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

Tomoaki Miura, Shin Nagai, Mika Takeuchi, Kazuhito Ichii, Hiroki Yoshioka
2019 Scientific Reports  
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  ...  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  ...  L18554 T.M. and S.N.) and the Center for Environmental Remote Sensing (CEReS) Joint Research Program. Himawari-8 gridded data used in this study were distributed by CEReS, Chiba University, Japan.  ... 
doi:10.1038/s41598-019-52076-x pmid:31666582 pmcid:PMC6821777 fatcat:u4fktxpjg5fjrimh6xgc4pzhka

Seasonal Comparisons of Himawari-8 AHI and MODIS Vegetation Indices over Latitudinal Australian Grassland Sites

Ngoc Nguyen Tran, Alfredo Huete, Ha Nguyen, Ian Grant, Tomoaki Miura, Xuanlong Ma, Alexei Lyapustin, Yujie Wang, Elizabeth Ebert
2020 Remote Sensing  
The Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary (GEO) satellite offers comparable spectral and spatial resolutions as low earth orbiting (LEO) sensors such as the Moderate Resolution  ...  unfamiliar observation geometries not experienced with MODIS, VIIRS, or Advanced Very High Resolution Radiometer (AVHRR) VI time series data.  ...  Acknowledgments: The authors would like to thank Leon Majewski from Australia Bureau of Meteorology for his processing of raw Himawari-8 data.  ... 
doi:10.3390/rs12152494 fatcat:mqwluvjpdnfdpfmmgbos3zdvfm

Estimating and Monitoring Land Surface Phenology in Rangelands: A Review of Progress and Challenges

Trylee Nyasha Matongera, Onisimo Mutanga, Mbulisi Sibanda, John Odindi
2021 Remote Sensing  
Land surface phenology (LSP) has been extensively explored from global archives of satellite observations to track and monitor the seasonality of rangeland ecosystems in response to climate change.  ...  and techniques that can be used to better characterize the phenological cycles of vegetation in rangeland ecosystems.  ...  Acknowledgments: The authors would like to NRF Project officers Precious Bonsile Nciliba and Andile Mshengu for their administrative support.  ... 
doi:10.3390/rs13112060 fatcat:7r76xji7gzg7blwo2pq55zqmp4

Toward More Integrated Utilizations of Geostationary Satellite Data for Disaster Management and Risk Mitigation

Atsushi Higuchi
2021 Remote Sensing  
Imager (ABI), and Meteosat Third Generation (MTG) Flexible Combined Imager (FCI), provide advanced imagery and atmospheric measurements of the Earth's weather, oceans, and terrestrial environments at  ...  Third-generation geostationary meteorological satellites (GEOs), such as Himawari-8/9 Advanced Himawari Imager (AHI), Geostationary Operational Environmental Satellites (GOES)-R Series Advanced Baseline  ...  PAWR data were provided by Kazuomi Morotomi and Shigeharu Shimamura of Japan Radio Co. Ltd., Japan. The XRAIN data were obtained from DIAS.  ... 
doi:10.3390/rs13081553 fatcat:r4x5s2aedjayjhq54y2zxde5cu

How did the characteristics of the growing season change during the past 100 years at a steep river basin in Japan?

Nagai Shin, Taku M. Saitoh, Kenlo Nishida Nasahara, Dusan Gomory
2021 PLoS ONE  
We assessed the generality and representativeness of the modelled SGS and EGS dates by using phenological events, live camera images taken at multiple points in the basin, and satellite observations made  ...  We calculated the dates of the start (SGS) and end of the growing season (EGS) in a steep river basin located in a mountainous region of central Japan over a century timescale by using a degree-day phenological  ...  Acknowledgments We thank the Koito-Pottery and Takayama Printing Co, Ltd, for providing live camera images. We thank all members of the Takayama community for their assistance in the field.  ... 
doi:10.1371/journal.pone.0255078 pmid:34330144 pmcid:PMC8324334 fatcat:xqp4be54pvg65ndywkoxcmqjsi

Current and near-term advances in Earth observation for ecological applications

Susan L. Ustin, Elizabeth M. Middleton
2021 Ecological Processes  
The suite of instruments we describe measure land surface characteristics, including land cover, but provide a more detailed monitoring of ecosystems, plant communities, and even some species then possible  ...  scales from local plots to the whole planet.  ...  Acknowledgements Thanks to all the space agencies and their governments for making satellite data widely available through their "free and open" data policies.  ... 
doi:10.1186/s13717-020-00255-4 pmid:33425642 pmcid:PMC7779249 fatcat:tojcb7xw3reajgwgci46zsuqk4

2019 Index IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing Vol. 12

2019 IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing  
., and Lopez, J.F  ...  Evaluation of HJ-1A/B CCD Surface Reflectance Products Using the VNIR and MODIS-Based Atmospheric Correction Approaches.  ...  ., +, JSTARS Oct. 2019 3826-3840 Evaluation of HJ-1A/B CCD Surface Reflectance Products Using the VNIR and MODIS-Based Atmospheric Correction Approaches.  ... 
doi:10.1109/jstars.2020.2973794 fatcat:sncrozq3fjg4bgjf4lnkslbz3u

Knowledge Extracted from Copernicus Satellite Data

Dumitru Octavian, Schwarz Gottfried, Eltoft Torbjørn, Kræmer Thomas, Wagner Penelope, Hughes Nick, Arthus David, Fleming Andrew, Koubarakis Manolis, Datcu Mihai
2019 Zenodo  
The proposed methodology uses new paradigms from Recurrent Neural Networks and Generative Adversarial Networks, supported by Bayesian and Information Bottleneck concepts. References 1.  ...  For doing this, we propose to select overlapping target areas from Synthetic Aperture Radar and multispectral images acquired with rapid succession.  ...  One of the most difficult task is to distinguish the glaciers, glaciers lakes and dry from dry land.  ... 
doi:10.5281/zenodo.3941573 fatcat:zzifwgljifck5bpjnboetsftfu