Land surface phenology (LSP) has been extensively explored from global archives of
satellite observations to track and monitor the seasonality of rangeland ecosystems in response
to climate change. Long term monitoring of LSP provides large potential for the evaluation of
interactions and feedbacks between climate and vegetation. With a special focus on the rangeland
ecosystems, the paper reviews the progress, challenges and emerging opportunities in LSP while
identifying possible gaps that could be explored in future. Specifically, the paper traces the evolution
of satellite sensors and interrogates their properties as well as the associated indices and algorithms
in estimating and monitoring LSP in productive rangelands. Findings from the literature revealed
that the spectral characteristics of the early satellite sensors such as Landsat, AVHRR and MODIS
played a critical role in the development of spectral vegetation indices that have been widely used in
LSP applications. The normalized difference vegetation index (NDVI) pioneered LSP investigations,
and most other spectral vegetation indices were primarily developed to address the weaknesses and
shortcomings of the NDVI. New indices continue to be developed based on recent sensors such as
Sentinel-2 that are characterized by unique spectral signatures and fine spatial resolutions, and their
successful usage is catalyzed with the development of cutting-edge algorithms for modeling the
LSP profiles. In this regard, the paper has documented several LSP algorithms that are designed to
provide data smoothing, gap filling and LSP metrics retrieval methods in a single environment
