Improved Characterisation of Vegetation and Land Surface Seasonal Dynamics in Central Japan with Himawari-8 Hypertemporal Data

Tomoaki Miura,Mika Takeuchi,Kazuhito Ichii,Shin Nagai,Hiroki Yoshioka

doi:10.1038/s41598-019-52076-x

Abstract

Spectral vegetation index time series data, such as the normalized difference vegetation index (NDVI), from moderate resolution, polar-orbiting satellite sensors have widely been used for analysis of vegetation seasonal dynamics from regional to global scales. The utility of these datasets is often limited as frequent/persistent cloud occurrences reduce their effective temporal resolution. In this study, we evaluated improvements in capturing vegetation seasonal changes with 10-min resolution NDVI data derived from Advanced Himawari Imager (AHI), one of new-generation geostationary satellite sensors. Our analysis was focused on continuous monitoring sites, representing three major ecosystems in Central Japan, where in situ time-lapse digital images documenting sky and surface vegetation conditions were available. The very large number of observations available with AHI resulted in improved NDVI temporal signatures that were remarkably similar to those acquired with in situ spectrometers and captured seasonal changes in vegetation and snow cover conditions in finer detail with more certainty than those obtained from Visible Infrared Imaging Radiometer Suite (VIIRS), one of the latest polar-orbiting satellite sensors. With the ability to capture in situ-quality NDVI temporal signatures, AHI “hypertemporal” data have the potential to improve spring and autumn phenology characterisation as well as the classification of vegetation formations.

Highlights

The utility of these VI time series datasets in characterizing vegetation dynamics is often constrained by clouds
We began our analysis by comparing Advanced Himawari Imager (AHI) Normalized Difference Vegetation Index (NDVI) temporal profiles to those of Visible Infrared Imaging Radiometer Suite (VIIRS) using the Phenological Eyes Network (PEN)-derived in situ phenology information as a reference
Since cloud contamination lowers the NDVI, cloud-contaminated NDVI data can be seen as those scattering below the upper envelopes of the NDVI temporal profiles which in turn most likely consisted of cloud-free pixels[30,31]

Summary

Introduction

The utility of these VI time series datasets in characterizing vegetation dynamics is often constrained by clouds. A new generation of geostationary satellite sensors have been launched during the last decade and planned for launch (see Supplementary Table S1) These sensors are capable of imaging an Earth’s hemisphere at 10–15 min intervals and equipped with the spectral bands suitable for the derivation of VIs, potentially serving as another significant data source for the studies of vegetation dynamics[13]. Yan et al.[28] reported the first application of Himawari-8 AHI two-band Enhanced Vegetation Index (EVI2) time series data to land surface phenology in Northern and Central Japan They found that AHI EVI2 higher temporal resolution data only helped improve the characterization of spring phenology in comparison to the MODIS counterpart. AHI NDVI results were compared with those obtained from one of the latest polar-orbiting satellite sensors, Visible Infrared Imaging Radiometer Suite (VIIRS) onboard the Suomi-National Polar-orbiting Partnership (S-NPP)

Methods

Results

Conclusion