A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high-arctic plant productivity

Stein Rune Karlsen, Helen B. Anderson, Rene Van der Wal, Brage Bremset Hansen

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Abstract

Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas. We used a field-based time-series (2000-2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-NDVI data set (2000-2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R<sup>2</sup> = 0.51 and 0.44, respectively). The commonly used 'maximum NDVI' plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images.
Original languageEnglish
Article number025011
JournalEnvironmental Research Letters
Volume13
Issue number2
Early online date6 Dec 2017
DOIs
Publication statusPublished - 14 Feb 2018

Fingerprint

NDVI
annual variation
Productivity
productivity
Biomass
biomass
Pixels
cloud cover
pixel
growing season
Svalbard
annual plant
Time series
vascular plant
Availability
Satellites
MODIS
archipelago
Proxy
satellite data

Keywords

  • cloud cover
  • plant productivity
  • MODIS
  • NDVI
  • growing degree days
  • Svalbard
  • High Arctic
  • variability

Cite this

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title = "A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high-arctic plant productivity",
abstract = "Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas. We used a field-based time-series (2000-2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-NDVI data set (2000-2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R<sup>2</sup> = 0.51 and 0.44, respectively). The commonly used 'maximum NDVI' plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images.",
keywords = "cloud cover, plant productivity, MODIS, NDVI, growing degree days, Svalbard, High Arctic, variability",
author = "Karlsen, {Stein Rune} and Anderson, {Helen B.} and {Van der Wal}, Rene and Hansen, {Brage Bremset}",
note = "Processing of the MODIS dataset for monitoring the onset of the growing season was, in part, funded by the Environmental Monitoring of Svalbard and Jan Mayen (MOSJ). We are grateful to senior advisor John Richard Hansen, our contact person at the Norwegian Polar Institute in MOSJ, for his support, and to the members of the Arctic Biomass project (Research Council of Norway [RCN], grant 227064/E10) for valuable discussion about the NDVI – biomass relationships. Additional funding was provided by the RCN through the projects ‘Predicting effects of climate change on Svalbard reindeer population dynamics: a mechanistic approach’ (grant 216051) and ‘SnoEco’ (grant 230970), the ESA PRODEX project ‘Sentinel-2 for High North Vegetation Phenology’ (grant 4000110654), and the Svalbard Environmental Protection Funded project ’Effects of climate change on plant productivity’ (grant 15/28).",
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journal = "Environmental Research Letters",
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T1 - A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high-arctic plant productivity

AU - Karlsen, Stein Rune

AU - Anderson, Helen B.

AU - Van der Wal, Rene

AU - Hansen, Brage Bremset

N1 - Processing of the MODIS dataset for monitoring the onset of the growing season was, in part, funded by the Environmental Monitoring of Svalbard and Jan Mayen (MOSJ). We are grateful to senior advisor John Richard Hansen, our contact person at the Norwegian Polar Institute in MOSJ, for his support, and to the members of the Arctic Biomass project (Research Council of Norway [RCN], grant 227064/E10) for valuable discussion about the NDVI – biomass relationships. Additional funding was provided by the RCN through the projects ‘Predicting effects of climate change on Svalbard reindeer population dynamics: a mechanistic approach’ (grant 216051) and ‘SnoEco’ (grant 230970), the ESA PRODEX project ‘Sentinel-2 for High North Vegetation Phenology’ (grant 4000110654), and the Svalbard Environmental Protection Funded project ’Effects of climate change on plant productivity’ (grant 15/28).

PY - 2018/2/14

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N2 - Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas. We used a field-based time-series (2000-2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-NDVI data set (2000-2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R<sup>2</sup> = 0.51 and 0.44, respectively). The commonly used 'maximum NDVI' plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images.

AB - Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloud cover. We developed a new method to overcome this problem, focussing on the high-arctic archipelago of Svalbard where extensive cloud cover during the growing season can prevent plant productivity from being estimated over large areas. We used a field-based time-series (2000-2009) of live aboveground vascular plant biomass data and a recently processed cloud-free MODIS-NDVI data set (2000-2014) to estimate, on a pixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring to the average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensed productivity measure was then compared, at two different spatial scales, with the peak plant biomass field data. At both the local scale, surrounding the field data site, and the larger regional scale, our NDVI measure was found to predict plant biomass (adjusted R<sup>2</sup> = 0.51 and 0.44, respectively). The commonly used 'maximum NDVI' plant productivity index showed no relationship with plant biomass, likely due to some years having very few cloud-free images available during the peak plant growing season. Thus, we propose this new summed NDVI from onset of spring to time of peak NDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climatic conditions restrict the availability of cloud-free images.

KW - cloud cover

KW - plant productivity

KW - MODIS

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KW - growing degree days

KW - Svalbard

KW - High Arctic

KW - variability

U2 - 10.1088/1748-9326/aa9f75

DO - 10.1088/1748-9326/aa9f75

M3 - Article

VL - 13

JO - Environmental Research Letters

JF - Environmental Research Letters

SN - 1748-9326

IS - 2

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