Many remote sensing indices have been developed for land surface phenology monitoring, but the ability of different remote sensing indices to represent the seasonal changes of land surface vegetation differs. At present, the evaluations of remote sensing indices for land surface phenology monitoring are mostly conducted under different standards, which results in poor comparability among research results. Thus, the best remote sensing indices cannot be selected according to different regions based on the aforementioned research results, which would affect the large-scale (e.g., hemispheric and even global) land surface phenology monitoring. Taking 406 records from 75 carbon flux tower stations and 482 records from 129 phenological camera stations as the reference standard, this study systematically evaluated the application of 10 remote sensing indexes in monitoring land surface phenology in the middle and high latitudes of the northern hemisphere. In addition, the best remote sensing indices and their accuracy under different conditions were compared and analyzed from two evaluation perspectives (including phenological extraction accuracy and phenological change trend consistency) and four dimensions (including vegetation type, geographical environment, phenological type, and phenological event).Although some remote sensing indices are the best in most conditions, the best remote sensing indices for different vegetation types, geographical environment, phenological types (functional phenology, structural phenology), and phenological events (spring and autumn) do not focus on a few of remote sensing indices but are scattered among all kinds of them. Even with the best remote sensing index, the error of land surface phenology monitoring is still large in some conditions. From different evaluation perspectives, the remote sensing indices with a high accuracy of phenology extraction are not exactly the same as those with a high consistency of phenological change trend, which suggests that the best remote sensing index should be selected according to the objects. The results of this study can provide the best remote sensing index selection basis for land surface phenology monitoring under different conditions, which will be helpful to improve the accuracy of large-scale land surface phenology monitoring and evaluate its uncertainty.

remote sensing index;land surface phenology;vegetation type;geographical environment;structural phenology;functional phenology