Remote sensing data assimilation for a prognostic phenology model

Abstract

[1] Predicting the global carbon and water cycle requires a realistic representation of vegetation phenology in climate models. However most prognostic phenology models are not yet suited for global applications, and diagnostic satellite data can be uncertain and lack predictive power. We present a framework for data assimilation of Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) and Leaf Area Index (LAI) from the MODerate Resolution Imaging Spectroradiometer (MODIS) to constrain empirical temperature, light, moisture and structural vegetation parameters of a prognostic phenology model. We find that data assimilation better constrains structural vegetation parameters than climate control parameters. Improvements are largest for drought-deciduous ecosystems where correlation of predicted versus satellite-observed FPAR and LAI increases from negative to 0.7-0.8. Data assimilation effectively overcomes the cloud-and aerosol-related deficiencies of satellite data sets in tropical areas. Validation with a 49-year-long phenology data set reveals that the temperature-driven start of season (SOS) is light limited in warm years. The model has substantial skill (R = 0.73) to reproduce SOS inter-annual and decadal variability. Predicted SOS shows a higher inter-annual variability with a negative bias of 5-20 days compared to species-level SOS. It is however accurate to within 1-2 days compared to SOS derived from net ecosystem exchange (NEE) measurements at a FLUXNET tower. The model only has weak skill to predict end of season (EOS). Use of remote sensing data assimilation for phenology model development is encouraged but validation should be extended with phenology data sets covering mediterranean, tropical and arctic ecosystems.

Shifting relative importance of climatic constraints on land surface phenology

“…In turn, these seasonal changes are largely regulated by climatic conditions and in particular by three main factors: temperature, photoperiod and moisture availability (Jolly et al 2005). These climatic factors impose spatially and temporally variable constraints on vegetation activity (Körner and Basler 2010), which may be modelled at various scales (Stöckli et al 2008).…”

Alternative methods to predict actual evapotranspiration illustrate the importance of accounting for phenology – Part 2: The event driven phenology model

“…In some vegetation models, developers went beyond just dates and linked seasonal dynamics of leaf area index (LAI) to thermal time based on plant thermal response functions (Neitsch et al, 2002;Bondeau et al, 2007;Rötzer et al, 2010). In other works researchers started using multiple factors simultaneously to derive phenological trajectories (Jolly et al, 2005;Setiyono et al, 2007;Stöckli et al, 2008). Finally, the interactive approach has been extended to include the concept of event drivers with the first successful trials reported in the companion paper (Kovalskyy and Henebry, 2011).…”

“…Land surface phenology studies the spatiotemporal development of the vegetated land surface using remote sensing (de Beurs and Henebry, 2004), and sometimes called "remote sensing phenology" (Morisette et al, 2009). Several pioneering studies in land surface phenology (de Beurs and Henebry, 2004;Reed, 2006;Zhang et al, 2007;Stöckli et al, 2008;Xiao et al, 2009) point to the need to move beyond the conventional representation of LSPs as static trajectories of vegetation cover properties with negligible response to changing weather conditions.…”

A data assimilation framework for constraining upscaled cropland carbon flux seasonality and biometry with MODIS

“…5), and their influence on crop establishment and growth are still rather poorly represented within SPAc. RS DA is also a suitable tool for mitigating uncertainties due to model parameters and weak understanding of phenological processes (Stöckli et al, 2008). There is clear value in making use of MODIS' full spatiotemporal richness when addressing current key uncertainties of upscaled crop modelling.…”

“…Bondeau et al, 2007;de Noblet-Ducoudré et al, 2004;Kucharik and Twine, 2007;Sus et al, 2010), which traditionally lacked a crop-specific plant functional type. Generally, BGCMs would benefit from a better representation of interannual phenological variability, which is poorly understood (Richardson et al, 2012;Stöckli et al, 2008).…”