Assimilation of satellite reflectance data into a dynamical leaf model to infer seasonally varying leaf areas for climate and carbon models (original) (raw)
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Assimilating Reflectance Data Into a Ecosystem Model to Improve Estimates of Terrestrial Carbon Flux
2007
Ecosystem models are valuable tools for understanding the growth of vegetation, its response to climatic change and its role in the cycling of greenhouse gasses. Data Assimilation (DA) of synoptic coverage Earth Observation (EO) data into ecosystem models provides a statistically optimal mechanism for constraining the model state vector trajectory both spatially and temporally. EO "products" such as leaf area index (LAI) are attractive candidates for assimilation, but it is difficult to assign accurate uncertainty estimates to such products (a critical requirement of DA) and, more importantly, they are derived on the basis of assumptions that may be contradictory to those in the ecosystem model. An attractive alternative, therefore, is to assimilate reflectance data; the uncertainty in which is more easily understood. The assumptions made in generating the reflectance data are independent of assumptions in the ecosystem model and may consequently be treated as additional sources of uncertainty. To achieve this it is necessary to build a canopy reflectance model into the assimilation scheme. This paper describes the coupling of a canopy reflectance model to a simple ecosystem model. Reflectance data are assimilated over a boreal forest and improvements in predicted carbon fluxes are shown with comparison to field data. Previous work has highlighted problems of lost samples due to snow cover, resulting in poorly constrained flux estimates during winter months. This issue is addressed by incorporating a snow reflectance model. Results utilising the EnKF as a parameter estimator are also discussed.
Ecological Applications, 2015
Our limited ability to accurately simulate leaf phenology is a leading source of uncertainty in models of ecosystem carbon cycling. We evaluate if continuously updating canopy state variables with observations is beneficial for predicting phenological events. We employed ensemble adjustment Kalman filter (EAKF) to update predictions of leaf area index (LAI) and leaf extension using tower-based photosynthetically active radiation (PAR) and moderate resolution imaging spectrometer (MODIS) data for 2002-2005 at Willow Creek, Wisconsin, USA, a mature, even-aged, northern hardwood, deciduous forest. The ecosystem demography model version 2 (ED2) was used as the prediction model, forced by offline climate data. EAKF successfully incorporated information from both the observations and model predictions weighted by their respective uncertainties. The resulting estimate reproduced the observed leaf phenological cycle in the spring and the fall better than a parametric model prediction. These results indicate that during spring the observations contribute most in determining the correct bud-burst date, after which the model performs well, but accurately modeling fall leaf senesce requires continuous model updating from observations. While the predicted net ecosystem exchange (NEE) of CO 2 precedes tower observations and unassimilated model predictions in the spring, overall the prediction follows observed NEE better than the model alone. Our results show state data assimilation successfully simulates the evolution of plant leaf phenology and improves model predictions of forest NEE.
Frontiers in Water
As vegetation regulates water, carbon, and energy cycles from the local to the global scale, its accurate representation in land surface models is crucial. The assimilation of satellite-based vegetation observations in a land surface model has the potential to improve the estimation of global carbon and energy cycles, which in turn can enhance our ability to monitor and forecast extreme hydroclimatic events, ecosystem dynamics, and crop production. This work proposes the assimilation of a remotely sensed vegetation product (Leaf Area Index, LAI) within the Noah Multi-Parameterization land surface model using an Ensemble Kalman Filter technique. The impact of updating leaf mass along with LAI is also investigated. Results show that assimilating LAI data improves the estimation of transpiration and net ecosystem exchange, which is further enhanced by also updating the leaf mass. Specifically, transpiration anomaly correlation coefficients improve in about 77 and 66% of the global land...
