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Generation and Evaluation of LAI and FPAR Products from Himawari-8 Advanced Himawari Imager (AHI) Data

Yepei Chen, Kaimin Sun, Chi Chen, Ting Bai, Taejin Park, Weile Wang, Ramakrishna R. Nemani, Ranga B. Myneni
2019 Remote Sensing  
In this paper, we estimate the geostationary Himawari-8 Advanced Himawari Imager (AHI) LAI/FPAR fields by training artificial neural networks (ANNs) with Himawari-8 normalized difference vegetation index  ...  For more effective use in monitoring of vegetation phenology, climate change impacts, disaster trend etc., in a timely manner, it is critical to generate LAI/FPAR with less cloud/cloud shadow contamination  ...  Figure 1 . 1 Flow chart of generating Advanced Himawari Imager (AHI) leaf area index/fraction of photosynthetically active radiation (LAI/FPAR) products.  ... 
doi:10.3390/rs11131517 fatcat:ykr645dlffcpjbsprmhyu4gfwu

Sun-Angle Effects on Remote-Sensing Phenology Observed and Modelled Using Himawari-8

Xuanlong Ma, Alfredo Huete, Ngoc Nguyen Tran, Jian Bi, Sicong Gao, Yelu Zeng
2020 Remote Sensing  
The vegetation indices (VI) data span two years and are from the Advanced Himawari Imager (AHI), which is onboard the Japanese Himawari-8 geostationary satellite.  ...  Here we examined the effect of seasonal and spatial variations in SZA on retrieving vegetation phenology from time series of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index  ...  Himawari-8 Advanced Himawari Imager Data Himawari-8 is a Japanese geostationary satellite launched on 7 October 2014 and is positioned above 140.7°E and 0.02°S [47] .  ... 
doi:10.3390/rs12081339 fatcat:6s3fqjikobfzfj67x3ae2wz5la

Geolocation Accuracy Assessment of Himawari-8/AHI Imagery for Application to Terrestrial Monitoring

Yuhei Yamamoto, Kazuhito Ichii, Atsushi Higuchi, Hideaki Takenaka
2020 Remote Sensing  
In the case of the Advanced Himawari Imager (AHI) onboard Himawari-8, geometric correction of the Himawari Standard Data provided by the Japan Meteorological Agency (JMA data) was conducted using thermal  ...  Recent advancements in new generation geostationary satellites have facilitated the application of their datasets to terrestrial monitoring.  ...  The Advanced Himawari Imager (AHI) onboard Himawari-8 is superior to conventional imagers, as it has more visible and near-infrared wavelength bands [1] , thereby enabling higher-dimensional terrestrial  ... 
doi:10.3390/rs12091372 fatcat:44qnneo4jjfcdmrl5sjgnamem4

Status of Phenological Research Using Sentinel-2 Data: A Review

Gourav Misra, Fiona Cawkwell, Astrid Wingler
2020 Remote Sensing  
been very little research on their efficacy to track vegetation cover and its phenology.  ...  important indicators of trends in phenology.  ...  in an oak-grass savanna system.  ... 
doi:10.3390/rs12172760 fatcat:c26aoq2gerbyvdsdritson54m4

ECOSTRESS estimates gross primary production with fine spatial resolution for different times of day from the International Space Station

Xing Li, Jingfeng Xiao, Joshua B. Fisher, Dennis D. Baldocchi
2021 Remote Sensing of Environment  
The predictive GPP model used instantaneous ECOSTRESS LST observations along with the daily enhanced vegetation index (EVI) from the Moderate Resolution Imaging Spectroradiometer (MODIS), land cover type  ...  We found distinct changes in GPP at different times of day (e.g., higher in late morning, peak around noon, approaching zero at dusk), and clear differences in productivity across landscapes (e.g., savannas  ...  These nine images could generally describe the changes in photosynthesis of vegetation over the course of one summer day.  ... 
doi:10.1016/j.rse.2021.112360 fatcat:g4tf4ck5yzfrxpy7pacm5vxrkm

