Gross primary production simulation in a coniferous forest using a daily gas exchange model with seasonal change of leaf physiological parameters derived from remote sensing data

Abstract

The importance of including the seasonal changes of canopy physiology in carbon balance simulation models was emphasized in earlier research. This paper proposes a new approach to combine the commonly available Normalized Difference Vegetation Index (NDVI) to derive the seasonal changes of canopy physiology with a modified daily step gas exchange model to simulate the gross primary production (GPP) in a coniferous forest stand. For validation, four years (1997)(1998)(1999)(2000) of continuous time-series data for GPP were derived using the observations of net ecosystem carbon dioxide exchange obtained with eddy covariance techniques. A variety of scenarios were designed to simulate the GPP with the daily model and the results (1) reaffirm the importance of including seasonal changes of canopy physiology in the carbon balance model; (2) demonstrate the feasibility of deriving the seasonal change information of canopy physiology from NDVI time-series; (3) show the importance of the quality of NDVI time-series. The results suggest that the daily model can be linked with remote sensing data, which provides information on the seasonal changes of canopy physiology, and can be an efficient tool for regional carbon balance simulation.

Estimating carbon dioxide fluxes from temperate mountain grasslands using broad-band vegetation indices

“…In fact, this approach may hold more promise for reducing the uncertainty of gap-filled data products Moffat et al, 2007). In particular for long data gaps, which represent a large source of uncertainty , or when rapid changes in ecosystem CO 2 exchange occur, such as with the investigated grasslands or during leaf-out of deciduous forests , broad-band vegetation indices may provide a good proxy for changes in ecosystem physiological activity (Wang et al, 2009) and could be assimilated into the gapfilling procedure. An example for this is given in Fig.…”

“…As an alternative, first probably published by Huemmrich et al (1999), reflectance in the broad bands of photosynthetically active (PAR; 400-700 nm) and near-infrared (NIR; 700-3000 nm) may be used to approximate the NDVI and other indices making use of the differential reflectance in these wave band regions. Broad-band NDVI has been successfully used to track phenology (Huemmrich et al, 1999;Wang et al, 2004;, predict the leaf area index (Wilson and Meyers, 2007;Rocha and Shaver, 2009) and GPP (Wang et al, 2004(Wang et al, , 2009Jenkins et al, 2007;. Given that most of the radiation measurements necessary to calculate broad-band vegetation indices, probably with the exception of reflected PAR, are made routinely at the majority of the existing flux towers the potential of broad-band vegetation indices has not yet been fully exploited and, at least to our knowledge, we are not aware of published studies which used broad-band vegetation indices to estimate NEE.…”

A Simple Transformation for Visualizing Non-seasonal Landscape Change From Dense Time Series of Satellite Data

“…Coppin and others [8] labeled this category of methods "temporal trajectory analysis," pointing out that they offer a means of addressing subtler, gradually progressing types of changes. These include, for instance, studies of climate-driven land surface shifts (e.g., [21]- [24]), trends in vegetation phenology (e.g., [25]- [28]), vegetative productivity (e.g., [29], [30]), and changing land-atmosphere exchanges (e.g., [31]- [33]). Research and development of new remote sensing methods for temporal trajectory analysis is increasingly common in the literature, both for Landsat (e.g., [34], [35]) and MODIS (e.g., [36], [37]).…”

Spatial and temporal variations of forest LAI in China during 2000–2010

“…In addition, forest LAI is the key factor influencing carbon sequestration [22], and it directly affects regional or global climate conditions through regulating the partition of sensible and latent heat fluxes [23][24][25]. Therefore, reliable forest LAI data is a prerequisite to improving the reliability of ecological and land surface process models, and reducing uncertainties in carbon source and sink estimates [26][27][28][29].…”