Remotely sensed ground cover data play an important role in Australian rangelands research development and extension, reflecting broader global trends in the use of remotely sensed data. We tested the relationship between remotely sensed ground cover data and field-based assessments of grazing land condition in the largest quantitative analysis of its type to date. We collated land condition data from 2282 sites evaluated between 2004 and 2018 in the Burdekin and Fitzroy regions of Queensland. Condition was defined using the Grazing Land Management land condition framework that ranks grazing land condition on a four point ordinal scale based on dimensions of vegetation composition, ground cover level and erosion severity. Nine separate ground cover derived indices were then calculated for each site. We found that all indices significantly correlated with grazing land condition on corresponding sites. Highest correlations occurred with indices that benchmarked ground cover at the site against regional ground cover assessed over several years. These findings provide some validation for the general use of ground cover data as an indicator of rangeland health/productivity. We also constructed univariate land condition models with a subset of these indices. Our models predicted land condition significantly better than random assignment though only moderately well; no model correctly predicted land condition class on >40% of sites. While the best models predicted condition correctly at >60% of A and D condition sites, condition at sites in B and C condition sites was poorly predicted. Several factors limit how well ground cover levels predict land condition. The main challenge is modelling a multidimensional value (grazing land condition) with a unidimensional ground cover measurement. We suggest that better land condition models require a range of predictors to address this multidimensionality but cover indices can make a substantial contribution in this context.

遥感地面覆盖数据在澳大利亚牧场研究开发和推广中发挥着重要作用，反映了遥感数据使用的更广泛的全球趋势。我们在迄今为止规模最大的类型定量分析中测试了遥感地面覆盖数据与基于实地的牧场状况评估之间的关系。我们整理了 2004 年至 2018 年间在昆士兰 Burdekin 和 Fitzroy 地区评估的 2282 个地点的土地状况数据。条件是使用牧场管理土地条件框架定义的，该框架根据植被成分、地面覆盖水平和侵蚀严重程度的维度，在四点顺序尺度上对牧场条件进行排名。然后为每个站点计算九个单独的地面覆盖派生指数。我们发现所有指标都与相应地点的牧场状况显着相关。最高的相关性出现在以场地地面覆盖与多年评估的区域地面覆盖为基准的指数中。这些发现为将地面覆盖数据作为牧场健康/生产力指标的一般用途提供了一些验证。我们还使用这些指数的一个子集构建了单变量土地状况模型。我们的模型预测土地状况明显好于随机分配，尽管只是中等程度；没有模型正确预测 > 40% 的场地的土地状况等级。虽然最佳模型正确预测了 >60% 的 A 和 D 条件站点的条件，但在 B 和 C 条件站点的站点条件预测不佳。有几个因素限制了地面覆盖水平对土地状况的预测能力。主要挑战是使用一维地面覆盖测量对多维值（牧场条件）进行建模。我们建议更好的土地状况模型需要一系列预测变量来解决这种多维问题，但覆盖指数可以在这方面做出重大贡献。