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

• Main Authors: Manuela Balzarolo, Josep Peñuelas, Frank Veroustraete
• Format: Article
• Language: English
• Published: MDPI AG 2019-07-01
• Series: Remote Sensing
• Subjects: FLUXNET; plant functional types; upscaling NDVI; Fourier model fitting; seasonality; phenology
• Online Access: https://www.mdpi.com/2072-4292/11/14/1656

The objective of this paper was to evaluate the use of in situ normalized difference vegetation index (NDVI<i>_is</i>) and Moderate Resolution Imaging Spectroradiometer NDVI (NDVI<i>_MD)</i> 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 &#215; 0.5 km and 3.5 &#215; 3.5 km) centered at flux tower location. The goodness of the relationship between NDVI<i>_is</i> and NDVI<i>_MD</i> 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 &#215; 0.5 km). Both NDVI<i>_is</i> and NDVI<i>_MD</i> 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.