remote sensing Article Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation Xueying Li 1,2,3 , Wenquan Zhu 1,2, * , Zhiying Xie 1,2 , Pei Zhan 1,2,4 , Xin Huang 1,2 , Lixin Sun 1,2 and Zheng Duan 3   Citation: Li, X.; Zhu, W.; Xie, Z.; Zhan, P.; Huang, X.; Sun, L.; Duan, Z. Assessing the Effects of Time Interpolation of NDVI Composites on Phenology Trend Estimation. Remote Sens. 2021, 13, 5018. https://doi.org/ 10.3390/rs13245018 Academic Editor: Jianxi Huang Received: 31 October 2021 Accepted: 7 December 2021 Published: 10 December 2021 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). 1 State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Beijing Normal University and Aerospace Information Research Institute of Chinese Academy of Sciences, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; xueying.li@nateko.lu.se (X.L.); xiezy@mail.bnu.edu.cn (Z.X.); peizhan@nuist.edu.cn (P.Z.); huangxin@mail.bnu.edu.cn (X.H.); 201821051190@mail.bnu.edu.cn (L.S.) 2 Beijing Engineering Research Center for Global Land Remote Sensing Products, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China 3 Department of Physical Geography and Ecosystem Science, Lund University, 22362 Lund, Sweden; zheng.duan@nateko.lu.se 4 School of Applied Meteorology, Nanjing University of Information Science & Technology, Nanjing 210044, China * Correspondence: zhuwq75@bnu.edu.cn Abstract: The accurate evaluation of shifts in vegetation phenology is essential for understanding of vegetation responses to climate change. Remote-sensing vegetation index (VI) products with multi-day scales have been widely used for phenology trend estimation. VI composites should be interpolated into a daily scale for extracting phenological metrics, which may not fully capture daily vegetation growth, and how this process affects phenology trend estimation remains unclear. In this study, we chose 120 sites over four vegetation types in the mid-high latitudes of the northern hemisphere, and then a Moderate Resolution Imaging Spectroradiometer (MODIS) MCD43A4 daily surface reflectance data was used to generate a daily normalized difference vegetation index (NDVI) dataset in addition to an 8-day and a 16-day NDVI composite datasets from 2001 to 2019. Five different time interpolation methods (piecewise logistic function, asymmetric Gaussian function, polynomial curve function, linear interpolation, and spline interpolation) and three phenology extraction methods were applied to extract data from the start of the growing season and the end of the growing season. We compared the trends estimated from daily NDVI data with those from NDVI composites among (1) different interpolation methods; (2) different vegetation types; and (3) different combinations of time interpolation methods and phenology extraction methods. We also analyzed the differences between the trends estimated from the 8-day and 16-day composite datasets. Our results indicated that none of the interpolation methods had significant effects on trend estimation over all sites, but the discrepancies caused by time interpolation could not be ignored. Among vegetation types with apparent seasonal changes such as deciduous broadleaf forest, time interpolation had significant effects on phenology trend estimation but almost had no significant effects among vegetation types with weak seasonal changes such as evergreen needleleaf forests. In addition, trends that were estimated based on the same interpolation method but different extraction methods were not consistent in showing significant (insignificant) differences, implying that the selection of extraction methods also affected trend estimation. Compared with other vegetation types, there were generally fewer discrepancies between trends estimated from the 8-day and 16-day dataset in evergreen needleleaf forest and open shrubland, which indicated that the dataset with a lower temporal resolution (16-day) can be applied. These findings could be conducive for analyzing the uncertainties of monitoring vegetation phenology changes. Keywords: vegetation phenology; phenology trend; NDVI composites; time interpolation Remote Sens. 2021, 13, 5018. https://doi.org/10.3390/rs13245018 https://www.mdpi.com/journal/remotesensing