Improvement of evapotranspiration estimates for grasslands in the southern Great Plains: Comparing a biophysical model (SWAT) and remote sensing (MODIS)
Lei Qiao, Rodney E. Will, Kevin Wagner, Tian Zhang, Chris B. Zou
Abstract
Mixed- and tall-grass prairies in the southern Great Plains, USA Estimates of evapotranspiration (ET) are widely available from remote sensing and commonly used for water management. However, this approach is limited by prolonged satellite revisit periods and prominent algorithm-induced bias. We compared ET estimates from MODIS (Moderate Resolution Imaging Spectroradiometer) products and those based on the biophysically-based Soil and Water Assessment Tool (SWAT) to measurements using the eddy covariance (EC) technique for a subhumid, tall-grass prairie site near Stillwater, Oklahoma (OK) and a semiarid, mixed-grass prairie site near Clinton, OK in the southern Great Plains, USA. SWAT and MODIS produced ET estimates closer to EC measurements for the tall-grass prairie (calibration site) than for the mixed-grass prairie (validation site), with a better performance from SWAT. For the tall-grass prairie, the R2 values were relatively high and comparable (0.77 and 0.87), and the biases were relatively small (−0.40% and 5.04%) for the SWAT and MODIS comparisons to EC. SWAT performed much better than MODIS for the mixed-grass prairie with R2 values of 0.68 vs. 0.13 and bias of − 1.87% vs. − 45.71%, respectively. The SWAT simulation also reproduced better estimates of aboveground net primary productivity than the MODIS products. This study suggests that site-specific SWAT simulations can produce better ET estimates than MODIS products, especially in the water-limited mixed-grass prairie.