Potential of using surface temperature data to benchmark Sentinel-2-based forest phenometrics in boreal Finland

Titta Majasalmi*, Miina Rautiainen

*Tämän työn vastaava kirjoittaja

Tutkimustuotos: LehtiartikkeliArticleScientificvertaisarvioitu

16 Lataukset (Pure)

Abstrakti

Key message: We present a new approach to calibrate timings of phenological events from satellite data (e.g., Sentinel-2 MSI data) with readily available surface temperature data. The new approach improves the estimation of growing season length in boreal forests. Context: Satellite data is used to calibrate phenology models employed in land surface model components of climate models. However, realistic quantification of forest phenological transitions, such as the greenup and senescence, across large spatial scales remains challenging due to the lack of sufficient ground validation data representative of both forest tree canopy and forest understory species compositions. Aims: The aim of this study was to develop a new approach to benchmark boreal forest land surface phenology obtained from Sentinel-2 (S2) against surface temperature data. Methods: We computed S2 phenological transition dates and compared them to ground reference data on temperature from a network of meteorological stations across Finland (60–70N°). Results: Our results showed that applying standard phenometrics directly to S2 data to estimate the growing season length in boreal forests may lead to clear biases in all species groups. Conclusion: Our approach to use temperature data to calibrate boreal forest phenometrics allows flexible application across spatial scales (i.e., point or grid) and different satellite sensors. It can be combined with any vegetation land cover product to provide a link between surface temperature data and forest seasonal reflectance properties.

AlkuperäiskieliEnglanti
Artikkeli6
Sivumäärä17
JulkaisuAnnals of Forest Science
Vuosikerta79
Numero1
DOI - pysyväislinkit
TilaJulkaistu - jouluk. 2022
OKM-julkaisutyyppiA1 Julkaistu artikkeli, soviteltu

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