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Influence of Landscape Heterogeneity and Spatial Resolution in Multi-Temporal In Situ and MODIS NDVI Data Proxies for Seasonal GPP Dynamics

1
CSIC, Global Ecology Unit CREAF-CSIC-UAB, 08913 Cerdanyola del Vallès, Catalonia, Spain
2
CREAF, 08913 Cerdanyola del Vallès, Catalonia, Spain
3
Department of Bioscience Engineering, University of Antwerp, Groenenborgerlaan 171, B-2020 Antwerpen, Belgium
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(14), 1656; https://doi.org/10.3390/rs11141656
Received: 24 April 2019 / Revised: 8 July 2019 / Accepted: 9 July 2019 / Published: 11 July 2019
(This article belongs to the Special Issue Land Surface Phenology)
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Abstract

The objective of this paper was to evaluate the use of in situ normalized difference vegetation index (NDVIis) and Moderate Resolution Imaging Spectroradiometer NDVI (NDVIMD) time series data as proxies for ecosystem gross primary productivity (GPP) to improve GPP upscaling. We used GPP flux data from 21 global FLUXNET sites across main global biomes (forest, grassland, and cropland) and derived MODIS NDVI at contrasting spatial resolutions (between 0.5 × 0.5 km and 3.5 × 3.5 km) centered at flux tower location. The goodness of the relationship between NDVIis and NDVIMD varied across biomes, sites, and MODIS spatial resolutions. We found a strong relationship with a low variability across sites and within year variability in deciduous broadleaf forests and a poor correlation in evergreen forests. Best performances were obtained for the highest spatial resolution at 0.5 × 0.5 km). Both NDVIis and NDVIMD elicited roughly three weeks later the starting of the growing season compared to GPP data. Our results confirm that to improve the accuracy of upscaling in situ data of site GPP seasonal responses, in situ radiation measurement biomes should use larger field of view to sense an area, or more sensors should be placed in the flux footprint area to allow optimal match with satellite sensor pixel size. View Full-Text
Keywords: FLUXNET; plant functional types; upscaling NDVI; Fourier model fitting; seasonality; phenology FLUXNET; plant functional types; upscaling NDVI; Fourier model fitting; seasonality; phenology
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Balzarolo, M.; Peñuelas, J.; Veroustraete, F. Influence of Landscape Heterogeneity and Spatial Resolution in Multi-Temporal In Situ and MODIS NDVI Data Proxies for Seasonal GPP Dynamics. Remote Sens. 2019, 11, 1656.

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