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 Anderson, René van der Wal, Brage Bremset Hansen

Research output: Contribution to journalLetterpeer-review

40 Citations (Scopus)

Abstract

Efforts to estimate plant productivity using satellite data can be frustrated by the presence of cloudcover. We developed a new method to overcome this problem, focussing on the high-arcticarchipelago of Svalbard where extensive cloud cover during the growing season can prevent plantproductivity 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-freeMODIS-Normalised Difference Vegetation Index (NDVI) data set (2000−2014) to estimate, on apixel-by-pixel basis, the onset of plant growth. We then summed NDVI values from onset of spring tothe average time of peak NDVI to give an estimate of annual plant productivity. This remotely sensedproductivity measure was then compared, at two different spatial scales, with the peak plant biomassfield data. At both the local scale, surrounding the field data site, and the larger regional scale, ourNDVI measure was found to predict plant biomass (adjusted R2 = 0.51 and 0.44, respectively). Thecommonly used ‘maximum NDVI’ plant productivity index showed no relationship with plantbiomass, likely due to some years having very few cloud-free images available during the peak plantgrowing season. Thus, we propose this new summed NDVI from onset of spring to time of peakNDVI as a proxy of large-scale plant productivity for regions such as the Arctic where climaticconditions restrict the availability of cloud-free images.
Original languageEnglish
Article number025011
JournalEnvironmental Research Letters
Volume13
Publication statusPrint publication - 14 Feb 2018
Externally publishedYes

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