Modeling grassland spring onset across the Western United States using MODIS data

Wednesday, 17 December 2014
Qinchuan Xin, Tsinghua University, Ministry of Education Key Laboratory for Earth System Modeling, Beijing, China, Mark Broich, University of New South Wales, Sydney, NSW, Australia, Peng Zhu, Tsinghua University, Center for Earth System Sciences, Beijing, China and Peng Gong, University of California Berkeley, Berkeley, CA, United States
Vegetation phenology strongly controls photosynthetic activity and ecosystem function and is essential for monitoring the response of vegetation to climate change and variability. Terrestrial biosphere models require robust phenology models to understand and simulate the relationship between ecosystems and changing climate. In this study, we combine satellite observations (MODIS) and large-scale climatic datasets(NLDAS and Daymet) to develop and refine phenology models for characterizing the spatiotemporal patterns of grassland green-up variations in 2001 - 2010. We implement and calibrate multiple phenological models, which predict the timing of grassland spring onset via commonly available climatological variables. Model evaluation using satellite observations suggests that the Modified Growing-Degree Day (MGDD) model and theAccumulated Growing Season Index (AGSI) model perform better than other tested models. Inclusion of a photoperiod trigger in the temperature-based phenology model could improve the model applicability at the regional scale. In addition, we observe that AGSI outperforms MGDD by capturing interannual phenology variation in large semi-arid areas, likely due to explicit consideration of vapor pressure deficit. We further validate modeled timing of spring onset with eddy covariance datasets from flux tower sites. The timing of grassland spring onsets derived from recalibrated models show good agreement with reference timingsfrom satellite observations and in-situ measurements. Our results are in line with recent studies and imply that there is a need to calibrate current phenology models to predict grassland spring onsets accurately. We present a new spring phenology model that uses vapor pressure deficit to down-regulate GDD accumulation. We demonstrate the feasibility of combining satellite observations and climatic datasets for phenology model developments.