Woody canopy cover is an essential variable to characterize and monitor vegetation health, carbon accumulation and land–atmosphere exchange processes. Remote sensing-based global woody and forest cover maps are available, yet with varying qualities. In arid and semi-arid areas, existing global products often underestimate the presence of woody cover due to the sparse woody cover and bright soil background. Case studies on smaller regions have shown that a combination of collected field data and medium-to-high resolution free satellite data (e.g., Landsat / Sentinel-2) can provide woody cover estimates with practically-sufficient accuracies. However, most earlier studies focused on comparably small regions and relied on costly field data. Here, we present a fully remote sensing-based work-flow to derive woody cover estimates over an area covering more than 0.5 million km². The work-flow is showcased over the Zagros Mountains, a semi-arid mountain range covering western Iran, the northeast of Iraq and some smaller fraction of southeast Turkey. We use the Google Earth Engine to create homogeneous Sentinel-2 mosaics of the region using data from several years. These data are combined with reference woody cover values derived by a semi-automatic procedure from Google® and Bing® very high resolution (VHR) imagery. Several random forest (RF) models at different spatial grains were trained and at each grain validated with iterative splits of the reference data into training and validation sets (100 repetitions). Best results (considering the trade-off between model performance and spatial detail) were obtained for the model with 40 m spatial grain which showed stable relationships between the VHR-derived reference data and the Sentinel-2 based estimates of woody cover density. The model resulted in median values of coefficient of determination (R2) and RMSE of 0.67 and 0.11, respectively. Our work-flow is potentially also applicable to other arid and semi-arid regions and can contribute to improve currently available global woody cover products, which often perform poorly in semi-arid and arid regions. Comparisons between our woody cover products with common global woody or forest-cover products indicate a clear superiority of our approach. In future studies, these results may be further improved by taking into account regional differences in the drivers of woody-cover patterns along the environmental gradient of the Zagros area.

木质树冠盖度是表征和监测植被健康、碳积累和土地-大气交换过程的重要变量。可以使用基于遥感的全球木本和森林覆盖图，但质量各不相同。在干旱和半干旱地区，由于木质覆盖稀疏和土壤背景明亮，现有的全球产品往往低估了木质覆盖的存在。对较小区域的案例研究表明，收集到的实地数据和中到高分辨率的免费卫星数据（例如 Landsat / Sentinel-2）相结合，可以提供具有实际足够准确度的木质覆盖估计。然而，大多数早期研究都集中在相对较小的区域，并依赖于昂贵的现场数据。这里，². 工作流程展示在扎格罗斯山脉上，这是一个半干旱的山脉，覆盖伊朗西部、伊拉克东北部和土耳其东南部的一小部分。我们使用谷歌地球引擎使用几年的数据创建该地区的同质 Sentinel-2 马赛克。这些数据与通过半自动程序从 Google® 和 Bing® 超高分辨率 (VHR) 图像得出的参考木质覆盖值相结合。训练了不同空间粒度的几个随机森林 (RF) 模型，并在每个粒度上通过将参考数据迭代拆分为训练和验证集（100 次重复）进行验证。使用 40 m 空间纹理的模型获得了最佳结果（考虑模型性能和空间细节之间的权衡），该模型显示了 VHR 衍生的参考数据与基于 Sentinel-2 的木质覆盖密度估计值之间的稳定关系。该模型产生的决定系数 (R2) 和 RMSE 的中值分别为 0.67 和 0.11。我们的工作流程也可能适用于其他干旱和半干旱地区，并有助于改进目前可用的全球木质覆盖产品，这些产品在半干旱和干旱地区通常表现不佳。我们的木质覆盖产品与全球常见的木质或森林覆盖产品之间的比较表明我们的方法具有明显的优势。在未来的学习中，