“Blend-then-index” or “index-then-blend”: A theoretical analysis for generating high-resolution NDVI time series by STARFM

Xuehong Chen, Meng Liu, Xiaolin Zhu, Jin Chen, Yanfei Zhong, Xin Cao

Research output: Journal article publicationJournal articleAcademic researchpeer-review

17 Citations (Scopus)

Abstract

There are two strategies for generating high-resolution vegetation index time series using spatiotemporal data blending methods, named as “Blend-then-Index” (BI) and “Indexthen- Blend” (IB), according to the order of vegetation index calculation and data blending. This study aims to determine which strategy can obtain better results for generating a high-resolution normalized difference vegetation index (NDVI) time series using the spatial and temporal adaptive reflectance fusion model (STARFM). The theoretical error analysis suggests that the more accurate strategy depends on the vegetation growth stages: BI has a smaller error than IB when the NDVI values at the prediction date are higher than the input NDVI values and vice versa. Simulated experiments using Landsat images were conducted to verify the theoretical analysis. This study provides guidelines for producing better high-resolution vegetation index time series using STARFM.
Original languageEnglish
Pages (from-to)65-73
Number of pages9
JournalPhotogrammetric Engineering and Remote Sensing
Volume84
Issue number2
DOIs
Publication statusPublished - 1 Feb 2018

ASJC Scopus subject areas

  • Computers in Earth Sciences

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