Using SMAP Level-4 soil moisture to constrain MOD16 evapotranspiration over the contiguous USA

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); R-2 = 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); R-2 = 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.

Automated summary

Evapotranspiration (ET) is a crucial hydrological variable that connects the water, carbon, and energy cycles of the Earth. Remote sensing-based models are often used to quantify ET at large scales, but few models incorporate soil moisture, leading to decreased accuracy. By integrating soil moisture estimates from NASA's SMAP mission into the MOD16 ET algorithm framework, along with vegetation observations and regional meteorology data, model performance can be significantly improved, especially in arid climates.