Earth Observation for Phenological Metrics (EO4PM): Temporal Discriminant to Characterize Forest Ecosystems

Abstract

The study of vegetation phenology has great relevance in many fields since the importance of knowing timing and shifts in periodic plant life cycle events to face the consequences of global changes in issues such as crop production, forest management, ecosystem disturbances, and human health. The availability of high spatial resolution and dense revisit time satellite observations, such as Sentinel-2 satellites, allows high resolution phenological metrics to be estimated, able to provide key information from time series and to discriminate vegetation typologies. This paper presents an automated and transferable procedure that combines validated methodologies based on local curve fitting and local derivatives to exploit full satellite Earth observation time series to produce information about plant phenology. Multivariate statistical analysis is performed for the purpose of demonstrating the capacity of the generated smoothed vegetation curve, temporal statistics, and phenological metrics to serve as temporal discriminants to detect forest ecosystems processes responses to environmental gradients. The results show smoothed vegetation curve and temporal statistics able to highlight seasonal gradient and leaf type characteristics to discriminate forest types, with additional information about forest and leaf productivity provided by temporal statistics analysis. Furthermore, temporal, altitudinal, and latitudinal gradients are obtained from phenological metrics analysis, which also allows to associate temporal gradient with specific phenophases that support forest types distinction. This study highlights the importance of integrated data and methodologies to support the processes of vegetation recognition and monitoring activities.

Cropland Mapping Using Earth Observation Derived Phenological Metrics

“…Multi-temporal information has been demonstrated to increase the crop type classification's accuracy significantly [7]. In the context of crop type mapping and the monitoring of agricultural practices, synthesizing information to fewer phenological metrics facilitates image data processing by reducing the time series' dimensionality [18].…”

“…The biophysical processor [16] available in SNAP software was used to estimate the LAI from the surface reflectance data. The time series of the vegetation indices were first gap-filled and interpolated daily using the Stinemann algorithm [17], and later temporally smoothed using the procedure based on second-order weighted polynomial fitting and Whittaker smoothing, as described in [18]. From the NDVI and LAI time series, temporal statistics and phenological metrics, derived following Gu et al [19], were calculated and used as temporal predictors in the classification model (Table 1).…”

Correcting Systematic Errors by Hybrid 2D Correlation Loss Functions in nonlinear inverse modelling

“…Potential applications of curve fitting are countless and encompass virtually all scientific disciplines. Examples include biosynthesis, 6 thermoluminescence, 7 solar energy, 8 materials science and technology, 9 agriculture, 10 cancer research, 11 kinetics, 12 thermal engineering, 13 transportation, 14 soil science, 15 remote sensing of ecosystems, 16 epidemiology, 17 power and energy engineering, 18 population growth 19 and spectroscopy, 20 to name just a few. The disagreement metrics to minimize during the fit depends on the properties of the noise and possibly on prior information on the parameters to fit.…”

Editorial for the Special Issue “Advances of Remote Sensing in the Analysis of the Spatial and Temporal Variability of Land Surface”

“…Filipponi et al [12] focused on the fine-tuning of an automated and transferable procedure combining robust and validated statistical methodologies to exploit satellite Sentinel-2 time series. The aim of the work was to provide information about plant phenology, as a crucial discipline for supporting crop and forest management and evaluating the responses of ecosystems to global changes.…”