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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #393630
Research Project: From Field to Watershed: Enhancing Water Quality and Management in Agroecosystems through Remote Sensing, Ground Measurements, and Integrative Modeling

Location: Hydrology and Remote Sensing Laboratory

Title: Integrating vegetation phenology and SWAT model for improved modeling of ecohydrological processes

Author
item CHEN, SHOUZHI - Beijing Normal University
item FU, YONGSHUO - Beijing Normal University
item WU, ZHAOFEI - Beijing Normal University
item HAO, FANGHUA - Beijing Normal University
item HAO, ZENGCHAO - Beijing Normal University
item GUO, YAHUI - Beijing Normal University
item GENG, XIAOJUN - Beijing Normal University
item LI, XIAOYAN - Beijing Normal University
item ZHANG, XUAN - Beijing Normal University
item TANG, JING - Beijing Normal University
item SINGH, VIJAY - Texas A&M University
item Zhang, Xuesong

Submitted to: Remote Sensing
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 11/8/2022
Publication Date: 11/30/2022
Citation: Chen, S., Fu, Y., Wu, Z., Hao, F., Hao, Z., Guo, Y., Geng, X., Li, X., Zhang, X., Tang, J., Singh, V.P., Zhang, X. 2022. Integrating vegetation phenology and SWAT model for improved modeling of ecohydrological processes. Remote Sensing. 616:128817. https://doi.org/10.1016/j.jhydrol.2022.128817.
DOI: https://doi.org/10.1016/j.jhydrol.2022.128817

Interpretive Summary: Agroecosystem models have been widely used in assessing impacts of agricultural management on the water cycle at local and watershed scales. In this research, we developed an algorithm that allows the Soil and Water Assessment Tool (SWAT) model to leverage the normalized difference vegetation index (NDVI) time series data from Global Inventory Modeling and Mapping Studies 3rd generation (GIMMS3g) to determine vegetation phenology (e.g., start of growing season and end of growing season). We found that this improvement helps reduce the bias of simulated leaf area index (LAI) by over 40%, and notably increases the accuracy of simulated evapotranspiration in the Han River Basin, China. We anticipate the model-data integration method developed in this study will enhance the utility of SWAT and other agroecosystem models to provide credible information to support best water management practices in agricultural landscapes

Technical Abstract: Agroecosystem models have been widely used in assessing impacts of agricultural management on the water cycle at local and watershed scales. In this research, we developed an algorithm that allows the Soil and Water Assessment Tool (SWAT) model to leverage the normalized difference vegetation index (NDVI) time series data from Global Inventory Modeling and Mapping Studies 3rd generation (GIMMS3g) to determine vegetation phenology (e.g., start of growing season and end of growing season). We found that this improvement helps reduce the bias of simulated leaf area index (LAI) by over 40%, and notably increases the accuracy of simulated evapotranspiration in the Han River Basin, China. We anticipate the model-data integration method developed in this study will enhance the utility of SWAT and other agroecosystem models to provide credible information to support best water management practices in agricultural landscapes can substantially improve the SWAT model performance, highlighting the importance of phenology simulation in the estimation of terrestrial water and energy balance.