) 盐碱地不同土层土壤有机质和全氮含量总体偏低，具有中等空间变异性；2) 不同土层土壤有机质含量及0~20 cm土层土壤全氮含量具有一定的空间相关性，变异受结构性因素和随机性因素影响，20~40 cm土层土壤全氮含量空间相关性较弱，变异受随机性因素影响大；3) 不同土层土壤有机质和全氮含量分布状况相似，片状相间分布，东西方向呈先增加后减少趋势，南北方向呈减少趋势；4)不同土层土壤有机质和全氮含量具有强正相关性，0~20 cm和20~40 cm土层土壤有机质含量估算全氮含量的最佳模型分别为S型模型和幂函数模型，回归方程分别为Y = e0.099−7.338/X、Y = 0.064*X0.849。 Taking typical distribution of saline-alkali land in the Yellow River Delta-the Yellow River Island as research area, applying traditional statistics, Geostatistics and GIS technology, the research ana-lyzed the spatial variability characteristics of soil organic matter and total nitrogen in different soil layers in saline-alkali land and explored the correlation between them. The results showed that: 1) The contents of soil organic matter and total nitrogen in different soil layers were generally low and with moderate spatial variability; 2) Soil organic matter in different soil layers and soil total nitro-gen in the soil layers of the depth in 0~20 cm had a certain spatial correlation, and the variation was affected by structure factors and random factors, and the spatial correlation of soil total nitrogen in the 20~40 cm soil layer was relatively weak, and the variation is greatly affected by random factors; 3) The distributions of soil organic matter and total nitrogen in different soil layers are similar. They were distributed in patches, the overall trend was increased and then decreased in the direction of east-west, and decreased in the direction of north-south; 4) There was a strong positive correlation between soil organic matter and total nitrogen content in different soil layers. The best models used for estimating total nitrogen content by soil organic matter content were the S-type model and the power function model respectively in the depth of 0~20 cm and 20~40 cm soil layers, the regression equations were Y = e0.099−7.338/X、Y = 0.064*X0.849.