Abstract
Long-term global datasets of the Leaf Area Index (LAI) are important for monitoring global vegetation dynamics and are an important input for Earth system models (ESM). The comparison of long-term datasets is based on two recently available datasets both derived from AVHRR (Advanced Very High Resolution Radiometer) time series. The LAI3g dataset is developed from the new improved third generation Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) from AVHRR sensors and best-quality MODIS LAI data. The second long-term LAI dataset is based on the 8-km spatial resolution GIMMS-AVHRR data (Goettingen GIS & Remote Sensing, GGRS dataset). The GGRS-LAI product uses a satellite-based LAI. This algorithm uses a three-dimensional physical radiative transfer model, which establishes the relationship between LAI, vegetation fractional cover and given patterns of surface reflectance, view-illumination conditions and optical properties of vegetation. The model incorporates a number of site-/region-specific parameters, including the vegetation architecture variables, such as leaf angle distribution, clumping index and light extinction coefficient. For the application of the model to Kazakhstan, the vegetation architecture variables were computed at the local (pixel) level based on extensive field surveys of the biophysical properties of vegetation in representative grassland areas of Kazakhstan. As a main result of our study, we could summarize that the differences between both products are most pronounced at the start and the end of the growing season. During the spring and autumn months, the LAI difference maps showed a considerable difference of LAI GGRS and LAI3g. LAI3g is characterized by a considerably earlier start and a later finish to the growing season than LAI GGRS. Moreover, LAI3g showed LAI > 0 during the winter months when any green vegetation is absent in all land covers of Kazakhstan. A direct cause for this could be a too high base level of the LAI3g during the leafless phase.
Highlights
A major need stated by the NASA Earth science research strategy and European Space Agency (ESA) in environmental research is to develop long-term, consistent and calibrated data and products that are valid across multiple missions and satellite sensors
The global LAI3g product explains 25% of the variability in the reference Leaf Area Index (LAI) estimates at the forest-dominated validation site in Almaty and 44% at the grass-dominated validation site in Shetsky district of Kazakhstan
The variations explained by the global LAI3g product are much lower than by the regional LAI GGRS product (69% and 68% in the forest and grassland validation sites, respectively), detecting a relatively poor performance of LAI3g in the main land cover types of Kazakhstan
Summary
A major need stated by the NASA Earth science research strategy and European Space Agency (ESA) in environmental research is to develop long-term, consistent and calibrated data and products that are valid across multiple missions and satellite sensors. There are no published studies on the validation of any global LAI products in Central Asia Within this scope, an ongoing research work on validation of the existing global LAI products in a grassland region in Central Kazakhstan is of great importance.
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