Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions

作者：Bhattarai Nishan^*; Wagle Pradeep; Gowda Prasanna H; Kakani Vijaya G

来源：ISPRS Journal of Photogrammetry and Remote Sensing, 2017, 133: 128-141.

DOI：10.1016/j.isprsjprs.2017.10.010

• Crop water stress index
• Eddy covariance
• Evapotranspiration
• Single-source SEB models
• Regression model

The ability of remote sensing-based surface energy balance (SEB) models to track water stress in rain-fed switchgrass (Panicum virgatum L.) has not been explored yet. In this paper, the theoretical framework of crop water stress index (CWSI; 0 = extremely wet or no water stress condition and 1 = extremely dry or no transpiration) was utilized to estimate CWSI in rain-fed switchgrass using Landsat-derived evapotranspiration (ET) from five remote sensing based single-source SEB models, namely Surface Energy Balance Algorithm for Land (SEBAL), Mapping ET with Internalized Calibration (METRIC), Surface Energy Balance System (SEBS), Simplified Surface Energy Balance Index (S-SEBI), and Operational Simplified Surface Energy Balance (SSEBop). CWSI estimates from the five SEB models and a simple regression model that used normalized difference vegetation index (NDVI), near-surface temperature difference, and measured soil moisture (SM) as covariates were compared with those derived from eddy covariance measured ET (CWSIEC) for the 32 Landsat image acquisition dates during the 2011 (dry) and 2013 (wet) growing seasons. Results indicate that most SEB models can predict CWSI reasonably well. For example, the root mean square error (RMSE) ranged from 0.14 (SEBAL) to 0.29 (SSEBop) and the coefficient of determination (R-2) ranged from 0.25 (SSEBop) to 0.72 (SEBAL), justifying the added complexity in CWSI modeling as compared to results from the simple regression model (R-2 = 0.55, RMSE = 0.16). All SEB models underestimated CWSI in the dry year but the estimates from SEBAL and S-SEBI were within 7% of the mean CWSIEC and explained over 60% of variations in CWSIEC. In the wet year, S-SEBI mostly overestimated CWSI (around 28%), while estimates from METRIC, SEBAL, SEBS, and SSEBop were within 8% of the mean CWSIEC. Overall, SEBAL was the most robust model under all conditions followed by METRIC, whose performance was slightly worse and better than SEBAL in dry and wet years, respectively. Underestimation of CWSI under extremely dry soil conditions and the substantial role of SM in the regression model suggest that integration of SM in SEB models could improve their performances under dry conditions. These insights will provide useful guidance on the broader applicability of SEB models for mapping water stresses in switchgrass under varying geographical and meteorological conditions.