Evaluating the Performance of Satellite-Derived Vegetation Indices in Gross Primary Productivity (GPP) Estimation at 30 m and 500 m Spatial Resolution

Vegetation indices (VIs) have been extensively employed as proxies for gross primary productivity (GPP). However, it is unclear how the spatial resolution effects the performance of VIs in GPP estimation in different biomes when matching the flux tower footprint. Here, we examined the relationship with tower GPP between Landsat-footprint VIs and MODIS-footprint VIs. We first calculated Landsat-footprint VIs (e.g., Normalized Difference Vegetation Index (NDVI), enhanced vegetation index (EVI), two-band EVI (EVI2), near-infrared reflectance of vegetation (NIRv) and kernel Normalized Difference Vegetation Index (kNDVI)) averaged over all the pixels within the footprint and MODIS-footprint VIs with 3 × 3 km or 1 × 1 km around the tower, respectively. We then examined the relationship between Landsat- and MODIS-footprint VIs and tower GPP at monthly scale over 76 FLUXNET sites across ten vegetation types worldwide. The results showed that Landsat-footprint VIs had stronger performance than MODIS-footprint VIs for GPP estimation in all ecosystems, with high improvement on croplands, wetlands, and grasslands and moderate improvements on mixed forest, evergreen needleleaf forest, and deciduous broadleaf forest. Moreover, NIRv showed a stronger correlation with tower-based GPP than NDVI, EVI, EVI2, and kNDVI in ten ecosystems both at 30 m and 500 spatial resolutions. Our findings highlighted the critical role of VIs with high spatial resolution and footprint-aware matching in GPP estimation.