Detection of Land Surface Phenology from New Generation Geostationary Satellites and Its Compassion with Observations from Polar-Orbiting Satellites
Abstract
Land surface phenology (LSP) has been widely detected from polar-orbiting sensors such as AVHRR, MODIS, and VIIRS during past two decades. However, the temporal frequency of the polar-orbiting sensors are unsatisfied for accurately extracting LSP for the seasons or regions with frequent cloud contaminations. The cloud impact can be mitigated using the new generation of geostationary sensors that include the Advanced Baseline Imager (ABI) onboard Operational Environmental Satellite (GOES) systems (GOES-16 launched in November 2016 and GOES-17 l aunched in March 2018 ) and the Advanced Himawari Imager (AHI) onboard the Himawari-8 (launched in October 2014) and Himawari-9 (launched in November 2016). The AHI and ABI observations with an interval of 5-10 minutes can significantly reduce cloud contaminations in a daily time series during a year, which can greatly improve the accuracy of LSP detections. In this study, we will obtain diurnal surface spectral reflectances (red, near infrared, and shortwave near infrared) that are produced from the GEONEX (Geostationary NASA-NOAA Earth Exchange) using MAIAC (Multi-Angle Implementation of Atmospheric Correction) algorithm in tropical Southeast Asia (AHI data) and the United Sates (ABI data) in 2017-2018 to calculate the enhanced vegetation index (EVI2) and the normalized difference water index (NDWI). The diurnal angularly-dependent EVI2 and NDWI data will be adjusted using the kernel-driven model of bidirectional reflectance distribution function (BRDF) in order to generate daily EVI2 and NDWI observations. The daily EVI2 and NDWI time series are applied to detect LSP metrics using a hybrid piecewise logistical model. The LSP detections are then evaluated by comparing with those extracted from VIIRS time series (500m) and the Harmonized Landsat and Sentinel-2 (HLS) observations (30m). Particularly, the influence of the quality of daily observation from different satellite sensors on the LSP detections is revealed. Finally, the LSP detection from high spatiotemporal data fused from AHI/ABI and HLS is investigated.
- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2019
- Bibcode:
- 2019AGUFM.A34F..03Z
- Keywords:
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- 3360 Remote sensing;
- ATMOSPHERIC PROCESSES;
- 0480 Remote sensing;
- BIOGEOSCIENCES;
- 1855 Remote sensing;
- HYDROLOGY;
- 4275 Remote sensing and electromagnetic processes;
- OCEANOGRAPHY: GENERAL