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Determining the start of the growing season from MODIS data in the Indian monsoon region: identifying available data in the rainy season and modeling the varied vegetation growth trajectories

Determining the start of the growing season from MODIS data in the Indian monsoon region: identifying available data in the rainy season and modeling the varied vegetation growth trajectories
Determining the start of the growing season from MODIS data in the Indian monsoon region: identifying available data in the rainy season and modeling the varied vegetation growth trajectories
In the Indian monsoon region, frequent cloud cover in the rainy season results in less valid satellite observations during the vegetation growth period, making it difficult to extract land surface phenology (LSP). Even worse, many valid but humid observations were misidentified as clouds in the MODIS cloud mask, causing severe gaps in the LSP product. Using a refined cloud detection approach to separate clear-sky and cloudy observations, this study found that potentially valid observations during the vegetation growth period could be identified. Furthermore, the varied vegetation growth trajectories cannot be well-fitted by a global curve-fitting approach, but can be modelled by using the locally adjusted cubic-spline capping approach, which performed well for any seasonal patterns. Applying this approach, the start of growing season (SOS) was determined with 9.18% of vegetation growth amplitude between the maximum and minimum NDVI to generate the SOS product (2000–2016). The valid percentage of this regional product largely increased from 29.30% to 69.76% compared with the MCD12Q2 product, and its reliability was approximate to that of deciduous broadleaf forest in North America and Europe. This product could serve as a basis for understanding the response of terrestrial ecosystems to the changing Indian monsoon.
2072-4292
1-16
Shang, Rong
ba18cc2e-a4b2-4ef3-a027-81653f47ab47
Liu, Ronggao
cd0bc5c8-0db1-4396-b926-9808b55a4c96
Xu, Mingzhu
480aeb40-7f85-415d-8d1f-93f37e4c2fca
Liu, Yang
0c25fb75-af47-41e0-985a-1785e4ded9b9
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Ge, Quansheng
4f00dd2c-33ad-40fc-aea2-5403cd71644f
Shang, Rong
ba18cc2e-a4b2-4ef3-a027-81653f47ab47
Liu, Ronggao
cd0bc5c8-0db1-4396-b926-9808b55a4c96
Xu, Mingzhu
480aeb40-7f85-415d-8d1f-93f37e4c2fca
Liu, Yang
0c25fb75-af47-41e0-985a-1785e4ded9b9
Dash, Jadunandan
51468afb-3d56-4d3a-aace-736b63e9fac8
Ge, Quansheng
4f00dd2c-33ad-40fc-aea2-5403cd71644f

Shang, Rong, Liu, Ronggao, Xu, Mingzhu, Liu, Yang, Dash, Jadunandan and Ge, Quansheng (2018) Determining the start of the growing season from MODIS data in the Indian monsoon region: identifying available data in the rainy season and modeling the varied vegetation growth trajectories. Remote Sensing, 10 (122), 1-16. (doi:10.3390/rs10010122).

Record type: Article

Abstract

In the Indian monsoon region, frequent cloud cover in the rainy season results in less valid satellite observations during the vegetation growth period, making it difficult to extract land surface phenology (LSP). Even worse, many valid but humid observations were misidentified as clouds in the MODIS cloud mask, causing severe gaps in the LSP product. Using a refined cloud detection approach to separate clear-sky and cloudy observations, this study found that potentially valid observations during the vegetation growth period could be identified. Furthermore, the varied vegetation growth trajectories cannot be well-fitted by a global curve-fitting approach, but can be modelled by using the locally adjusted cubic-spline capping approach, which performed well for any seasonal patterns. Applying this approach, the start of growing season (SOS) was determined with 9.18% of vegetation growth amplitude between the maximum and minimum NDVI to generate the SOS product (2000–2016). The valid percentage of this regional product largely increased from 29.30% to 69.76% compared with the MCD12Q2 product, and its reliability was approximate to that of deciduous broadleaf forest in North America and Europe. This product could serve as a basis for understanding the response of terrestrial ecosystems to the changing Indian monsoon.

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Accepted/In Press date: 15 January 2018
e-pub ahead of print date: 18 January 2018
Published date: 2018

Identifiers

Local EPrints ID: 417217
URI: http://eprints.soton.ac.uk/id/eprint/417217
ISSN: 2072-4292
PURE UUID: e320cf65-b296-451f-b488-5aaa71fa6ad9
ORCID for Jadunandan Dash: ORCID iD orcid.org/0000-0002-5444-2109

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Date deposited: 25 Jan 2018 17:30
Last modified: 18 Feb 2021 17:01

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Contributors

Author: Rong Shang
Author: Ronggao Liu
Author: Mingzhu Xu
Author: Yang Liu
Author: Jadunandan Dash ORCID iD
Author: Quansheng Ge

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