Evapotranspiration (ET) is a key hydrologic variable linking the Earth's water, carbon and energy cycles. At large spatial scales, remote sensing-based (RS) models are often used to quantify ET. Despite the large number of RS ET models available, few include soil moisture as a key environmental input, which can degrade model accuracy and utility. Here, we use model assimilation enhanced soil moisture estimates from the NASA SMAP (Soil Moisture Active Passive) mission as a water supply control in the MOD16 ET algorithm framework. SMAP-derived daily surface (0-5 cm depth) and root zone (0-1 m depth) soil moisture are used with MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation observations, and 4 km gridded regional surface meteorology (Gridmet) as primary inputs for estimating daily ET and underlying model soil and stomatal conductance terms. We calibrated the model environmental response parameters using tower eddy covariance ET observations representing major North American biomes. The model ET results were validated using a holdout set of tower observations spanning a large regional climate gradient. The updated ET estimates outperform the baseline MOD16 product across all tower validation sites (RMSE = 0.758 vs 1.108 mm day⁻¹; R² = 0.68 vs 0.45, respectively). Smaller relative improvements were obtained using a recalibrated model with 4 km Gridmet meteorology, but no soil moisture control (RMSE = 0.813 mm day⁻¹; R² = 0.66), indicating that these changes are essential for the improved model performance. The soil moisture-constrained model improvements and relative benefits from the SMAP observations are greater in arid climates, consistent with stronger soil moisture control on ET in water-limited regions. The use of SMAP soil moisture as an additional model constraint improves MOD16 regional performance and provides a new framework for investigating both soil and atmosphere controls on ET.

蒸发蒸腾量（ET）是连接地球水，碳和能量循环的关键水文变量。在大型空间尺度上，通常使用基于遥感的（RS）模型来量化ET。尽管有大量可用的RS ET模型，但很少有人将土壤水分作为关键的环境输入，这会降低模型的准确性和实用性。在这里，我们使用来自NASA SMAP（土壤水分主动被动）任务的模型同化增强的土壤水分估算作为MOD16 ET算法框架中的供水控制。SMAP衍生的日常表面（深度为0-5厘米）和根部区域（深度为0-1 m）与MODIS（中等分辨率成像光谱仪）植被观测数据一起使用，4 km的网格区域地面气象学（Gridmet）作为估算每日ET以及基础模型土壤和气孔导度项的主要输入。我们使用代表北美主要生物群落的塔涡协方差ET观测值对模型环境响应参数进行了校准。使用跨越大区域气候梯度的一组塔观测数据验证了模型ET结果。更新的ET估计值在所有塔验证站点上均优于基准MOD16产品（RMSE = 0.758与1.108 mm天^-1 ; R ² 分别为0.68和0.45）。使用具有4 km Gridmet气象学的重新校准模型可获得较小的相对改进，但没有土壤湿度控制（RMSE = 0.813 mm天^-1； R ²  = 0.66），表明这些变化对于改进模型性能至关重要。在干旱气候下，土壤水分约束模型的改进和SMAP观测的相对收益更大，这与在缺水地区对ET进行更强的土壤水分控制相一致。使用SMAP土壤水分作为附加模型约束可以改善MOD16区域性能，并为研究ET上的土壤和大气控制提供了新的框架。