Identifying multiple stressors in regional agro-ecosystems based on sentinel-2 spectral indices time series

Meiling Liu; Xiangnan Liu; Yuanyuan Meng; Andrew K. Skidmore; Tiejun Wang

doi:10.1109/IGARSS.2018.8651434

Identifying multiple stressors in regional agro-ecosystems based on sentinel-2 spectral indices time series

Meiling Liu^*, Xiangnan Liu, Yuanyuan Meng, Andrew K. Skidmore, Tiejun Wang

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

Abstract

The purpose of this study focused on integrating spectral indices with spatio-temporal characteristics to identify multi-stressor in crops. The experimental areas are located in Dongting Lake (DL), Hunan Province, China. Multitemporal Sentinel-2 (S2) images in 2016, 2017 were collected. Red-edge chlorophyll index (CIred-edge), rededge position (REP), normalized difference red-edge 2 (NDRE2) were calculated. The coefficients of spatiotemporal variation (CSTV) from spectral indices allowed us to discriminate crops exposed to pollution from heavy metal as well as environmental stressors. The results indicated that three indices were good indicators for identifying different environmental stressor in agriculture ecosystem. Crops under heavy metal stress remained stable with lower CSTV values, while crop `hot spots' (with greater CSTV values) were affected by abrupt stressors (i.e., pest and disease, drought) at some growth stage. It concluded that spectral indices and spatio-temporal characteristics show promise for monitoring crops with various stressors.

Original language	English
Title of host publication	2018 IEEE International Geoscience and Remote Sensing Symposium
Subtitle of host publication	proceedings
Place of Publication	Piscataway, New Jersey
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	9335-9338
Number of pages	4
ISBN (Electronic)	9781538671504, 9781538671498
ISBN (Print)	9781538671511
DOIs	https://doi.org/10.1109/IGARSS.2018.8651434
Publication status	Published - 2018
Event	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 - Valencia, Spain Duration: 22 Jul 2018 → 27 Jul 2018

Conference

Conference	38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018
Country/Territory	Spain
City	Valencia
Period	22/07/18 → 27/07/18

Keywords

Abrupt
Heavy metal pollution
Sentinel-2 images
Spatio-temporal characteristics Stable stress

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10.1109/IGARSS.2018.8651434

Cite this

Liu, M., Liu, X., Meng, Y., Skidmore, A. K., & Wang, T. (2018). Identifying multiple stressors in regional agro-ecosystems based on sentinel-2 spectral indices time series. In 2018 IEEE International Geoscience and Remote Sensing Symposium: proceedings (pp. 9335-9338). [8651434] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/IGARSS.2018.8651434

@inproceedings{e2aec5e4ad5140958b080610a9776778,

title = "Identifying multiple stressors in regional agro-ecosystems based on sentinel-2 spectral indices time series",

abstract = "The purpose of this study focused on integrating spectral indices with spatio-temporal characteristics to identify multi-stressor in crops. The experimental areas are located in Dongting Lake (DL), Hunan Province, China. Multitemporal Sentinel-2 (S2) images in 2016, 2017 were collected. Red-edge chlorophyll index (CIred-edge), rededge position (REP), normalized difference red-edge 2 (NDRE2) were calculated. The coefficients of spatiotemporal variation (CSTV) from spectral indices allowed us to discriminate crops exposed to pollution from heavy metal as well as environmental stressors. The results indicated that three indices were good indicators for identifying different environmental stressor in agriculture ecosystem. Crops under heavy metal stress remained stable with lower CSTV values, while crop `hot spots' (with greater CSTV values) were affected by abrupt stressors (i.e., pest and disease, drought) at some growth stage. It concluded that spectral indices and spatio-temporal characteristics show promise for monitoring crops with various stressors.",

keywords = "Abrupt, Heavy metal pollution, Sentinel-2 images, Spatio-temporal characteristics Stable stress",

author = "Meiling Liu and Xiangnan Liu and Yuanyuan Meng and Skidmore, {Andrew K.} and Tiejun Wang",

year = "2018",

doi = "10.1109/IGARSS.2018.8651434",

language = "English",

isbn = "9781538671511",

pages = "9335--9338",

booktitle = "2018 IEEE International Geoscience and Remote Sensing Symposium",

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

address = "United States",

note = "38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018 ; Conference date: 22-07-2018 Through 27-07-2018",

