One of the main challenges in land degradation assessment is that a rigorous and systematic approach to addressing its complex dynamics is still missing. The development and application of operative tools at regional and global scales remain a challenge. Land degradation is usually defined as a long-term decline in ecosystem function and productivity. Due to its temporal and spatial resolution as well as data availability, the use of time series of spectral vegetation indexes obtained from satellite sensors has become frequent in recent studies in this field. Slope of linear trends of the normalized difference vegetation index is usually considered an accurate indicator and is widely used as a proxy for land degradation. Yet this method is built on a number of simplifying conceptual and methodological assumptions that prevent capturing more complex dynamics, such as cyclic or periodic behaviors. Our aim was to examine the limitations associated with using linear normalized difference vegetation index trends as proxies for land degradation by comparing outcomes with an alternative methodological procedure based on wavelet autoregressive methods. We explored these issues in 5 case studies from Africa and South America. We observed that trend explained a marginal portion of total temporal variability, whereas monotonic functions, such as linear trends, were unable to capture dynamics that were non-unidirectional, resulting in misinterpretation of actual trends. Wavelet autoregressive method results were encouraging as a step towards the application of more accurate methods to provide sound scientific information of land degradation and restoration.