Remote sensing of terrestrial chlorophyll content

Dash, Jadunandan; Curran, Paul J.; Foody, Giles M.

doi:10.1007/978-3-540-78209-4_5

Cited by 4 publications

(3 citation statements)

References 80 publications

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“…MTCI has limited sensitivity to atmospheric effects and also soil background and view angle (Dash et al, 2008) and with the availability of near real-time weekly and global MTCI composites enables researchers to extract phenological variables accurately. This research evaluates the potential of MTCI to extract phenological variables and monitor change in phenological variables over the last five years in India.…”

Section: Mtcimentioning

confidence: 99%

The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India

Dash

Jeganathan

Atkinson

2010

Remote Sensing of Environment

132

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Section: Mtcimentioning

confidence: 99%

The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India

Dash

Jeganathan

Atkinson

2010

Remote Sensing of Environment

132

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“…It is an essential variable in monitoring the terrestrial carbon budget by supporting an accurate estimate of primary productivities [3,4] used as an input parameter for biosphere models to predict carbon and water changes [5], and light use efficiency [6]. Information on the amount and distribution of CCC has been utilized to answer many ecological questions related to monitoring and evaluating terrestrial ecosystem properties such as identifying types of vegetation, mapping vegetation cover, and understanding the condition of vegetation [7]. Changes in CCC indicate the effects of disease, nutritional, and environmental stresses [8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes

et al. 2020

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Accurate measurement of canopy chlorophyll content (CCC) is essential for the understanding of terrestrial ecosystem dynamics through monitoring and evaluating properties such as carbon and water flux, productivity, light use efficiency as well as nutritional and environmental stresses. Information on the amount and distribution of CCC helps to assess and report biodiversity indicators related to ecosystem processes and functional aspects. Therefore, measuring CCC continuously and globally from earth observation data is critical to monitor the status of the biosphere. However, generic and robust methods for regional and global mapping of CCC are not well defined. This study aimed at examining the spatiotemporal consistency and scalability of selected methods for CCC mapping across biomes. Four methods (i.e., radiative transfer models (RTMs) inversion using a look-up table (LUT), the biophysical processor approach integrated into the Sentinel application platform (SNAP toolbox), simple ratio vegetation index (SRVI), and partial least square regression (PLSR)) were evaluated. Similarities and differences among CCC products generated by applying the four methods on actual Sentinel-2 data in four biomes (temperate forest, tropical forest, wetland, and Arctic tundra) were examined by computing statistical measures and spatiotemporal consistency pairwise comparisons. Pairwise comparison of CCC predictions by the selected methods demonstrated strong agreement. The highest correlation (R2 = 0.93, RMSE = 0.4371 g/m2) was obtained between CCC predictions of PROSAIL inversion by LUT and SNAP toolbox approach in a wetland when a single Sentinel-2 image was used. However, when time-series data were used, it was PROSAIL inversion against SRVI (R2 = 0.88, RMSE = 0.19) that showed greatest similarity to the single date predictions (R2 = 0.83, RMSE = 0.17 g/m2) in this biome. Generally, the CCC products obtained using the SNAP toolbox approach resulted in a systematic over/under-estimation of CCC. RTMs inversion by LUT (INFORM and PROSAIL) resulted in a non-biased, spatiotemporally consistent prediction of CCC with a range closer to expectations. Therefore, the RTM inversion using LUT approaches particularly, INFORM for ‘forest’ and PROSAIL for ‘short vegetation’ ecosystems, are recommended for CCC mapping from Sentinel-2 data for worldwide mapping of CCC. Additional validation of the two RTMs with field data of CCC across biomes is required in the future.

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“…Boyd et al, (2011) (Dash and Curran, 2004;Dash et al, 2010) in studying vegetation phenology in the UK and recommended use of MTCI for phenological studies mainly due to its sensitivity to canopy chlorophyll content.. Thus, MTCI is related directly to canopy chlorophyll content, a function of chlorophyll concentration and leaf area index (LAI) and, therefore, is a useful proxy for the canopy physical and chemical alterations associated with phenological change (Dash et al, 2008).…”

Section: Introductionmentioning

confidence: 99%

Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series

Khwarahm

Dash

Skjøth

et al. 2017

Science of The Total Environment

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Grass and birch pollen are two major causes of seasonal allergic rhinitis (hay fever) in the UK and parts of Europe affecting around 15-20% of the population. Current prediction of these allergens in the UK is based on (i) measurements of pollen concentrations at a limited number of monitoring stations across the country and (ii) general information about the phenological status of the vegetation. Thus, the current prediction methodology provides information at a coarse spatial resolution only. Most station-based approaches take into account only local observations of flowering, while only a small number of approaches take into account remote observations of land surface phenology. The systematic gathering of detailed information about vegetation status nationwide would therefore be of great potential utility. In particular, there exists an opportunity to use remote sensing to estimate phenological variables that are related to the flowering phenophase and, thus, pollen release. In turn, these estimates can be used to predict pollen release at a fine spatial resolution. In this study, time-series of MERIS Terrestrial Chlorophyll Index (MTCI) data were used to predict two key phenological variables: the start of season and peak of season. A technique was then developed to estimate the flowering phenophase of birch and grass from the MTCI time-series. For birch, the timing of flowering was defined as the time after the start of the growing season when the MTCI value reached 25% of the maximum. Similarly, for grass this was defined as the time when the MTCI value reached 75% of the maximum. The predicted pollen release dates were validated with data from nine pollen monitoring stations in the UK. For both birch and grass, we obtained large positive correlations between the MTCI-derived start of pollen season and the start of the pollen season defined using station data, with a slightly larger correlation observed for birch than for grass. The technique was applied to produce detailed maps for the flowering of birch and grass across the UK for each of the years from 2003 to 2010. The results demonstrate that the remote sensing-based maps of onset flowering of birch and grass for the UK together with the pollen forecast from the Meteorology Office and National Pollen and Aerobiology Research Unit (NPARU) can potentially provide more accurate information to pollen allergy sufferers in the UK.

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Remote sensing of terrestrial chlorophyll content

Cited by 4 publications

References 80 publications

The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India

The use of MERIS Terrestrial Chlorophyll Index to study spatio-temporal variation in vegetation phenology over India

Evaluating Prediction Models for Mapping Canopy Chlorophyll Content Across Biomes

Mapping the birch and grass pollen seasons in the UK using satellite sensor time-series

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