Remote Sensing of Environment, 2006
In this paper, we present an improved procedure for collecting no or little atmosphere-and snow-contaminated observations from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. The resultant time series of daily MODIS data of a temperate deciduous broadleaf forest (the Bartlett Experimental Forest) in 2004 show strong seasonal dynamics of surface reflectance of green, near infrared and shortwave infrared bands, and clearly delineate leaf phenology and length of plant growing season. We also estimate the fractions of photosynthetically active radiation (PAR) absorbed by vegetation canopy (FAPAR canopy ), leaf (FAPAR leaf ), and chlorophyll (FAPAR chl ), respectively, using a coupled leafcanopy radiative transfer model (PROSAIL-2) and daily MODIS data. The Markov Chain Monte Carlo (MCMC) method (the Metropolis algorithm) is used for model inversion, which provides probability distributions of the retrieved variables. A two-step procedure is used to estimate the fractions of absorbed PAR: (1) to retrieve biophysical and biochemical variables from MODIS images using the PROSAIL-2 model; and (2) to calculate the fractions with the estimated model variables from the first step. Inversion and forward simulations of the PROSAIL-2 model are carried out for the temperate deciduous broadleaf forest during day of year (DOY) 184 to 201 in 2005. The reproduced reflectance values from the PROSAIL-2 model agree well with the observed MODIS reflectance for the five spectral bands (green, red, NIR 1 , NIR 2 , and SWIR 1 ). The estimated leaf area index, leaf dry matter, leaf chlorophyll content and FAPAR canopy values are close to field measurements at the site. The results also showed significant differences between FAPAR canopy and FAPAR chl at the site. Our results show that MODIS imagery provides important information on biophysical and biochemical variables at both leaf and canopy levels.
Biogeosciences, 2014
Although forest management is one of the instruments proposed to mitigate climate change, the relationship between forest management and canopy albedo has been ignored so far by climate models. Here we develop an approach that could be implemented in Earth system models. A stand-level forest gap model is combined with a canopy radiation transfer model and satellite-derived model parameters to quantify the effects of forest thinning on summertime canopy albedo. This approach reveals which parameter has the largest affect on summer canopy albedo: we examined the effects of three forest species (pine, beech, oak) and four thinning strategies with a constant forest floor albedo (light to intense thinning regimes) and five different solar zenith angles at five different sites (40
Tree Physiology, 1999
Seasonal differences in phenology between coniferous and deciduous tree species need to be considered when developing models to estimate CO 2 exchange in temperate forest ecosystems. Because seasonal variations in CO 2 flux in temperate forests are closely correlated with plant phenology, we quantified the phenology of forest species in a multilayered forest with patches of Scots pine (Pinus sylvestris L.) and oak (Quercus robur L.) in Brasschaat, Belgium. A scaling-up modeling approach was developed to simulate reflectance at the leaf and canopy scales over a one-year cycle. Chlorophyll concentration, water content, specific leaf area and leaf area index of the forest species were measured throughout an entire year (1997). Scaling-up from the leaf to canopy was achieved by linking the PROSPECT and SAIL models. The result is the annual progression of the fraction of absorbed photosynthetically active radiation (fAPAR) in a 1 km 2 forest area, which can be directly related to high-resolution, remotely sensed data.
Nonlinear Processes in Geophysics Discussions, 2016
We newly developed a data assimilation system based on a particle filter approach with the Spatially Explicit Individual-Based Dynamic Global Vegetation Model (SEIB-DGVM). We first performed an idealized observing system simulation experiment to evaluate the impact of assimilating the leaf area index (LAI) data every 4 days, assuming the satellite-based LAI. Although we assimilated only LAI as a whole, the forest and grass LAIs were estimated separately with high accuracy. Uncertain model parameters and other state variables were also estimated accurately. Therefore, we extended the experiment to the real world using the real Moderate Resolution Imaging Spectroradiometer (MODIS) LAI data, and obtained promising results.
An improved analysis of forest carbon dynamics using data assimilation
Global Change Biology, 2005
There are two broad approaches to quantifying landscape C dynamicsby measuring changes in C stocks over time, or by measuring fluxes of C directly. However, these data may be patchy, and have gaps or biases. An alternative approach to generating C budgets has been to use process-based models, constructed to simulate the key processes involved in C exchange. However, the process of model building is arguably subjective, and parameters may be poorly defined. This paper demonstrates why data assimilation We show, via sensitivity analysis, how assimilating an estimate of photosynthesiswhich might be provided indirectly by remotely sensed dataimproves the analysis of NEE.