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Land surface phenology retrievals for arid and semi-arid ecosystems

Land surface phenology retrievals for arid and semi-arid ecosystems
Land surface phenology retrievals for arid and semi-arid ecosystems

Land surface phenology (LSP) plays a critical role in the regulation of photosynthesis, evapotranspiration, and energy fluxes. Significant progress has been made in extracting LSP information over large areas using satellite data, yet LSP retrievals remain a challenge over vast arid and semi-arid ecosystems because of sparse greenness, high variability and the lack of distinct annual patterns; for example, the MODerate Imaging Spectrometer (MODIS) Land Cover Dynamics Product MCD12Q2 that provides LSP metrics globally often failed to provide LSP information in these ecosystems. In this study, we used a modified threshold algorithm to extract LSP timing metrics, including the start, peak, and end of growing seasons, using the 16-day composite Enhanced Vegetation Index (EVI) time series from MODIS data. We applied this regionally customized algorithm across all arid and semi-arid climate regions of Australia (75% of the continental land area) encompassing shrublands, grasslands, savannas, woodlands, and croplands, extracting LSP metrics annually from 2003 to 2018, with up to two (phenology) seasons accounted for in each year. Our algorithm yielded an average of 64.9% successful rate of retrieval (proportion of pixels with retrieved LSP metrics) across 16 years in Arid and Semi-arid AUStralia (AS-AUS), which was a significant increase compared to the 14.5% rate of retrieval yielded in our study area by the global product and the major cause of the different performances between these two approaches was the different EVI amplitude restrictions utilized to avoid spurious peaks (i.e. EVI amplitude ≥ 0.1 used by the global product and peak EVI ≥ time series average EVI used by our algorithm). Gross primary productivity (GPP) measurements at OzFlux eddy covariance (EC) tower sites were used to cross-compare with the presence/absence of growing seasons detected by our algorithm, and 97% of our retrieved seasons matched with those extracted using EC data. Preliminary tests at five OzFlux sites showed that our algorithm was robust to view angle-induced sensitivity of the input data and showed similar performance when using EVI data calculated using MODIS Nadir BRDF-Adjusted Reflectance product. Our retrieved LSP metrics revealed that vegetation growth in arid ecosystems is highly irregular and can occur at any time of the year, more than once in a year, or can skip a year. The proportion of pixels with two growing seasons was found to be correlated with the average annual precipitation of the study area (p < 0.01), providing an estimation approach of LSP via rainfall. Our study improves the detection and measurement of vegetation phenology in arid and semi-arid regions by improving the spatial extend of LSP retrievals, which contributes to studies on LSP variations and dryland ecosystem resilience to climate change. More evaluation is planned for future work to assess and further improve the accuracy of the retrieved LSP metrics.

Arid and semi-arid ecosystems, EVI, Gross primary productivity, Land surface phenology, MODIS, TERN OzFlux
0924-2716
129-145
Xie, Qiaoyun
9ade5ac1-856c-4735-98b5-0a14bd954115
Cleverly, Jamie
3f74d015-2190-4c5c-9c23-f323ea1bbfbf
Moore, Caitlin
ad4e3ab4-2a75-465a-8e0d-31d6b9697fbc
Ding, Yangling
6259460f-dd90-4652-be68-9b811de0ba78
Hall, Christopher
079dfbb6-b92f-4ce8-a7e3-64ec3859055f
Ma, Xuanlong
d0194db5-b776-4324-b08e-5969ed7c1f56
Brown, Luke
3f3ee47e-ee1f-4a44-a223-36059b69ce92
Wang, Cong
b0484b33-8ed8-43a6-a762-1363035773fa
Beringer, Jason
0b1ac55c-f892-43fb-8e82-f0e78761954e
Prober, Suzanne
aca4aabd-9e17-42f6-b763-8630dac5b752
Macfarlane, Craig
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Meyer, Wayne
3259da6a-f3be-4cc7-8794-c85f89aa3b34
Yin, Gaofei
f9a780e8-dd68-4e0b-a697-64bbd647067d
Huete, Alfredo
0e13b5c9-b3de-4c1a-b501-59e7a286411e
Xie, Qiaoyun
9ade5ac1-856c-4735-98b5-0a14bd954115
Cleverly, Jamie
3f74d015-2190-4c5c-9c23-f323ea1bbfbf
Moore, Caitlin
ad4e3ab4-2a75-465a-8e0d-31d6b9697fbc
Ding, Yangling
6259460f-dd90-4652-be68-9b811de0ba78
Hall, Christopher
079dfbb6-b92f-4ce8-a7e3-64ec3859055f
Ma, Xuanlong
d0194db5-b776-4324-b08e-5969ed7c1f56
Brown, Luke
3f3ee47e-ee1f-4a44-a223-36059b69ce92
Wang, Cong
b0484b33-8ed8-43a6-a762-1363035773fa
Beringer, Jason
0b1ac55c-f892-43fb-8e82-f0e78761954e
Prober, Suzanne
aca4aabd-9e17-42f6-b763-8630dac5b752
Macfarlane, Craig
79b8e21a-7a5f-48b0-9034-bbdc94e4dcbd
Meyer, Wayne
3259da6a-f3be-4cc7-8794-c85f89aa3b34
Yin, Gaofei
f9a780e8-dd68-4e0b-a697-64bbd647067d
Huete, Alfredo
0e13b5c9-b3de-4c1a-b501-59e7a286411e

Xie, Qiaoyun, Cleverly, Jamie, Moore, Caitlin, Ding, Yangling, Hall, Christopher, Ma, Xuanlong, Brown, Luke, Wang, Cong, Beringer, Jason, Prober, Suzanne, Macfarlane, Craig, Meyer, Wayne, Yin, Gaofei and Huete, Alfredo (2022) Land surface phenology retrievals for arid and semi-arid ecosystems. ISPRS Journal of Photogrammetry and Remote Sensing, 185, 129-145. (doi:10.1016/j.isprsjprs.2022.01.017).

