Comparing Time-Lapse PhenoCams with Satellite Observations across the Boreal Forest of Quebec, Canada

Abstract

Intercomparison of satellite-derived vegetation phenology is scarce in remote locations because of the limited coverage area and low temporal resolution of field observations. By their reliable near-ground observations and high-frequency data collection, PhenoCams can be a robust tool for intercomparison of land surface phenology derived from satellites. This study aims to investigate the transition dates of black spruce (Picea mariana (Mill.) B.S.P.) phenology by comparing fortnightly the MODIS normalized difference vegetation index (NDVI) and the enhanced vegetation index (EVI) extracted using the Google Earth Engine (GEE) platform with the daily PhenoCam-based green chromatic coordinate (GCC) index. Data were collected from 2016 to 2019 by PhenoCams installed in six mature stands along a latitudinal gradient of the boreal forests of Quebec, Canada. All time series were fitted by double-logistic functions, and the estimated parameters were compared between NDVI, EVI, and GCC. The onset of GCC occurred in the second week of May, whereas the ending of GCC occurred in the last week of September. We demonstrated that GCC was more correlated with EVI (R2 from 0.66 to 0.85) than NDVI (R2 from 0.52 to 0.68). In addition, the onset and ending of phenology were shown to differ by 3.5 and 5.4 days between EVI and GCC, respectively. Larger differences were detected between NDVI and GCC, 17.05 and 26.89 days for the onset and ending, respectively. EVI showed better estimations of the phenological dates than NDVI. This better performance is explained by the higher spectral sensitivity of EVI for multiple canopy leaf layers due to the presence of an additional blue band and an optimized soil factor value. Our study demonstrates that the phenological observations derived from PhenoCam are comparable with the EVI index. We conclude that EVI is more suitable than NDVI to assess phenology in evergreen species of the northern boreal region, where PhenoCam data are not available. The EVI index could be used as a reliable proxy of GCC for monitoring evergreen species phenology in areas with reduced access, or where repeated data collection from remote areas are logistically difficult due to the extreme weather.

Multiscale phenology of seasonally dry tropical forests in an aridity gradient

“…The leaf flushing in the Caatinga SDTF occurs fast and synchronously after the first rains of the rainy season (Alberton et al, 2019;Paloschi et al, 2020), and this pattern was detected similarly in both RS methods. A similar analysis for temperate deciduous vegetation indicates a better correspondence between the SOS and satellite index (Hufkens et al, 2012;Keenan et al, 2014;Klosterman et al, 2014;Zhang et al, 2018) and also for evergreen vegetation (Khare et al, 2022) in those very seasonal ecosystems. The better agreement of TDs from both methods with the SOS is likely related to the fast leaf flushing response in all these markedly seasonal ecosystems (Zhang et al, 2018).…”

“…Land surface phenology (LSP) works with satellite imagery collections from a wide variety of orbital sensors. Among the most commonly applied mechanisms to track long-term leafing trends are the MODIS products (Huete et al, 2002;Khare et al, 2022). The derived time series of vegetation indices (VIs), such as enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI), is the base for calculations of the phenological metrics that define the growing seasons, such as the start of season (SOS), the peak of season (POS), and the end of season (EOS), that in general correspond to the green-up, maturity, and senescence stages of a target vegetation (Zhang et al, 2003;Tuanmu et al, 2010;Berra and Gaulton, 2021).…”

Monitoring nature's calendar from space: Emerging topics in land surface phenology and associated opportunities for science applications

“…Although phenocam image processing technology has made great progress, the inherently spectral limitations of camera sensors should not be overlooked for vegetation phenology detections, especially ground validation for satellite-derived phenology observations. Phenocam has become a promising way for phenological studies in forests, grasslands, and agricultural areas (Baumann et al, 2017;Brown et al, 2017;Keenan et al, 2014;Khare et al, 2021;Nietupski et al, 2021;Ren & Peichl, 2021;Richardson et al, 2012;Song et al, 2022;Tian, Cai, Jin, et al, 2021;Toomey et al, 2015).…”

“…Phenocam has become a promising way for phenological studies in forests, grasslands, and agricultural areas (Baumann et al, 2017 ; Brown et al, 2017 ; Keenan et al, 2014 ; Khare et al, 2021 ; Nietupski et al, 2021 ; Ren & Peichl, 2021 ; Richardson et al, 2012 ; Song et al, 2022 ; Tian, Cai, Jin, et al, 2021 ; Toomey et al, 2015 ). Additionally, phenocam images have been used to derive important information on snowmelt processes (Kim et al, 2021 ; Zheng et al, 2022 ).…”

Comparing Different Spatial Resolutions and Indices for Retrieving Land Surface Phenology for Deciduous Broadleaf Forests

“…Several studies on vegetation phenology retrieval by satellite have been conducted using PhenoCam network observations as ground verification, and their objectivity is increasingly recognized [11,[39][40][41]. However, it is difficult to support a broad scale study with PhenoCam data alone, and previous studies have applied PhenoCam data more to validating satellite phenology [37,40,42] and checking phenology models [39,43].…”