Fine-Scale Phenology of Urban Trees From Satellite Image Time Series: Toward a Comprehensive Analysis of Influencing Factors

Abstract

While satellite time series are essential tools to derive phenometrics at unprecedented spatial and temporal scales, nonsystematic acquisition or medium spatial resolution of available missions is potentially problematic. At the same time, low-cost observation networks bridge the gap between satellite and in situ observations, which considerably increases ground-based data and associated possibilities. Here, we provide robust statistics about the reliability of satellite-derived phenometrics of urban trees across phenophases. Environmental and acquisition factors influencing the quality of phenometric estimates were analyzed. First, a multifacet regression-based analysis was conducted to measure discrepancies between PlanetScope (and Sentinel-2) and ground-based measurements across phenophases. Second, we performed hierarchical partitioning to tackle the effects of biological parameters (canopy closure and color leaf) for assessing phenometrics with satellite time series. Third, we ran Monte Carlo simulations to propagate errors according to viewing angles in PlanetScope acquisition. Our results show that: 1) PlanetScope provides consistent phenometric estimates for different tree layouts belonging to the same species (average $R^{2}$ = 0.50$\pm$0.18); performances are higher than those of Sentinel-2 but duration-based phenometrics estimates were poorly reconstructed with both satellite missions; 2) contributions of biological parameters in the vegetation signal above trees strongly vary between growth periods; while canopy closure drives the growing season signal (independent contribution $>$40%), color leaf plays a major role in the senescence season; and 3) variable viewing angles in PlanetScope acquisitions showed only significant effects on duration-based metrics estimates. Our research opens new perspectives for monitoring urban trees, which improves the measurement of ecosystem services for local inhabitants.

Keywords

• Discrepancies
• high spatial resolution
• phenology
• PlanetScope (PS)
• satellite image time series (SITS)
• Sentinel-2 (S2)