Abstract
As an important land-surface parameter, vegetation phenology has been estimated from observations by various satellite-borne sensors with substantially different spatial resolutions, ranging from tens of meters to several kilometers. The inconsistency of satellite-derived phenological metrics (e.g., green-up date, GUD, also known as the land-surface spring phenology) among different spatial resolutions, which is referred to as the “scale effect” on GUD, has been recognized in previous studies, but it still needs further efforts to explore the cause of the scale effect on GUD and to quantify the scale effect mechanistically. To address these issues, we performed mathematical analyses and designed up-scaling experiments. We found that the scale effect on GUD is not only related to the heterogeneity of GUD among fine pixels within a coarse pixel, but it is also greatly affected by the covariation between the GUD and vegetation growth speed of fine pixels. GUD of a coarse pixel tends to be closer to that of fine pixels with earlier green-up and higher vegetation growth speed. Therefore, GUD of the coarse pixel is earlier than the average of GUD of fine pixels, if the growth speed is a constant. However, GUD of the coarse pixel could be later than the average from fine pixels, depending on the proportion of fine pixels with later GUD and higher growth speed. Based on those mechanisms, we proposed a model that accounted for the effects of heterogeneity of GUD and its co-variation with growth speed, which explained about 60% of the scale effect, suggesting that the model can help convert GUD estimated at different spatial scales. Our study provides new mechanistic explanations of the scale effect on GUD.
Highlights
Land-surface phenology, reflecting the seasonality of vegetated land surface detected by remotely sensed imagery, has attracted increasing attention in recent decades, as it provides an independent, long-term, globally sensed measure for assessing ecosystem responses to climate change [1,2,3]
Previous studies have found that the value of a phenological metric at coarse resolution is not necessarily equal to the average of the metric at fine resolution for the same footprint, which is known as the “scale effect” [5,6]
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Summary
Land-surface phenology, reflecting the seasonality of vegetated land surface detected by remotely sensed imagery, has attracted increasing attention in recent decades, as it provides an independent, long-term, globally sensed measure for assessing ecosystem responses to climate change [1,2,3]. Significant progress has been made to detect phenology metrics, the green-up date (GUD), based on vegetation index (VI) time-series, there is still considerable inconsistency in the detected land-surface phenology among different sensors [3,4,5], posing challenges in the precise quantification of vegetation phenological changes and their responses to climate change at a large scale. The inconsistency may be caused by differences in imaging condition, spectral response functions of sensors and geometric registration. It can be caused by the difference of spatial resolutions of employing VI, data because sensors with different spatial resolutions observe vegetation at various scales, ranging from individual species to complex landscapes that consist of various vegetation types and phenological timings. Understanding how the scale effect influences land-surface phenology detection is becoming a fundamental point for cross-scale comparisons and validation of phenology metrics against field observations [5,6,7]
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