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Time series of Normalized Difference Vegetation Index (NDVI) derived from multiple satellite sensors are crucial data to study vegetation dynamics. The Land Long Term Data Record Version 4 (LTDR V4) NDVI dataset was recently released at a 0.05 × 0.05° spatial resolution and daily temporal resolution. In this study, annual NDVI time series that are composited by the LTDR V4 and Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI datasets (MOD13C1) are compared and evaluated for the period from 2001 to 2014 in China. The spatial patterns of the NDVI generally match between the LTDR V4 and MOD13C1 datasets. The transitional zone between high and low NDVI values generally matches the boundary of semi-arid and sub-humid regions. A significant and high coefficient of determination is found between the two datasets according to a pixel-based correlation analysis. The spatially averaged NDVI of LTDR V4 is characterized by a much weaker positive regression slope relative to that of the spatially averaged NDVI of the MOD13C1 dataset because of changes in NOAA AVHRR sensors between 2005 and 2006. The measured NDVI values of LTDR V4 were always higher than that of MOD13C1 in western China due to the relatively lower atmospheric water vapor content in western China, and opposite observation appeared in eastern China. In total, 18.54% of the LTDR V4 NDVI pixels exhibit significant trends, whereas 35.79% of the MOD13C1 NDVI pixels show significant trends. Good agreement is observed between the significant trends of the two datasets in the Northeast Plain, Bohai Economic Rim, Loess Plateau, and Yangtze River Delta. By contrast, the datasets contrasted in northwestern desert regions and southern China. A trend analysis of the regression slope values according to the vegetation type shows good agreement between the LTDR V4 and MOD13C1 datasets. This study demonstrates the spatial and temporal consistencies and discrepancies between the AVHRR LTDR and MODIS MOD13C1 NDVI products in China, which could provide useful information for the choice of NDVI products in subsequent studies of vegetation dynamics. [ABSTRACT FROM AUTHOR]

Vegetation is an important part of terrestrial ecosystems. Although vegetation dynamics have explicit spatial and temporal dimensions, the study of the temporal process is in its infancy. Evaluation of temporal scaling behavior could provide a unique perspective for exploring the temporal nature of vegetation dynamics. In this study, the Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI) was used to reflect vegetation dynamics, and the temporal scaling behavior of the NDVI in China was determined via detrended fluctuation analysis (DFA). Our main objectives were to reveal the temporal scaling behavior of NDVI time series and to understand variation among vegetation types. First, DFA revealed similar exponents, which ranged from 0.6 to 0.9, for all selected pixels, implying that a long-range correlation was generally present in the NDVI time series at the individual pixel scale. We then extended the analysis to all of China and found that 99.30% of the pixel exponents ranged from 0.5 to 1. These results suggest that the NDVI time series displays strong long-range correlation throughout most of China; however, the exponents exhibited regional variability. To explain these differences, we further analyzed the exponents for 12 vegetation types based on a vegetation map of China. All of the vegetation types exhibited well-defined long-range correlation, with exponents ranging from 0.7189 to 0.8436. For all vegetation types, the maximum and average value and standard deviation of the exponents decreased with increasing annual maximum NDVI values, suggesting that low vegetation density is much more sensitive to external factors. These findings may be useful for understanding vegetation dynamics as a complex, temporally varying phenomenon. [ABSTRACT FROM AUTHOR]