Responses of Vegetation Productivity to the Droughts in 2004 and 2015 Over Tropical Asia Simulated by Different Models

• Published in: IGARSS 2024 - 2024 IEEE International Geoscience and Remote Sensing Symposium pp. 4982 - 4985
• Main Authors: Yang, Hua, He, Wei, Xie, Donghui, Jiang, Lingmei
• Format: Conference Proceeding
• Language: English
• Published: IEEE 07.07.2024
• Subjects: Asia; data-driven model; drought; Droughts; gross primary productivity; LUE model; Moisture; Productivity; Soil moisture; Vegetation; Vegetation mapping
• ISSN: 2153-7003
• DOI: 10.1109/IGARSS53475.2024.10642601

The frequency and intensity of droughts have increased rapidly up to now and will become more severe in the future. To better characterize the impacts of drought on vegetation productivity over large scales by remote-sensing-driven models, we should understand the responses of vegetation to historical drought events. In this study, the responses of vegetation gross primary productivity (GPP) to the droughts in 2004 and 2015 over tropical Asia were analyzed along with hydrometeorological data and compared with data-driven models. We found that the GPP anomalies in data-driven models are negative in both drought events. However, the simulation of light-use efficiency (LUE) models revealed the GPP anomalies are negative in 2004, while positive in 2015. We discussed its possible causation. A key factor for LUE models is how they represent the effect of water stresses on GPP, where soil moisture (SM) is an indicator that largely differed from other variables, e.g. saturated vapor pressure deficit and land surface water index in characterizing water stresses. The anomalies of SM are obviously different in 2004 and 2015, thus, the representation of SM stress could a crucial factor for improving the LUE models to better simulated GPP in characterizing responses of vegetation GPP to droughts over large scales.