| Summary: | Detailed information from global remote sensing has greatly advanced ourunderstanding of Earth as a system in general and of agricultural processes in particular.Vegetation monitoring with global remote sensing systems over long time periods iscritical to gain a better understanding of processes related to agricultural change over longtime periods. This specifically relates to sub-humid to semi-arid ecosystems, whereagricultural change in grazing lands can only be detected based on long time series. Byintegrating data from different sensors it is theoretically possible to construct NDVI timeseries back to the early 1980s. However, such integration is hampered by uncertainties inthe comparability between different sensor products. To be able to rely on vegetationtrends derived from integrated time series it is therefore crucial to investigate whether vegetation trends derived from NDVI and phenological parameters are consistent acrossproducts. In this paper we analyzed several indicators of vegetation change for a range ofagricultural systems in Inner Mongolia, China, and compared the results across differentsatellite archives. Specifically, we compared two of the prime NDVI archives—AVHRR Global Inventory Modeling and Mapping Studies (GIMMS) and SPOT Vegetation (VGT)NDVI. Because a true accuracy assessment of long time series is not possible, we furthercompared SPOT VGT NDVI with NDVI from MODIS Terra as a benchmark. We foundhigh similarities in interannual trends, and also in trends of the seasonal amplitude andintegral between SPOT VGT and MODIS Terra (r > 0.9). However, we observedconsiderable disagreements in NDVI-derived trends between AVHRR GIMMS and SPOTVGT. We detected similar discrepancies for trends based on phenological parameters, suchas amplitude and integral of NDVI curves corresponding to seasonal vegetation cycles.Inconsistencies were partially related to land cover and vegetation density. Differentpre-processing schemes and the coarser spatial resolution of AVHRR GIMMS introducedfurther uncertainties. Our results corroborate findings from other studies that vegetationtrends derived from AVHRR GIMMS data not always reflect true vegetation changes. Amore thorough understanding of the factors introducing uncertainties in AVHRR GIMMStime series is needed, and we caution against using AVHRR GIMMS data in regionalstudies without applying regional sensitivity analyses.
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