Assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites

Tracking dryland vegetation phenology under a changing climate is of great concern because dryland ecosystems have broad spatial coverage and are important drivers of global carbon cycles. However, dryland ecosystems often consist of two or more different vegetation types with divergent phenological...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Anna Kato, Kimberly M. Carlson, Tomoaki Miura
Formato:	article
Lenguaje:	EN
Publicado:	Elsevier 2021
Materias:	Remote sensing Vegetation index Phenology Dryland Ecology QH540-549.5
Acceso en línea:	https://doaj.org/article/41e0770dfc944f6686cd109f0175ba25
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:41e0770dfc944f6686cd109f0175ba25
record_format	dspace
spelling	oai:doaj.org-article:41e0770dfc944f6686cd109f0175ba252021-12-01T04:58:24ZAssessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites1470-160X10.1016/j.ecolind.2021.108042https://doaj.org/article/41e0770dfc944f6686cd109f0175ba252021-11-01T00:00:00Zhttp://www.sciencedirect.com/science/article/pii/S1470160X2100707Xhttps://doaj.org/toc/1470-160XTracking dryland vegetation phenology under a changing climate is of great concern because dryland ecosystems have broad spatial coverage and are important drivers of global carbon cycles. However, dryland ecosystems often consist of two or more different vegetation types with divergent phenological patterns and are characterized by relatively low vegetation greenup signal compared to noise present in satellite imagery. Thus, accurately characterizing dryland phenology – including the start, peak, and end of season (SOS, POS, and EOS, respectively) – across large temporal and spatial scales with satellite vegetation indices (VIs) has proven challenging. Working across six dryland flux tower sites in the United States from 2003 to 2014, we asked: 1) How well do satellite VIs explain GPP? 2) How accurately can satellite VIs characterize the number and timing of phenological transition dates? and 3) Why does this accuracy vary across VIs? To address these questions, we used daily GPP data from the FLUXNET2015 dataset. We computed four daily VIs from the Moderate Resolution Imaging Spectrometer (MODIS) sensor including two greenness indices (i.e., NDVI and EVI), and two water-related indices (i.e., Land Surface Water Index (LSWI) and NDVI-LSWI difference). We derived phenological transition dates from spline smoothed VI and GPP time series. We found that all major phenological patterns apparent in GPP time series were present in VI data across sites and VIs. Among the four VIs, EVI explained the most variance in daily GPP (R2 = 0.65). Between 88 and 90% of phenological events found in GPP time series – especially recurring, large magnitude events but also some sporadic, small ephemeral events – were detected from MODIS VI data. The rate of omission, when an event was detected from GPP but not from VI data, was similar across VIs (10–12% of all GPP events). Commissions, when an event was detected from VI but not from GPP data, varied more widely from 18% for NDVI to 30% for LSWI. Transition date accuracy as measured by mean absolute difference (MAD) between GPP- and VI-derived SOS and POS ranged from 13 to 15 days across VIs but was lower for EOS (17–26 days). EOS from LSWI most accurately characterized GPP-derived EOS. This may be because LSWI is sensitive to leaf water content, which declines in certain dryland vegetation even when leaves remain green. Our results highlight the potential for satellite VI-derived metrics to accurately track spatio-temporal variation in the phenological event occurrence and timing in dryland ecosystems.Anna KatoKimberly M. CarlsonTomoaki MiuraElsevierarticleRemote sensingVegetation indexPhenologyDrylandEcologyQH540-549.5ENEcological Indicators, Vol 130, Iss , Pp 108042- (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Remote sensing Vegetation index Phenology Dryland Ecology QH540-549.5
spellingShingle	Remote sensing Vegetation index Phenology Dryland Ecology QH540-549.5 Anna Kato Kimberly M. Carlson Tomoaki Miura Assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites
description	Tracking dryland vegetation phenology under a changing climate is of great concern because dryland ecosystems have broad spatial coverage and are important drivers of global carbon cycles. However, dryland ecosystems often consist of two or more different vegetation types with divergent phenological patterns and are characterized by relatively low vegetation greenup signal compared to noise present in satellite imagery. Thus, accurately characterizing dryland phenology – including the start, peak, and end of season (SOS, POS, and EOS, respectively) – across large temporal and spatial scales with satellite vegetation indices (VIs) has proven challenging. Working across six dryland flux tower sites in the United States from 2003 to 2014, we asked: 1) How well do satellite VIs explain GPP? 2) How accurately can satellite VIs characterize the number and timing of phenological transition dates? and 3) Why does this accuracy vary across VIs? To address these questions, we used daily GPP data from the FLUXNET2015 dataset. We computed four daily VIs from the Moderate Resolution Imaging Spectrometer (MODIS) sensor including two greenness indices (i.e., NDVI and EVI), and two water-related indices (i.e., Land Surface Water Index (LSWI) and NDVI-LSWI difference). We derived phenological transition dates from spline smoothed VI and GPP time series. We found that all major phenological patterns apparent in GPP time series were present in VI data across sites and VIs. Among the four VIs, EVI explained the most variance in daily GPP (R2 = 0.65). Between 88 and 90% of phenological events found in GPP time series – especially recurring, large magnitude events but also some sporadic, small ephemeral events – were detected from MODIS VI data. The rate of omission, when an event was detected from GPP but not from VI data, was similar across VIs (10–12% of all GPP events). Commissions, when an event was detected from VI but not from GPP data, varied more widely from 18% for NDVI to 30% for LSWI. Transition date accuracy as measured by mean absolute difference (MAD) between GPP- and VI-derived SOS and POS ranged from 13 to 15 days across VIs but was lower for EOS (17–26 days). EOS from LSWI most accurately characterized GPP-derived EOS. This may be because LSWI is sensitive to leaf water content, which declines in certain dryland vegetation even when leaves remain green. Our results highlight the potential for satellite VI-derived metrics to accurately track spatio-temporal variation in the phenological event occurrence and timing in dryland ecosystems.
format	article
author	Anna Kato Kimberly M. Carlson Tomoaki Miura
author_facet	Anna Kato Kimberly M. Carlson Tomoaki Miura
author_sort	Anna Kato
title	Assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites
title_short	Assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites
title_full	Assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites
title_fullStr	Assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites
title_full_unstemmed	Assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites
title_sort	assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites
publisher	Elsevier
publishDate	2021
url	https://doaj.org/article/41e0770dfc944f6686cd109f0175ba25
work_keys_str_mv	AT annakato assessingtheinterannualvariabilityofvegetationphenologicaleventsobservedfromsatellitevegetationindextimeseriesindrylandsites AT kimberlymcarlson assessingtheinterannualvariabilityofvegetationphenologicaleventsobservedfromsatellitevegetationindextimeseriesindrylandsites AT tomoakimiura assessingtheinterannualvariabilityofvegetationphenologicaleventsobservedfromsatellitevegetationindextimeseriesindrylandsites
_version_	1718405656113840128

Assessing the inter-annual variability of vegetation phenological events observed from satellite vegetation index time series in dryland sites

Ejemplares similares