Monitoring Savanna Vegetation Phenology Using Advanced Himawari Imager

Xuanlong Ma; Ngoc Nguyen Tran; Song Leng; Qiaoyun Xie; Alfredo Huete

doi:10.1109/IGARSS47720.2021.9554251

Monitoring Savanna Vegetation Phenology Using Advanced Himawari Imager

Xuanlong Ma, Ngoc Nguyen Tran, Song Leng, Qiaoyun Xie, Alfredo Huete

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

Abstract

Vegetation phenology represents a key attribute of an ecosystem and plays an important role in regulating terrestrial carbon and water cycles. Here we used observations from the Advanced Himawari Imager (AHI) onboard the new generation Japanese geostationary (GEO) satellite Himawari-8. The objective was to assess the potentials of retrieving savanna phenology from H8/AHI vegetation index time series along a 1100 km ecological rainfall gradient, known as the North Australian Tropical Transect (NATT). Key phenology transition dates (start, peak, end, and length of season) were extracted from H8/AHI Enhanced Vegetation Index (EVI) time series and then compared to those extracted from MODIS EVI. Results showed that H8/AHI with its higher temporal resolution offers several advantages in monitoring savanna vegetation dynamics than MODIS. The denser EVI time series from H8/AHI not only avoids the artefacts caused by data interpolation but also enabled a more certain characterization of seasonal vegetation growth patterns than MODIS. The short lived, rainfall pulse-driven vegetation cycles in dry savannas were also better detected using H8/AHI.

Original language	English
Title of host publication	IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publication	Proceedings
Place of Publication	USA
Publisher	IEEE, Institute of Electrical and Electronics Engineers
Pages	1597-1599
Number of pages	3
ISBN (Print)	978-1-6654-0369-6
DOIs	https://doi.org/10.1109/IGARSS47720.2021.9554251
Publication status	Published - 2021
Externally published	Yes
Event	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 - Brussels, Belgium Duration: 12 Jul 2021 → 16 Jul 2021

Conference

Conference	2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021
Country/Territory	Belgium
City	Brussels
Period	12/07/21 → 16/07/21

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10.1109/IGARSS47720.2021.9554251

Cite this

@inproceedings{c7f4a2d42a234936bee555463148c88b,

title = "Monitoring Savanna Vegetation Phenology Using Advanced Himawari Imager",

abstract = "Vegetation phenology represents a key attribute of an ecosystem and plays an important role in regulating terrestrial carbon and water cycles. Here we used observations from the Advanced Himawari Imager (AHI) onboard the new generation Japanese geostationary (GEO) satellite Himawari-8. The objective was to assess the potentials of retrieving savanna phenology from H8/AHI vegetation index time series along a 1100 km ecological rainfall gradient, known as the North Australian Tropical Transect (NATT). Key phenology transition dates (start, peak, end, and length of season) were extracted from H8/AHI Enhanced Vegetation Index (EVI) time series and then compared to those extracted from MODIS EVI. Results showed that H8/AHI with its higher temporal resolution offers several advantages in monitoring savanna vegetation dynamics than MODIS. The denser EVI time series from H8/AHI not only avoids the artefacts caused by data interpolation but also enabled a more certain characterization of seasonal vegetation growth patterns than MODIS. The short lived, rainfall pulse-driven vegetation cycles in dry savannas were also better detected using H8/AHI.",

keywords = "geostationary, Himawari-8, tropical savannas, vegetation phenology",

author = "Xuanlong Ma and Tran, {Ngoc Nguyen} and Song Leng and Qiaoyun Xie and Alfredo Huete",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE; 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021 ; Conference date: 12-07-2021 Through 16-07-2021",

year = "2021",

doi = "10.1109/IGARSS47720.2021.9554251",

language = "English",

isbn = "978-1-6654-0369-6",

pages = "1597--1599",

booktitle = "IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium",

publisher = "IEEE, Institute of Electrical and Electronics Engineers",

address = "United States",

}

Ma, X, Tran, NN, Leng, S, Xie, Q & Huete, A 2021, Monitoring Savanna Vegetation Phenology Using Advanced Himawari Imager. in IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings. IEEE, Institute of Electrical and Electronics Engineers, USA, pp. 1597-1599, 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021, Brussels, Belgium, 12/07/21. https://doi.org/10.1109/IGARSS47720.2021.9554251

Monitoring Savanna Vegetation Phenology Using Advanced Himawari Imager. / Ma, Xuanlong; Tran, Ngoc Nguyen; Leng, Song et al.
IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium: Proceedings. USA: IEEE, Institute of Electrical and Electronics Engineers, 2021. p. 1597-1599.

Research output: Chapter in Book/Conference paper › Conference paper › peer-review

TY - GEN

T1 - Monitoring Savanna Vegetation Phenology Using Advanced Himawari Imager

AU - Ma, Xuanlong

AU - Tran, Ngoc Nguyen

AU - Leng, Song

AU - Xie, Qiaoyun

AU - Huete, Alfredo

PY - 2021

Y1 - 2021

N2 - Vegetation phenology represents a key attribute of an ecosystem and plays an important role in regulating terrestrial carbon and water cycles. Here we used observations from the Advanced Himawari Imager (AHI) onboard the new generation Japanese geostationary (GEO) satellite Himawari-8. The objective was to assess the potentials of retrieving savanna phenology from H8/AHI vegetation index time series along a 1100 km ecological rainfall gradient, known as the North Australian Tropical Transect (NATT). Key phenology transition dates (start, peak, end, and length of season) were extracted from H8/AHI Enhanced Vegetation Index (EVI) time series and then compared to those extracted from MODIS EVI. Results showed that H8/AHI with its higher temporal resolution offers several advantages in monitoring savanna vegetation dynamics than MODIS. The denser EVI time series from H8/AHI not only avoids the artefacts caused by data interpolation but also enabled a more certain characterization of seasonal vegetation growth patterns than MODIS. The short lived, rainfall pulse-driven vegetation cycles in dry savannas were also better detected using H8/AHI.

AB - Vegetation phenology represents a key attribute of an ecosystem and plays an important role in regulating terrestrial carbon and water cycles. Here we used observations from the Advanced Himawari Imager (AHI) onboard the new generation Japanese geostationary (GEO) satellite Himawari-8. The objective was to assess the potentials of retrieving savanna phenology from H8/AHI vegetation index time series along a 1100 km ecological rainfall gradient, known as the North Australian Tropical Transect (NATT). Key phenology transition dates (start, peak, end, and length of season) were extracted from H8/AHI Enhanced Vegetation Index (EVI) time series and then compared to those extracted from MODIS EVI. Results showed that H8/AHI with its higher temporal resolution offers several advantages in monitoring savanna vegetation dynamics than MODIS. The denser EVI time series from H8/AHI not only avoids the artefacts caused by data interpolation but also enabled a more certain characterization of seasonal vegetation growth patterns than MODIS. The short lived, rainfall pulse-driven vegetation cycles in dry savannas were also better detected using H8/AHI.

KW - geostationary

KW - Himawari-8

KW - tropical savannas

KW - vegetation phenology

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U2 - 10.1109/IGARSS47720.2021.9554251

DO - 10.1109/IGARSS47720.2021.9554251

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SN - 978-1-6654-0369-6

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EP - 1599

BT - IGARSS 2021 - 2021 IEEE International Geoscience and Remote Sensing Symposium

PB - IEEE, Institute of Electrical and Electronics Engineers

CY - USA

T2 - 2021 IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2021

Y2 - 12 July 2021 through 16 July 2021

ER -

Monitoring Savanna Vegetation Phenology Using Advanced Himawari Imager

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