The MODIS (The Moderate Resolution Imaging Spectroradiometer) yearly NPP (Net Primary Production) 1 km resolution products were collected over Estonia for years 2000-2010. The MODIS NPP product for forest pixels showed a clear West-East decreasing trend over the Estonian territory. At the same time the trunk volume increment estimates extracted from the Estonian national statistics averaged over the same period showed the opposite trend. The MODIS NPP algorithm seems to overestimate the contribution of meteorological variables and to ignore the role of soil fertility differences. To improve the predictive power of MODIS algorithm to describe local NPP differences, the local meteorological data with higher spatial resolution should be used as an input in the NPP calculations, whereas the algorithm should be modified by optimizing the input parameters and including parameters of soil fertility into the calculation scheme.
The aim of this study was to compile the leaf area index (LAI) map of a 3×3 km VALERI test site in Järvselja, Estonia. Canopy transmittance measurements of LAI by LAI-2000 Plant Canopy Analyzers and digital cameras supplied with fisheye converters were carried out on 42 elementary sampling units at ground level and at breast height level. The vegetation LAI was estimated as a sum of the tree canopy true green LAI obtained from the inversion of canopy gap fraction data and of the ground vegetation effective LAI. Red channel from SPOT-4 HRV-IR image and airborne lidar data based canopy transmittance were used for up-scaling. The LAI map was compared with standard LAI products from Terra MODIS and ENVISAT MERIS. We found that lidar data based LAI estimate on up-scaled map saturated at high values (LAI > 4.5) compared to the LAI estimates based on SPOT-4 HRV-IR red channel. Validation of MODIS LAI product revealed substantial underestimates of LAI compared to the up-scaled field measurements and rather large random noise. ENVISAT MERIS LAI product was more similar to up-scaled field measurements; however, rather large unexpected random variations exist in its time series.