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

Abstract

Vegetation phenology has been viewed as the nature's calendar and an integrative indicator of plant-climate interactions. The correct representation of vegetation phenology is important for models to accurately simulate the exchange of carbon, water, and energy between the vegetated land surface and the atmosphere. Remote sensing has advanced the monitoring of vegetation phenology by providing spatially and temporally continuous data that together with conventional ground observations offers a unique contribution to our knowledge about the environmental impact on ecosystems as well as the ecological adaptations and feedback to global climate change. Land surface phenology (LSP) is defined as the use of satellites to monitor seasonal dynamics in vegetated land surfaces and to estimate phenological transition dates. LSP, as an interdisciplinary subject among remote sensing, ecology, and biometeorology, has undergone rapid development over the past few decades. Recent advances in sensor technologies, as well as data fusion techniques, have enabled novel phenology retrieval algorithms that refine phenology details at even higher spatiotemporal resolutions, providing new insights into ecosystem dynamics. As such, here we summarize the recent advances in LSP and the associated opportunities for science applications. We focus on the remaining challenges, promising techniques, and emerging topics that together we believe will truly form the very frontier of the global LSP research field.

Four ways to define the growing season

“…The visible colour change often reflects the stochastic occurrence of a first frosty night and hence, leads to an overestimation of the significance attributed to colour change (Keskitalo et al, 2005; Körner & Basler, 2010; Tang et al, 2016), with key developmental processes completed quite a while before. Delayed autumnal leaf colouration has often been assumed to add to seasonal productivity (e.g., Ma et al, 2022; Piao et al, 2008; Yang et al, 2017), while the signals obtained from either eddy‐covariance flux towers or models using remote sensing data reflect ongoing CO 2 exchange by green foliage only (e.g., Keenan et al, 2014), such data cannot be used to infer annual (lasting) C sequestration, a rather popular inference.…”

Continued spring phenological advance under global warming hiatus over the Pan-Third Pole

“…To our best knowledge, this is the first study focused on the spring phenology over the Pan-Third Pole, and provided satellite-based evidence about how spring phenology changes and their controls, especially during the warming hiatus. Although we detect generally consistent significant advancing trends in spring phenology based on both NDVI3g and NDVIm datasets, there are still large uncertainties in the magnitude and/or sign of trends between different products ( Peng et al., 2017 ; Moon et al., 2021 ; Ma et al., 2022 ). These inconsistencies could stem from the following factors: biological meaning (leaf emergence or plant photosynthesis), extraction methods ( Cong et al., 2012 ), spatial and temporal resolution (different levels of mixed pixel effect, and different observation frequency) ( Zhang et al., 2003 ; Zhang et al., 2009 ; Melaas et al., 2013 ; Shen et al., 2014 ; Tian et al., 2020 ; Tian et al., 2021 ), the BRDF effect (solar illumination angle and satellite view angle) ( Morton et al., 2014 ; Ma et al., 2019 ; Petri and Galvao, 2019 ; Ma et al., 2020 ; Norris and Walker, 2020 ; Lu et al., 2022 ), and effects due to atmospheric (aerosols, clouds, and hazes) ( Chen et al., 2004 ; Cai et al., 2017 ) or snow ( Wang et al., 2013 ).…”

A phenology of fear: Investigating scale and seasonality in predator–prey games between wolves and white‐tailed deer

“…We thus see little reason to expect the variation here is anomalous: multi‐year behavioral studies at specific times of year often exhibit little interannual variation (Cusack et al, 2020). Seasonality is globally widespread, there is increased recognition that it is better defined by biologically relevant measures (e.g., land‐surface phenology) than with strict ordinal dates, and quantifying variation in the duration and timing of seasonal indicators plays a critical role in understanding the impacts of broader global change (Ma et al, 2022). We pose that many landscapes of fear exhibit a broadly predictable phenology related to the timing of precipitation, snow, vegetation growth, and other dynamic environmental attributes, and that these phenologies of fear may themselves be sensitive to global change.…”