}

Liu, M, Liu, X, Meng, Y, Skidmore, AK & Wang, T 2018, Identifying multiple stressors in regional agro-ecosystems based on sentinel-2 spectral indices time series. in 2018 IEEE International Geoscience and Remote Sensing Symposium: proceedings., 8651434, Institute of Electrical and Electronics Engineers (IEEE), Piscataway, New Jersey, pp. 9335-9338, 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018, Valencia, Spain, 22/07/18. https://doi.org/10.1109/IGARSS.2018.8651434

Identifying multiple stressors in regional agro-ecosystems based on sentinel-2 spectral indices time series. / Liu, Meiling; Liu, Xiangnan; Meng, Yuanyuan et al.
2018 IEEE International Geoscience and Remote Sensing Symposium: proceedings. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (IEEE), 2018. p. 9335-9338 8651434.

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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T1 - Identifying multiple stressors in regional agro-ecosystems based on sentinel-2 spectral indices time series

AU - Liu, Meiling

AU - Liu, Xiangnan

AU - Meng, Yuanyuan

AU - Skidmore, Andrew K.

AU - Wang, Tiejun

PY - 2018

Y1 - 2018

N2 - The purpose of this study focused on integrating spectral indices with spatio-temporal characteristics to identify multi-stressor in crops. The experimental areas are located in Dongting Lake (DL), Hunan Province, China. Multitemporal Sentinel-2 (S2) images in 2016, 2017 were collected. Red-edge chlorophyll index (CIred-edge), rededge position (REP), normalized difference red-edge 2 (NDRE2) were calculated. The coefficients of spatiotemporal variation (CSTV) from spectral indices allowed us to discriminate crops exposed to pollution from heavy metal as well as environmental stressors. The results indicated that three indices were good indicators for identifying different environmental stressor in agriculture ecosystem. Crops under heavy metal stress remained stable with lower CSTV values, while crop `hot spots' (with greater CSTV values) were affected by abrupt stressors (i.e., pest and disease, drought) at some growth stage. It concluded that spectral indices and spatio-temporal characteristics show promise for monitoring crops with various stressors.

AB - The purpose of this study focused on integrating spectral indices with spatio-temporal characteristics to identify multi-stressor in crops. The experimental areas are located in Dongting Lake (DL), Hunan Province, China. Multitemporal Sentinel-2 (S2) images in 2016, 2017 were collected. Red-edge chlorophyll index (CIred-edge), rededge position (REP), normalized difference red-edge 2 (NDRE2) were calculated. The coefficients of spatiotemporal variation (CSTV) from spectral indices allowed us to discriminate crops exposed to pollution from heavy metal as well as environmental stressors. The results indicated that three indices were good indicators for identifying different environmental stressor in agriculture ecosystem. Crops under heavy metal stress remained stable with lower CSTV values, while crop `hot spots' (with greater CSTV values) were affected by abrupt stressors (i.e., pest and disease, drought) at some growth stage. It concluded that spectral indices and spatio-temporal characteristics show promise for monitoring crops with various stressors.

KW - Abrupt

KW - Heavy metal pollution

KW - Sentinel-2 images

KW - Spatio-temporal characteristics Stable stress

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AN - SCOPUS:85063124142

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EP - 9338

BT - 2018 IEEE International Geoscience and Remote Sensing Symposium

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, New Jersey

T2 - 38th Annual IEEE International Geoscience and Remote Sensing Symposium, IGARSS 2018

Y2 - 22 July 2018 through 27 July 2018

ER -

Liu M, Liu X, Meng Y, Skidmore AK, Wang T. Identifying multiple stressors in regional agro-ecosystems based on sentinel-2 spectral indices time series. In 2018 IEEE International Geoscience and Remote Sensing Symposium: proceedings. Piscataway, New Jersey: Institute of Electrical and Electronics Engineers (IEEE). 2018. p. 9335-9338. 8651434 doi: 10.1109/IGARSS.2018.8651434

Identifying multiple stressors in regional agro-ecosystems based on sentinel-2 spectral indices time series

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