Recent investigations have supported the possibility of predicting soil water content (SWC) through a simplified soil water balance (WB) model fed with remotely sensed actual evapotranspiration (ETa) estimates. This approach, however, requires information on main soil features (i.e. depth, wilting point, field capacity) which are generally difficult to retrieve over large regions. The current paper proposes an alternative model which directly predicts SWC relying on the same logic of the recently proposed ETa estimation method, i.e. the combination of meteorological and normalized difference vegetation index (NDVI) datasets. The theoretical bases of the old and new SWC estimation methods are first described. Both methods are then applied in Central Italy using ancillary and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI data having a spatial resolution of 250 m; the outputs obtained are assessed versus SWC measurements taken both continuously and instantaneously. In the former case three ecosystems are analyzed (i.e. the grassland of Amplero and the forests of Barbialla and Amiata), where SWC was measured by probes during three different years (2003, 2012 and 2018, respectively). The latter experiment concerns 362 sample sites where SWC gravimetric measurements were taken during a similar time span (2000–2018). In both cases SWC is first normalized into relative SWC (RSWC), which is more directly influential on vegetation conditions. The results of both experiments indicate that the two simulation methods perform similarly when the former is driven by adequate information on soil features. The main limitations of the two simulation approaches are due to the spatial resolution mismatch between SWC measurements and estimates, which has a relatively minor impact on the first three homogeneous areas but is decisive for the other sampled sites. In general, the new simulation method is capable of predicting RSWC with relatively high spatial and temporal resolution without the use of specific soil information.

最近的研究已经支持通过简化的土壤水分平衡（WB）模型并结合遥感的实际蒸散量（ETa）估算值来预测土壤含水量（SWC）的可能性。但是，这种方法需要有关主要土壤特征（即深度，枯萎点，田间持水量）的信息，而这些信息通常很难在大范围内获取。本文提出了一种替代模型，该模型可根据最近提出的ETa估算方法的相同逻辑直接预测SWC，即气象数据和归一化植被指数（NDVI）数据集的组合。首先介绍新旧SWC估计方法的理论基础。然后在意大利中部使用空间分辨率为250 m的辅助和中等分辨率成像光谱仪（MODIS）NDVI数据来应用这两种方法。相对于连续和瞬时进行的SWC测量，评估获得的输出。在前一种情况下，分析了三个生态系统（即，Amplero的草地以及Barbialla和Amiata的森林），其中通过三个不同年份（分别是2003年，2012年和2018年）的探测来测量SWC。后一个实验涉及362个样本站点，这些站点在相似的时间跨度（2000–2018）中进行了SWC重量测量。在这两种情况下，首先将SWC归一化为相对SWC（RSWC），这对植被状况具有更直接的影响。两种实验的结果表明，当前者受土壤特征的足够信息驱动时，两种模拟方法的性能相似。两种模拟方法的主要局限性是由于SWC测量值和估计值之间的空间分辨率不匹配，这对前三个同质区域影响相对较小，但对其他采样点具有决定性作用。通常，这种新的模拟方法能够在不使用特定土壤信息的情况下以相对较高的时空分辨率来预测RSWC。这对前三个同质区域的影响相对较小，但对其他采样点具有决定性作用。通常，这种新的模拟方法能够在不使用特定土壤信息的情况下以相对较高的时空分辨率来预测RSWC。这对前三个同质区域的影响相对较小，但对其他采样点具有决定性作用。通常，这种新的模拟方法能够在不使用特定土壤信息的情况下以相对较高的时空分辨率来预测RSWC。