Hyperspectral Applications to Landscape Phenology

Authored by: Alfredo Huete , Werapong Koedsin , Jin Wu

Advanced Applications in Remote Sensing of Agricultural Crops and Natural Vegetation

Print publication date:  December  2018
Online publication date:  December  2018

Print ISBN: 9781138364769
eBook ISBN: 9780429431166
Adobe ISBN:

10.1201/9780429431166-7

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Abstract

Phenology is the study of annual recurring biological life cycle events and the drivers and controls of their periodicity. Shifts in phenology depict a plant's integrated response to climate and environmental changes and have become an important source of information on how plants are responding to climate change. Satellite data, with its synoptic views, repetitive sampling, and high spectral resolution offer numerous opportunities to advance the study of phenology. Thus far, satellite products have primarily contributed to coarse scale studies of “landscape phenology,” defined as the aggregate seasonal vegetation patterns sensed by satellites. Investigations of hyperspectral vegetation phenology are very limited and have yet to be exploited, yet phenologic life cycle events, such as flowering, leaf onset, and litterfall, can be quite dramatic visually, and will alter canopy optical properties. In this chapter, we review current knowledge of what is known about phenology optical signals at leaf, canopy, and landscape scales; we provide an overview of current and potential hyperspectral applications to assess life cycle events and determine phenophases; and we discuss the challenges and limitations of hyperspectral sensing in phenology applications. Hyperspectral applications covered include species detections based on unique phenology curves; optimal phenophases for species discrimination; and the spatiotemporal duality of phenological data, in which both climate as well as changes in species composition influence phenology. The key challenge is to integrate hyperspectral and finer spatial resolution data into phenology characterizations in order to resolve species phenology mixing. We conclude that hyperspectral data are key to advancing phenology science.

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