Many soil moisture networks monitor only one land cover type, typically grassland, and the availability of in-situ soil moisture data in other land cover types is severely limited. Satellite-based radiometers lack adequate resolution to match the spatial variability in land cover, which often occurs at the sub-kilometer scale. Thus, spatial and temporal dynamics of root zone soil moisture in regions with heterogeneous land cover types remain poorly understood. Our objective was to determine how effectively root-zone soil moisture for diverse land cover types can be estimated using a water balance model driven by normalized high-resolution, remotely sensed vegetation indices (VI) data and in-situ meteorological data. Root zone soil moisture dynamics under four different land cover types were estimated using normalized VI data as a proxy for the basal crop coefficient. Correlation coefficients (r) between measured and modeled soil moisture ranged from 0.50–0.92, mean absolute error (MAE) ranged from 0.03–0.06 m³ m⁻³, and mean bias error (MBE) ranged from -0.05–0.02 m³ m⁻³ across tallgrass prairie, cropland, mixed hardwood forest, and loblolly pine plantation sites. Model-estimated soil moisture under each land cover type was more accurate than both measured data from the nearest long-term grassland monitoring site and data from the NASA-USDA Enhanced Soil Moisture Active-Passive (SMAP) soil moisture product, providing evidence that in-situ meteorological data and remotely sensed VI data may be integrated into a simple water balance model to better estimate root zone soil moisture across diverse land cover types.

许多土壤水分网络仅监视一种土地覆被类型，通常是草地，而其他土地覆被类型中原位土壤水分数据的可用性受到严重限制。基于卫星的辐射计缺乏足够的分辨率以匹配土地覆盖的空间变异性，而土地变异性通常发生在亚千米尺度上。因此，对于土地覆盖类型不均的地区，根区土壤水分的时空动态仍然知之甚少。我们的目标是确定使用归一化高分辨率，遥感植被指数（VI）数据和原位气象数据驱动的水平衡模型，可以有效地估算出各种土地覆盖类型的根区土壤水分。使用归一化VI数据作为基础作物系数的替代物，估算了四种不同土地覆被类型下的根区土壤水分动态。相关系数（r）在测量的土壤湿度和模拟土壤湿度之间介于0.50-0.92之间，平均绝对误差（MAE）介于0.03-0.06 m ³ m ^-3之间，平均偏差误差（MBE）介于-0.05-0.02 m ³ m ^-3之间横跨高草草原，农田，硬木混交林和火炬松种植园。用模型估算的每种土地覆盖类型下的土壤水分比最近的长期草地监测站的测量数据和NASA-USDA增强的土壤水分主动-被动（SMAP）土壤水分产品的数据更为准确，这提供了证据可以将原地气象数据和遥感VI数据整合到一个简单的水平衡模型中，以更好地估算各种土地覆盖类型的根区土壤水分。