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

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A new NDVI measure that overcomes data sparsity in cloud-covered regions predicts annual variation in ground-based estimates of high arctic plant productivity
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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-Normalised Difference Vegetation Index (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 2 = 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.
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hello my name is staying the the collagen and together made others in it and they have on the and blogger house we presented publication which deals with ultimate the clap productivity with set the Linked Data to map the plant productivity would set the late date there is a very challenging task in particular in very cold the areas as the worst of the area the IOC because of their high level small by but in this study we presented new rattled which works well also in Oracle DOES the base of this study is a time series of modus Acetylate data scoring small bar and goes back to the year 2000 the simulation shows the seasonal dynamic map by the motor state that we have been using it shows the snow masking pattern and
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OOP and the value and the you field by mistake and we found a very good fit both for the local mosque surrounding the field data as well as for a more regional mask covering most of the known showdown these opium I gave tray fit with a few data compared with peak and the as shown here here we have been mapping the and plant productivity the blue colors indicate less productivity compared with an average year and read a higher than average productivity to summarize for central parts of small bar he found out that time integrated dandily i.e. from the onset of the growing season to the time average peaked and the eye given a very high feet with the the top by mistake the and we recommend all the to use dismantled and got productivity studies in comparable areas so thank you for your attention