Abstract

The information on land surface phenology (LSP) was extracted from remote sensing data in many studies. However, few studies have evaluated the impacts of satellite products with different spatial resolutions on LSP extraction over regions with a heterogeneous topography. To bridge this knowledge gap, this study took the Loess Plateau as an example region and employed four types of satellite data with different spatial resolutions (250, 500, and 1000 m MODIS NDVI during the period 2001–2020 and ~10 km GIMMS3g during the period 1982–2015) to investigate the LSP changes that took place. We used the correlation coefficient (r) and root mean square error (RMSE) to evaluate the performances of various satellite products and further analyzed the applicability of the four satellite products. Our results showed that the MODIS-based start of the growing season (SOS) and end of the growing season (EOS) were highly correlated with the ground-observed data with r values of 0.82 and 0.79, respectively (p < 0.01), while the GIMMS3g-based phenology signal performed badly (r < 0.50 and p > 0.05). Spatially, the LSP that was derived from the MODIS products produced more reasonable spatial distributions. The inter-annual averaged MODIS SOS and EOS presented overall advanced and delayed trends during the period 2001–2020, respectively. More than two-thirds of the SOS advances and EOS delays occurred in grasslands, which determined the overall phenological changes across the entire Loess Plateau. However, both inter-annual trends of SOS and EOS derived from the GIMMS3g data were opposite to those seen in the MODIS results. There were no significant differences among the three MODIS datasets (250, 500, and 1000 m) with regard to a bias lower than 2 days, RMSE lower than 1 day, and correlation coefficient greater than 0.95 (p < 0.01). Furthermore, it was found that the phenology that was derived from the data with a 1000 m spatial resolution in the heterogeneous topography regions was feasible. Yet, in forest ecosystems and areas with an accumulated temperature ≥10 °C, the differences in phenological phase between the MODIS products could be amplified.

Highlights

  • Land surface phenology (LSP) has been recognized as one of the most effective indicators of climate change [1,2,3,4] and is closely related to animal migration, gross primary production, and crop productivity [5,6,7]

  • This study first verified the performances of the start of the growing season (SOS) and end of the growing season (EOS) that were produced from the 250 m moderate-resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) and GIMMS3g NDVI against the ground-observed data from 2001 to 2013

  • ~10 km GIMMS3g data during the period 1982–2015, as well as the rate of change in the curvature of the logistic models, this study investigated the applicability and spatial scaling effects of various remote sensing products with different spatial resolutions on phenology extraction in a complex-terrain region

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Summary

Introduction

Land surface phenology (LSP) has been recognized as one of the most effective indicators of climate change [1,2,3,4] and is closely related to animal migration, gross primary production, and crop productivity [5,6,7]. Ground observations usually only reflect the phenological information of Remote Sens. Satellite remote sensing has the potential to continuously observe the variation in vegetation phenology at multiple scales [14,15,16,17]. Phenology has been widely monitored in different types of remote sensing data in attempts to understand the interactions between vegetation and climate change during the past few decades [18,19,20,21,22]. Several sets of freely accessible remote sensing products with different resolutions were released, such as the third generation GIMMS (GIMMS3g)

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