Abstract
Remote sensing provides invaluable insight into the dynamics of vegetation with global coverage and reasonable temporal resolution. Normalized Difference Vegetation Index (NDVI) is widely used to study vegetation greenness, production, phenology and the responses of ecosystems to climate fluctuations. The extended global NDVI3g dataset created by Global Inventory Modeling and Mapping Studies (GIMMS) has an exceptional 32 years temporal coverage. Due to the methodology that was used to create NDVI3g inherent noise and uncertainty is present in the dataset. To evaluate the accuracy and uncertainty of application of NDVI3g at regional scale we used Collection-6 data from the MODerate resolution Imaging Spectroradiometer (MODIS) sensor on board satellite Terra as a reference. After noise filtering, statistical harmonization of the NDVI3g dataset was performed for Central Europe based on MOD13 NDVI. Mean seasonal NDVI profiles, start, end and length of the growing season, magnitude and timing of peak NDVI were calculated from NDVI3g (original, noise filtered and harmonized) and MODIS NDVI and compared with each other. NDVI anomalies were also compared and evaluated using simple climate sensitivity metrics. The results showed that (1) the original NDVI3g has limited applicability in Central Europe, which was also implied by the significant disagreement between the NDVI3g and MODIS NDVI datasets; (2) the harmonization of NDVI3g with MODIS NDVI is promising since the newly created dataset showed improved quality for diverse vegetation metrics. For NDVI anomaly detection NDVI3g showed limited applicability, even after harmonization. Climate–NDVI relationships are not represented well by NDVI3g. The presented results can help researchers to assess the expected quality of the NDVI3g-based studies in Central Europe.
Highlights
We evaluated the overall differences between the Terra/MODerate resolution Imaging Spectroradiometer (MODIS) maximum Normalized Difference Vegetation Index (NDVI) based dataset (Terra NDVIMODIS, aggregated to the NDVI3g grid resolution) and the NDVI3g datasets with different processing levels
We found that during the period of 2000–2013, the overall relationship for the whole study area between NDVI3gO and the NDVIMODIS dataset had an R2 of 0.721, an root mean square error (RMSE) of 0.122, a bias of −0.008 and a mean absolute deviation of 0.095
We presented an in-depth analysis of the quality and suitability of the NDVI3g dataset for the estimation of different vegetation characteristics in Central Europe
Summary
Changes in onset and cessation of the growing season are interacting with the carbon cycle [3,7,8], accurate estimation of plant phenology is essential [9,10,11,12,13]. All of these studies clearly demonstrate the Remote Sens. Global or regional scale studies focusing on changes of plant growth and interaction between climate fluctuations and plant functioning are essential to clarify the role and sensitivity of the terrestrial biosphere under a changing climate [8]. The most commonly used quantity derived from remote sensing data is the Normalized Difference Vegetation
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