Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review

• Published in: ISPRS journal of photogrammetry and remote sensing Vol. 159; pp. 364 - 377
• Main Authors: Gao, Lin, Wang, Xiaofei, Johnson, Brian Alan, Tian, Qingjiu, Wang, Yu, Verrelst, Jochem, Mu, Xihan, Gu, Xingfa
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier B.V 01.01.2020
• Subjects: algorithms; ecology; forestry; fractional vegetation cover; monitoring; Normalized difference vegetation index; phenotype; Remote sensing; Review; soil; Spectral unmixing; systematic review; Vegetation fractional cover; Normalized difference vegetation index; Review; Vegetation fractional cover; Remote sensing; Spectral unmixing
• ISSN: 0924-2716; 1872-8235
• DOI: 10.1016/j.isprsjprs.2019.11.018

Green fractional vegetation cover (fc) is an important phenotypic factor in the fields of agriculture, forestry, and ecology. Spatially explicit monitoring of fc via relative vegetation abundance (RA) algorithms, especially those based on scaled maximum/minimum vegetation index (VI) values, has been widely investigated in remote sensing research. Although many studies have explored the effectiveness of RA algorithms over the past 30 years, a literature review summarizing the corresponding theoretical background, issues, current state-of-the-art techniques, challenges, and prospects has not yet been published. The overall objective of the present study was to accomplish a comprehensive and systematic review of RA algorithms considering these factors based on the scientific papers published from January 1990 to November 2019. This review revealed that the key issues related to RA algorithms is the determination of the appropriate normalized difference vegetation index (NDVI) values of the full vegetation cover and bare soil (denoted hereafter by NDVI∞ and NDVIs, respectively). The existing methods used to correct for these issues were investigated, and their advantages and disadvantages are discussed in depth. In literature trends, we found that the number of reported studies in which RA algorithms were used has increased consistently over time, and that most authors tend to utilize the linear NDVI model, rather than other models in the RA algorithm family. We also found that RA algorithms have been utilized to analyze the images with spatial resolutions ranging from the sub-meter to kilometer, most commonly, using images of 30-m spatial resolution. Finally, current challenges and forward-looking insights in remote estimation of fc using RA algorithms are discussed to guide future research and directions.