Authors: Xiaoyang Zhang*, , Yongchang Ye, South Dakota State University
Topics: Remote Sensing, Land Use and Land Cover Change
Keywords: time series, satellite data, land surface phenology
Session Type: Paper
Presentation File: No File Uploaded
Polar-orbiting satellites (such as AVHRR, MODIS, and VIIRS) provide daily global coverage, which have been widely applied to detect land surface phenology (LSP) at regional and global scales. However, the cloud-free observations from the polar-orbiting sensors during a year period are generally less than 10% in most regions across the globe. The related time series significantly affects the quality of LSP detections, particularly in the regions with seasonally persistent cloud cover. Recently, the new generation geostationary satellite sensors, including the Advanced Baseline Imager (ABI) onboard Operational Environmental Satellite (GOES) systems (GOES-16/17) and the Advanced Himawari Imager (AHI) onboard the Himawari-8/9, have become operational. The ABI and AHI provide observations with an interval of 5-10 minutes although the spatial resolution, which is 500 m in red reflectance and 1000 m near infrared, and shortwave near infrared reflectance, is relatively coarser. The diurnal ABI and AHI observations offer a unique opportunity to reduce cloud contaminations in a daily time series during a year. This study calculates diurnal variation in the enhanced vegetation index (EVI2) and the normalized difference water index (NDWI) and establishes daily cloud-free time series in from AHI and ABI from 2016-2018. The daily EVI2 and NDWI time series are applied to detect LSP metrics using a hybrid piecewise logistical model. The LSP detections are then compared with those extracted from VIIRS time series (500m). Finally, the influence of the quality of daily observation from different satellite sensors on the LSP detections is examined.
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