Location: Forage and Livestock Production ResearchTitle: Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions Author
|Bhattarai, Nishan - University Of Michigan|
|Kakani, Vijaya - Oklahoma State University|
Submitted to: Journal of Photogrammetry and Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 10/10/2017
Publication Date: 10/16/2017
Citation: Bhattarai, N., Wagle, P., Gowda, P., Kakani, V. 2017. Utility of remote sensing-based surface energy balance models to track water stress in rain-fed switchgrass under dry and wet conditions. Journal of Photogrammetry and Remote Sensing. Available at: https://doi.org/10.1016/j.isprsjprs.2017.10.010.
Interpretive Summary: Rainfed biofuel production systems are vulnerable to climate variability and extremes. The short-term in-season dry spells that reduce soil moisture soil moisture and plant growth particularly during key growth stages are often ignored due to the level of complexity associated with characterizing such conditions. In this study, we evaluated five widely used remote sensing-based evapotranspiration (ET) models (SEBAL, METRIC, SEBS, S-SEBI, and SSEBop) to estimate crop water stress index (CWSI) and compared against CWSI derived from eddy covariance measured ET (CWSIEC). Thirty two Landsat satellite images covering both dry and wet growing seasons. Our results showed that all ET models underestimated CWSI during dry years, but the estimates from SEBAL and S-SEBI were within 7% of the mean CWSIEC. In wet years, CWSI estimates from METRIC and SEBAL were within 4% and 8%, respectively, S-SEBI overestimated CWSI by 28%, and SEBS and SSEBop consistently underestimated (18% and 16%, respectively) due to overestimation of ET. Overall, SEBAL performed the best under both dry and wet conditions followed by METRIC. Our results suggest that the integration of a soil moisture component in ET models could improve their performances under dry conditions.
Technical Abstract: The ability of remote sensing-based surface energy balance (SEB) models to track water stress in rain-fed switchgrass has not been explored yet. In this paper, the theoretical framework of crop water stress index (CWSI) was utilized to estimate CWSI in rain-fed switchgrass (Panicum virgatum L.) 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 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 coefficient of determination (R2) 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 (R2 = 0.55, RMSE = 0.17). All SEB models underestimated CWSI in the dry year but the estimates from SEBAL and S-SEBI were within 7% of the mean CWSIEC. In the wet year, S-SEBI mostly overestimated CWSI (around 28%), METRIC and SEBAL estimates were within 4% and 8%, respectively, and SEBS and SSEBop consistently underestimated CWSI (18% and 16%, respectively) due to overestimation of ET. Overall, SEBAL was the most robust model under all conditions followed by METRIC, whose performance was slightly worse and better 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 a SM component 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.