Abstract
Abstract. Land surface phenology (LSP) is a kind of vital information for land cover classification and vegetation growth monitoring. Time series Landsat images, with the advantages of long observations and high spatial resolution, have been widely used in LSP identification. However, LSP transaction dates, such as start of season (SOS) and end of season (EOS), are highly influenced by the coarse temporal resolution. In this study, we compare the inter-annual difference of LSP SOS from 5 years interval, 10 years interval and all years interval Landsat images, and improve the SOS estimated model by considering the accumulated growing degree-days (AGDD) of soil temperature and soil moisture. Results indicate that LSP SOS can serve as a good proxy for reflecting ground vegetation phenology, especially using 5 years interval Landsat images. Soil temperature and soil moisture have certain influence on SOS estimation, and the R-squared value reached 0.9 after model adjustment. This study can provide guidance for estimating suitable inter-annual LSP transaction dates under different sceneries in the future.
Highlights
Land surface phenology (LSP) is a kind of vital information for land cover classification and vegetation growth monitoring, reflecting the changes in terrestrial ecosystems and climate
LSP start of season (SOS) results between 40° and 41° latitude was later than SOS between 41° and 42° in Open Shrublands, Woody Savannas, Savannas, and Permanent Wetlands
Mean absolute difference (MAD) results in 5-10 years interval in Closed Shrublands during 41° and 42° had the minimum value with 1.65 days
Summary
Land surface phenology (LSP) is a kind of vital information for land cover classification and vegetation growth monitoring, reflecting the changes in terrestrial ecosystems and climate. Zhang et al, (2020) proposed a new algorithm of LSP product at a 30-meter gridded spatial resolution fusing the operational harmonized Landsat and Sentinel-2 (HLS) products and VIIRS surface reflectance products during 2016 and 2018. Due to the coarse temporal resolution (16-day revisit cycles), yearly fulfil requirement images are often insufficient to support the LSP identification such as start of season (SOS) and end of season (EOS), especially under cloud, rain or other bad weather conditions. To overcome this limitation, several approaches have been development to improve the temporal resolution and keep the high spatial resolution at the same time.
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