Evaluation of the consistency of long-term NDVI time series derived from AVHRR,SPOT-vegetation, SeaWiFS, MODIS, and Landsat ETM+ sensors

By Molly E. Brown, Jorge E. Pinzón, K. Didan, J. Morisette, Compton J. Tucker. IEEE Transactions on Geoscience and Remote Sensing, 2006

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Spring Normalized Difference Vegetation Index (NDVI) predicts annual variation in timing of peak faecal crude protein in mountain ungulates (2009, result): “We then re-fitted the cubic spline smoother to obtain a new estimate of peak date in FCP.”
Land cover 2.0 (2018, methodology): “…empirical manner, for example, by computation of spectral vegetation indices derived from di ﬀ erent sensors, and/or by applying statistical transformations between similar bands of di ﬀ erent sensors (Brown et al. 2006; Miura, Huete, and Yoshioka 2006; Roy et al. 2016c; Zhang et al. In review).”
Global Data Sets of Vegetation Leaf Area Index (LAI)3g and Fraction of Photosynthetically Active Radiation (FPAR)3g Derived from Global Inventory Modeling and Mapping Studies (GIMMS) Normalized Difference Vegetation Index (NDVI3g) for the Period 1981 to 2011 (2013, methodology): “LAI and FPAR are therefore key state variables in many biogeochemical, ecological, hydrological and crop yield models [3–17].”