A framework for deep learning emulation of numerical models with a case study in satellite remote sensing [article]

Kate Duffy, Thomas Vandal, Weile Wang, Ramakrishna Nemani, Auroop R. Ganguly
2022 arXiv   pre-print
STUDY AREA AND DATA SETS Datasets used in this study are from the Advanced Himawari Imager (AHI) sensor carried by the Japanese geostationary satellite Himawari-8.  ...  Seasonal analysis is used to evaluate the performance under annual fluctuations in vegetation phenology (Figure 7 ).  ... 
arXiv:1910.13408v2 fatcat:ukewtpv5zfagdjal6ayyyg2vcm

A Comparison of Tropical Rainforest Phenology Retrieved From Geostationary (SEVIRI) and Polar-Orbiting (MODIS) Sensors Across the Congo Basin

Dong Yan, Xiaoyang Zhang, Yunyue Yu, Wei Guo
2016 IEEE Transactions on Geoscience and Remote Sensing  
This paper retrieved and compared land surface phenology from observations acquired by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard geostationary satellites and the Moderate Resolution  ...  The significance of influences was determined using the one-tailed two-sample Kolmogorov-Smirnov test.  ...  The monitoring of global tropical rainforest phenology using a set of geostationary satellites is very promising.  ... 
doi:10.1109/tgrs.2016.2552462 fatcat:mnpiegkcnnbtdgo5rcmvfr4tye

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

Susan L. Ustin, Elizabeth M. Middleton
2021 Ecological Processes  
We focused on imagers that passively measure wavelengths in the reflected solar and emitted thermal spectrum.  ...  These data will enable us to quantify and characterize various soil properties such as iron content, several types of soil clays, organic matter, and other components.  ...  The Japanese Space Agency (JAXA) was the first to fly a third-generation geostationary satellite in 2014, the Himawari 8 (Table 5) , that carries the Advanced Himawari Imager (AHI), a 16-band multispectral  ... 
doi:10.1186/s13717-020-00255-4 pmid:33425642 pmcid:PMC7779249 fatcat:tojcb7xw3reajgwgci46zsuqk4

Cloud Cover in the Australian Region: Development and Validation of a Cloud Masking, Classification and Optical Depth Retrieval Algorithm for the Advanced Himawari Imager

Yi Qin, Andrew D. L. Steven, Thomas Schroeder, Tim R. McVicar, Jing Huang, Martin Cope, Shangzhi Zhou
2019 Frontiers in Environmental Science  
This paper presents a cloud masking, cloud classification and optical depth retrieval algorithm and its application to the Advanced Himawari Imager (AHI) on the Himawari-8/9 satellites using visible, near  ...  The products generated from this study are being used in several applications including ocean color remote sensing, solar energy, vegetation monitoring and detection of smoke for the study of their health  ...  AHI images were provided by the Japanese Meteorology Agency through Australian Bureau of Meteorology. CALIOP cloud layer product was obtained from the NASA Langley Research Center.  ... 
doi:10.3389/fenvs.2019.00020 fatcat:u6zseuwjkje2nbxpwxo2e4wnya

A Framework for Deep Learning Emulation of Numerical Models With a Case Study in Satellite Remote Sensing

Kate Duffy, Thomas J Vandal, Weile Wang, Ramakrishna R Nemani, Auroop R Ganguly
STUDY AREA AND DATASETS Datasets used in this study are from the Advanced Himawari Imager (AHI) sensor carried by the Japanese geostationary satellite Himawari-8.  ...  The seasonal analysis is used to evaluate the performance under annual fluctuations in vegetation phenology (see Fig. 7 ).  ... 
doi:10.1109/tnnls.2022.3169958 pmid:35511836 fatcat:ko5c2zf3ujgs3lsrq37v3qskfe