Record type: Article

Abstract

Land surface phenology (LSP) plays a critical role in the regulation of photosynthesis, evapotranspiration, and energy fluxes. Significant progress has been made in extracting LSP information over large areas using satellite data, yet LSP retrievals remain a challenge over vast arid and semi-arid ecosystems because of sparse greenness, high variability and the lack of distinct annual patterns; for example, the MODerate Imaging Spectrometer (MODIS) Land Cover Dynamics Product MCD12Q2 that provides LSP metrics globally often failed to provide LSP information in these ecosystems. In this study, we used a modified threshold algorithm to extract LSP timing metrics, including the start, peak, and end of growing seasons, using the 16-day composite Enhanced Vegetation Index (EVI) time series from MODIS data. We applied this regionally customized algorithm across all arid and semi-arid climate regions of Australia (75% of the continental land area) encompassing shrublands, grasslands, savannas, woodlands, and croplands, extracting LSP metrics annually from 2003 to 2018, with up to two (phenology) seasons accounted for in each year. Our algorithm yielded an average of 64.9% successful rate of retrieval (proportion of pixels with retrieved LSP metrics) across 16 years in Arid and Semi-arid AUStralia (AS-AUS), which was a significant increase compared to the 14.5% rate of retrieval yielded in our study area by the global product and the major cause of the different performances between these two approaches was the different EVI amplitude restrictions utilized to avoid spurious peaks (i.e. EVI amplitude ≥ 0.1 used by the global product and peak EVI ≥ time series average EVI used by our algorithm). Gross primary productivity (GPP) measurements at OzFlux eddy covariance (EC) tower sites were used to cross-compare with the presence/absence of growing seasons detected by our algorithm, and 97% of our retrieved seasons matched with those extracted using EC data. Preliminary tests at five OzFlux sites showed that our algorithm was robust to view angle-induced sensitivity of the input data and showed similar performance when using EVI data calculated using MODIS Nadir BRDF-Adjusted Reflectance product. Our retrieved LSP metrics revealed that vegetation growth in arid ecosystems is highly irregular and can occur at any time of the year, more than once in a year, or can skip a year. The proportion of pixels with two growing seasons was found to be correlated with the average annual precipitation of the study area (p < 0.01), providing an estimation approach of LSP via rainfall. Our study improves the detection and measurement of vegetation phenology in arid and semi-arid regions by improving the spatial extend of LSP retrievals, which contributes to studies on LSP variations and dryland ecosystem resilience to climate change. More evaluation is planned for future work to assess and further improve the accuracy of the retrieved LSP metrics.

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Accepted/In Press date: 21 January 2022
Published date: March 2022
Additional Information: Funding Information: This study was supported by the Terrestrial Ecosystems Research Network (TERN) Australian landscape phenology and vegetation dynamics for climate resilience, ecosystem services, and forecasting project ( CSIRO - C013420 ) and the Australian Research Council 's Discovery Projects funding schemes ( DP170101630 , DP210100347 ), and flux data were supported by the Australian government through TERN Ecosystem Processes. Qiaoyun Xie acknowledges support from University of Technology Sydney Chancellor’s Postdoctoral Research Fellowship. Yanling Ding acknowledges the support from the Fundamental Research Funds for the Central Universities (Project No. 2412020FZ004 ). Xuanlong Ma acknowledges the support from the Fundamental Research Funds for the Central Universities ( lzujbky-2021-ct11 ). Cong Wang acknowledges the support from the National Natural Science Foundation of China (42101391) and the Fundamental Research Funds for the Central Universities (Project No. 30106210182 , CCNU21XJ028 ). We thank the anonymous reviewers for their valuable comments and suggestions. Publisher Copyright: © 2022 International Society for Photogrammetry and Remote Sensing, Inc. (ISPRS)
Keywords: Arid and semi-arid ecosystems, EVI, Gross primary productivity, Land surface phenology, MODIS, TERN OzFlux

Identifiers

Local EPrints ID: 454925
URI: http://eprints.soton.ac.uk/id/eprint/454925
ISSN: 0924-2716
PURE UUID: 4ea054c1-343e-4c22-a25a-8d7d300bb38a
ORCID for Luke Brown: ORCID iD orcid.org/0000-0003-4807-9056

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Date deposited: 01 Mar 2022 17:50
Last modified: 05 Nov 2024 18:40

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Contributors

Author: Qiaoyun Xie
Author: Jamie Cleverly
Author: Caitlin Moore
Author: Yangling Ding
Author: Christopher Hall
Author: Xuanlong Ma
Author: Luke Brown ORCID iD
Author: Cong Wang
Author: Jason Beringer
Author: Suzanne Prober
Author: Craig Macfarlane
Author: Wayne Meyer
Author: Gaofei Yin
Author: Alfredo Huete

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