The potential of the MERIS Terrestrial Chlorophyll Index for carbon flux estimation

In this study we evaluated the potential of the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index (MTCI) for monitoring gross primary productivity (GPP) across fifteen eddy covariance towers encompassing a wide variation in North American vegetation composition. The across-site relationship between MTCI and tower GPP was stronger than that between either the MODIS GPP or EVI and tower GPP, suggesting that data from the MERIS sensor can be used as a valid alternative to MODIS for estimating carbon fluxes. Correlations between tower GPP and both vegetation indices (EVI and MTCI) were similar only for deciduous vegetation, indicating that physiologically driven spectral indices, such as the MTCI, may also complement existing structurally-based indices in satellite-based carbon flux modeling efforts.     

Assessing FPAR source and parameter optimization scheme in application of a diagnostic carbon flux model

The combination of satellite remote sensing and carbon cycle models provides an opportunity for regional to global scale monitoring of terrestrial gross primary production, ecosystem respiration, and net ecosystem production. FPAR (the fraction of photosynthetically active radiation absorbed by the plant canopy) is a critical input to diagnostic models, however little is known about the relative effectiveness of FPAR products from different satellite sensors nor about the sensitivity of flux estimates to different parameterization approaches. In this study, we used multiyear observations of carbon flux at four eddy covariance flux tower sites within the conifer biome to evaluate these factors. FPAR products from the MODIS and SeaWiFS sensors, and the effects of single site vs. cross-site parameter optimization were tested with the CFLUX model. The SeaWiFs FPAR product showed greater dynamic range across sites and resulted in slightly reduced flux estimation errors relative to the MODIS product when using cross-site optimization. With site-specific parameter optimization, the flux model was effective in capturing seasonal and interannual variation in the carbon fluxes at these sites. The cross-site prediction errors were lower when using parameters from a cross-site optimization compared to parameter sets from optimization at single sites. These results support the practice of multisite optimization within a biome or ecoregion for parameterization of diagnostic carbon flux models.     

基于高时空分辨率融合影像的红树林总初级生产力遥感估算

红树林是热带与亚热带地区潮间带具备高植被生产力和高储碳量的滨海湿地植被类型,在维系全球碳平衡过程中扮演着重要的角色。目前通量站点尺度的红树林生产力研究已取得了一定的进展,然而由于受到遥感影像时空分辨率和红树林斑块分布的限制,区域尺度红树林总初级生产力（Gross Primary Production,GPP）估算仍少有涉及。基于影像融合算法获得的高时空分辨率植被指数数据集,结合红树林通量观测数据开展光能利用率模型的参数估计和模型验证研究,实现了区域尺度的红树林GPP估算,获取了一套2012年广东省高桥红树林GPP高时空分辨率数据集。数据验证得到的决定系数R² = 0.64,较现有的MOD17A2和GLASS产品GPP估算精度提高了48.9%。实验结果显示：高桥红树林最大光能利用率为3.07 g C MJ^-1,研究区内全年GPP均值为1 915.4 g C m^-2 a^-1。红树林季节平均GPP夏、秋季大于春、冬季。该方法和估算数据可为区域尺度红树林生产力研究和红树林保护提供高精度数据支持。     

Parameterization of a diagnostic carbon cycle model for continental scale application

Diagnostic carbon cycle models depend on parameterization to establish model sensitivity to climate variables and site factors. Here we acquired meteorological and carbon flux data from a diverse set (N = 18) of eddy covariance (EC) flux towers, along with MODIS data on FPAR (the fraction of incident photosynthetically active radiation that is absorbed by the plant canopy) at the sites, and used the data to develop a parameter set for the application of a diagnostic carbon cycle model over North America. The parameter optimization approach relied on goodness of fit between model simulations and tower estimates of gross primary production and net ecosystem production (NEP). Parameters such as light use efficiency (LUE) and base rate of heterotrophic respiration varied widely between sites representing different plant functional types (PFTs), thus supporting the value of stratification by PFT when parameterizing the model. Where multiple EC sites were available within a PFT, overall prediction error and bias in mean NEP was reduced by cross-site optimization as opposed to reliance on a single site. Optimization with the MODIS Enhanced Vegetation Index (EVI) instead of MODIS FPAR resulted in a similar goodness of fit, however, LUE values were pushed to levels that were not physiologically realistic when using EVI. The increasing availability of gap-filled EC tower data is rapidly improving the opportunities for direct coupling of satellite and ground observational data for parameterizing of diagnostic carbon flux models.     

The relative importance of light-use efficiency modifications from environmental conditions and cultivation for estimation of large-scale net primary productivity

Understanding spatial and temporal variation in net primary production (NPP), the amount of carbon fixed into biomass by vegetation, is a central goal of ecosystem ecologists. Optical remote sensing techniques can help address this need by providing accurate, consistent, and reliable approximations of photosynthetic activity at large scales. However, converting photosynthetic activity into NPP requires estimates of light-use efficiency, which has been shown to vary among vegetation types. In this study, we compare remotely sensed estimates of absorbed photosynthetically active radiation with ground-based NPP estimates to determine appropriate light-use efficiency values for grasslands and croplands. We contrast the performance of models with and without information about vegetation type and light-use efficiency downregulation due to unfavorable environmental conditions. Our results suggest that: 1) current models may include overestimates of grassland light-use efficiency; 2) including vegetation information in light-use efficiency calculations causes a dramatically better fit between ground-based and remotely sensed estimates of primary production; and 3) incorporating environmental downregulation to light-use efficiency yields only minor improvements, which may be a result specific to annual estimates in grassland and cropland systems. In addition, this study presents a regional dataset of ground-based primary production estimates that may prove useful for future studies.