A review of vegetation phenological metrics extraction using time-series, multispectral satellite data

REMOTE SENSING OF ENVIRONMENT

卷:237

Review

Zeng, Linglin¹  Wardlow, Brian D.²  Xiang, Daxiang³  Hu, Shun⁴  Li, Deren⁵ 

[1] Huazhong Agr Univ, Coll Resources & Environm, Wuhan 430070, Peoples R China

[2] Univ Nebraska, Sch Nat Resources, Ctr Adv Land Management Informat Technol, 3310 Holdrege St, Lincoln, NE 68583 USA

[3] Changjiang River Water Resources Commiss, Changjiang River Sci Res Inst, Wuhan, Peoples R China

[4] Wuhan Univ, State Key Lab Water Resources & Hydropower Engn S, Wuhan 430072, Peoples R China

[5] Wuhan Univ, State Key Lab Informat Engn Surveying Mapping & R, 129 Luoyu Rd, Wuhan 430079, Peoples R China

关键词: Land surface phenology;    Specie-specific phenology;    Remote sensing;    Data smoothing;    Phenological metrics extraction;   

DOI  :  10.1016/j.rse.2019.111511

来源: Elsevier

【 摘 要 】

Vegetation dynamics and phenology play an important role in inter-annual vegetation changes in terrestrial ecosystems and are key indicators of climate-vegetation interactions, land use/land cover changes and variation in year-to-year vegetation productivity. Satellite remote sensing data have been widely used for vegetation phenology monitoring over large geographic domains using various types of observations and methods over the past several decades. The goal of this paper is to present a detailed review of existing methods for phonology detection and emerging new techniques based on the analysis of time-series, multispectral remote sensing imagery. This paper summarizes the objective and applications of detecting general vegetation phenology stages (e.g., green onset, time or peak greenness and growing season length) often termed 'land surface phonology', as well as more advanced methods that estimate species-specific phonological stages (e.g., silking stage of maize). Common data processing methods, such as data smoothing, applied to prepare the time-series remote sensing observations to be applied to phenological detection methods are presented. Specific land surface phenology detection methods as well as species-specific phenology detection methods based on multispectral satellite data are then discussed. The impact of different error sources in the data on remote-sensing based phonology detection are also discussed in detail, as well as ways to reduce these uncertainties and errors. Joint analysis of multi-scale observations ranging from satellite to more recent ground-based sensors is helpful for us to understand satellite-based phenology detection mechanism and extent phenology detection to regional scale in the future. Finally, emerging opportunities to further advance remote sensing of phenology is presented that includes observations from Cubesats, near-surface observations such as PhenoCams and image data fusion techniques to improve the spatial resolution of time-series image data sets needed for phenological characterization.

【 授权许可】

Free