Abstract
While satellite-based monitoring of vegetation activity at the earth’s surface is of vital importance for many eco-climatological applications, the degree of agreement among certain sensors and products providing estimates of the Normalized Difference Vegetation Index (NDVI) has been found to vary considerably. In order to assess the extent of such differences in highly heterogeneous terrain, we analyze and compare intra-annual seasonal fluctuations and long-term monotonic trends (2003–2012) in the Kilimanjaro region, Tanzania. The considered NDVI datasets include the Moderate Resolution Imaging Spectroradiometer (MODIS) products from Terra and Aqua, Collections 5 and 6, and the 3rd Generation Global Inventory Modeling and Mapping Studies (GIMMS) product. The degree of agreement in seasonal fluctuations is assessed by calculating a pairwise Index of Association (IOAs), whereas long-term trends are derived from the trend-free pre-whitened Mann–Kendall test. On the seasonal scale, the two Terra-MODIS products (and, accordingly, the two Aqua-MODIS products) are best associated with each other, indicating that the seasonal signal remained largely unaffected by the new Collection 6 calibration approach. On the long-term scale, we find that the negative impacts of band ageing on Terra-MODIS NDVI have been accounted for in Collection 6, which now distinctly outweighs Aqua-MODIS in terms of greening trends. GIMMS NDVI, by contrast, fails to capture small-scale seasonal and trend patterns that are characteristic for the highly fragmented landscape which is likely owing to the coarse spatial resolution. As a short digression, we also demonstrate that the amount of false discoveries in the determined trend fraction is distinctly higher for p < 0.05 ( 52.6 % ) than for p < 0.001 ( 2.2 % ) which should point the way for any future studies focusing on the reliable deduction of long-term monotonic trends.
Highlights
The Normalized Difference Vegetation Index (NDVI) [1] is widely applied in the fields of eco-climatological research to deduce seasonal and long-term vegetation dynamics from remotely sensed imagery
When comparing Terra-Moderate Resolution Imaging Spectroradiometer (MODIS) with Aqua-MODIS products regardless of the particular collection, the determined Index of Association (IOAs) slightly decreases towards an intermediate level
IOAs involving NDVI3g is calculated from the MODIS climate modeling grids (CMG) products (Section 2.2.1), whereas
Summary
The Normalized Difference Vegetation Index (NDVI) [1] is widely applied in the fields of eco-climatological research to deduce seasonal and long-term vegetation dynamics from remotely sensed imagery. Despite its outstanding scientific value to monitor vegetation greenness at the earth’s surface, it became evident that analyzing data from different sensor systems resulted in considerably deviating signals which was attributed to quite a variety of factors. In this context, Alcaraz-Segura et al [2] compared 8-km Advanced Very High Resolution Radiometer (AVHRR). The observed differences were attributed to an inappropriate correction of the satellite drift and to the rather coarse spatial and temporal resolution of the half-monthly GIMMS dataset as compared to the 10-day CCRS product. Fensholt and Proud [6] compared global vegetation trends (2000–2010)
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