在（自然和人文）地理学，常常需要根据样本估计区域均值。常用的方法分两类：随机样本简单或分层相加，以及克里格（空间回归）技术。前者可以保证估值无偏，但不能保证最优（即估值误差最小）；克里格技术可以产生无偏最优估计，但要求区域是二阶平稳的，这常常不符合真实区域情形。本文提出了（复杂）分层非均质区域的空间均值估算公式，理论证明该模型具有无偏最优性质；并以区域耕地面积估算、地表温度估算、和模拟数据等实验为案例得到验证。 

In geosciences, one often needs to estimate the spatial means of surfaces representing physical attributes. Under certain conditions, this kind of estimation is often performed by a simple summation of a random sample or by some kind of a Kriging (spatial regression) technique. For example, the naive sample mean assumes that the sample is randomly distributed across space, which is a restrictive assumption with limited applicability in real-world situations (e.g., in the case of nonhomogeneous surfaces, the naive sample mean is a biased estimate of the actual surface mean). Kriging techniques can generate unbiased estimates for certain kinds of homogeneous surfaces but may be not appropriate in cases of stratified nonhomogeneity when the covariances exhibit considerable differences between different strata of the surface. In this paper, we extend the Kriging concept to study surfaces with stratified nonhomogeneity. The corresponding analytical formulas are derived, and empirical studies are performed that involve real-world and simulated data sets. Numerical comparative analysis showed that the proposed method performed well compared to other methods commonly used for the purpose of estimating surface means across space.