Medium resolution satellite-derived fractional cover estimates of bare soil (f_BS), photosynthetic vegetation (f_PV), and non-photosynthetic vegetation (f_NPV) provide a powerful means to study arid ecosystem dynamics. This paper employed remote sensing estimates of f_PV and f_NPV from five case study sites from Australia's vegetated dunefields to observe (a) vegetation growth response to rainfall ‘pulses’ and subsequent transition to non-photosynthetic dormancy or senescence; (b) multiple time scales of antecedent climatic influence on vegetation cover; (c) the susceptibility of dunes to wind-blown sand drift during periods of low cover; and (d) the implications of image resolution choice when ground cover is heterogeneous.

A spectral unmixing model for Australia's arid zone (termed ‘AZN’) was first developed by generating endmembers from a dataset of 1405 field surveys; Landsat time series estimates of f_PV and f_NPV were subject to a Seasonal-Trend decomposition by Loess (STL); Time series components were correlated with rainfall (P) and aridity at various accumulation periods; Fire maps were used to compare the climatic response of unburnt and burnt vegetation; Landform maps were used to isolate dune vegetation cover from the adjacent interdunes; and Landsat estimates of erodible area were compared with Sentinel-2 and WorlView-3 data.

The new AZN model yielded Root Mean Square Error (RMSE) estimates of 14.5% (f_BS), 6.5% (f_PV) and 15.8% (f_NPV) during cross validation. The AZN model also compared favourably to an existing continental-scale model when evaluated with independent reference data. Rainfall pulse responses of dune vegetation were detected initially as f_PV, and 3–9 months later as a peak in f_NPV. Components of f_PV responded to P accumulated over 3–9-months (intra-annual), and 12–15-months (trend). The long-term build-up of f_NPV, if left unburnt, was influenced by rainfall patterns over the preceding 45–114 months. Fires reduced both the depth and strength of antecedent rainfall's influence on vegetation, and vegetation was often more sensitive to P than to aridity. Erodibility (total cover <14%) and partial erodibility (cover <35%) were more common at the driest sites but did not universally match aridity levels, due to fires and differing vegetation. The targeting of dune crest regions highlighted their enhanced susceptibility to sand drift (in most cases), and, given their occurrence on relatively narrow ridges (~30 m), the importance of estimating cover at Landsat resolutions or better (e.g. Sentinel-2).

中分辨率卫星衍生的裸土 ( f _BS )、光合植被 ( f _PV ) 和非光合植被 ( f _NPV ) 的部分覆盖估计提供了研究干旱生态系统动态的有力手段。本文采用f _PV和f _{NPV 的}遥感估计从澳大利亚植被覆盖的沙丘的五个案例研究地点观察 (a) 植被生长对降雨“脉冲”的响应以及随后向非光合作用休眠或衰老的过渡；(b) 前期气候对植被覆盖影响的多个时间尺度；(c) 沙丘在低覆盖期间易受风吹沙的影响；(d) 当地面覆盖不均匀时图像分辨率选择的影响。

澳大利亚干旱区（称为“AZN”）的光谱分离模型最初是通过从 1405 次实地调查的数据集生成端元而开发的；f _PV和f _{NPV 的}Landsat 时间序列估计受 Loess (STL) 的季节性趋势分解的影响；时间序列分量与不同积累期的降雨量（P）和干旱度相关；火灾图用于比较未燃烧和燃烧植被的气候响应；地形图被用来将沙丘植被与相邻的沙丘隔离开来；和 Landsat 对可侵蚀区域的估计与 Sentinel-2 和 WorlView-3 数据进行了比较。

新的 AZN 模型在交叉验证期间产生了 14.5% ( f _BS )、6.5% ( f _PV ) 和 15.8% ( f _NPV ) 的均方根误差 (RMSE) 估计值。在使用独立参考数据进行评估时，AZN 模型也优于现有的大陆尺度模型。沙丘植被的降雨脉冲响应最初检测为f _PV，3-9 个月后检测为f _NPV的峰值。f _{PV 的}分量对P累积超过 3-9 个月（年内）和 12-15 个月（趋势）做出反应。f _NPV的长期积累，如果不燃烧，则会受到前 45-114 个月降雨模式的影响。火灾降低了前期降雨对植被影响的深度和强度，而且植被对磷的敏感性往往比对干旱更敏感。可蚀性（总覆盖率 <14%）和部分可蚀性（覆盖率 <35%）在最干燥的地方更为常见，但由于火灾和不同的植被，它们并不普遍与干旱水平相匹配。以沙丘顶区域为目标突出了它们对沙漂移的敏感性增强（在大多数情况下），并且鉴于它们出现在相对狭窄的山脊（~30 m）上，以 Landsat 分辨率或更好的分辨率（例如 Sentinel-2）估计覆盖的重要性.