Area estimates of land cover and land cover change are often based on reference class labels determined by analysts interpreting satellite imagery and aerial photography. Different interpreters may assign different reference class labels to the same sample unit. This interpreter variability is typically not accounted for in variance estimators applied to area estimates of land cover. A simple measurement model provides the basis for an estimator of the total variance (V_Total) that takes into account both sampling variance and interpreter variance. This method requires two or more reference class interpretations (i.e., repeated measurements) obtained by analysts, working independently of each other, for the full sample or a random subsample of the full sample. Estimators of the total variance (${\widehat{V}}_{\mathit{Total}}$) and the variance component attributable to interpreters (${\widehat{V}}_1$) were obtained for the case of two reference class interpretations per repeated sample unit. To evaluate the effect of interpreter variability on variance estimation, we used land cover reference data interpreted by seven analysts who each interpreted the same 300 sample pixels from a region of the Pacific Northwest of the United States. From these data, we estimated the contribution of interpreter variance to the total variance (i.e., ${\widehat{V}}_1/{\widehat{V}}_{\mathit{Total}}$) and the relative bias of the standard simple random sampling variance estimator (${\widehat{V}}_{\mathit{stand}}$) as an estimator of V_Total, defined as 100%*(${\widehat{V}}_{\mathit{stand}}-{\widehat{V}}_{\mathit{Total}}$)/${\widehat{V}}_{\mathit{Total}}$. For each of five land cover classes, we computed ${\widehat{V}}_1$, ${\widehat{V}}_{\mathit{Total}}$, and ${\widehat{V}}_{\mathit{stand}}$ using the sample data from each of the 21 possible pairwise combinations of the seven interpreters, and then calculated the mean of ${\widehat{V}}_1/{\widehat{V}}_{\mathit{Total}}$ and the mean of the estimated relative bias of ${\widehat{V}}_{\mathit{stand}}$ over these 21 pairs. Based on the mean of ${\widehat{V}}_1/{\widehat{V}}_{\mathit{Total}}$ per class, interpreter variance contributed from 25% (cropland) to 76% (grass/shrub) of the total variance, indicating that interpreter variance was a non-negligible component of the total variance. Typically, the standard variance estimator, ${\widehat{V}}_{\mathit{stand}}$, underestimated the total variance with the mean estimated relative bias ranging from −3% (cropland) to −33% (grass/shrub). Classes with greater inconsistency between pairs of interpreters had larger contributions of interpreter variance to the total variance (${\widehat{V}}_1/{\widehat{V}}_{\mathit{Total}}$) and larger negative estimated relative bias of ${\widehat{V}}_{\mathit{stand}}$. Given that interpreter variance can contribute substantially to the total variance, the repeated measurements approach offers a practical way to incorporate this variability into an estimator of the total variance.

土地覆被和土地覆被变化的面积估计通常基于由解释卫星图像和航空摄影的分析师确定的参考类别标签。不同的解释器可能会为同一样本单元分配不同的参考类别标签。在应用于土地覆盖面积估计的方差估计中通常不考虑这种解释器的可变性。一个简单的测量模型为总方差 ( V _Total )的估计量提供了基础，该估计量同时考虑了抽样方差和解释器方差。该方法需要分析人员对完整样本或完整样本的随机子样本进行两个或多个参考类别解释（即重复测量），彼此独立工作。总方差的估计量 (${\widehat{伏}}_{\mathit{全部的}}$) 和归因于口译员的方差分量 (${\widehat{伏}}_1$) 是在每个重复样本单位有两个参考类别解释的情况下获得的。为了评估解释器变异性对方差估计的影响，我们使用了由七名分析师解释的土地覆盖参考数据，他们每人解释了来自美国太平洋西北部地区的相同 300 个样本像素。从这些数据中，我们估计了解释器方差对总方差的贡献（即，${\widehat{伏}}_1/{\widehat{伏}}_{\mathit{全部的}}$) 和标准简单随机抽样方差估计量的相对偏差 (${\widehat{伏}}_{\mathit{站立}}$) 作为V _Total的估计值，定义为 100%*(${\widehat{伏}}_{\mathit{站立}}-{\widehat{伏}}_{\mathit{全部的}}$)/${\widehat{伏}}_{\mathit{全部的}}$. 对于五个土地覆盖类别中的每一个，我们计算了${\widehat{伏}}_1$, ${\widehat{伏}}_{\mathit{全部的}}$， 和 ${\widehat{伏}}_{\mathit{站立}}$ 使用来自七个解释器的 21 种可能的成对组合中的每一种的样本数据，然后计算平均值 ${\widehat{伏}}_1/{\widehat{伏}}_{\mathit{全部的}}$ 以及估计的相对偏差的平均值 ${\widehat{伏}}_{\mathit{站立}}$超过这 21 对。基于均值${\widehat{伏}}_1/{\widehat{伏}}_{\mathit{全部的}}$每个类别，解释器方差占总方差的 25%（农田）到 76%（草/灌木），表明解释器方差是总方差的一个不可忽略的组成部分。通常，标准方差估计量，${\widehat{伏}}_{\mathit{站立}}$，低估了总方差，平均估计相对偏差范围从 -3%（农田）到 -33%（草/灌木）。解释器对之间不一致程度越大的类，解释器方差对总方差的贡献越大（${\widehat{伏}}_1/{\widehat{伏}}_{\mathit{全部的}}$) 和更大的负估计相对偏差 ${\widehat{伏}}_{\mathit{站立}}$. 鉴于解释器方差对总方差的贡献很大，重复测量方法提供了一种实用的方法来将这种可变性合并到总方差的估计中。