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

doi:10.3390/rs11131517

Remote Sensing (Jun 2019)

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

Affiliations

Yepei Chen: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Kaimin Sun: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Chi Chen: Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Ting Bai: State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
Taejin Park: Department of Earth and Environment, Boston University, Boston, MA 02215, USA
Weile Wang: NASA Ames Research Center, Moffett Field, CA 94035, USA
Ramakrishna R. Nemani: NASA Ames Research Center, Moffett Field, CA 94035, USA
Ranga B. Myneni: Department of Earth and Environment, Boston University, Boston, MA 02215, USA

DOI: https://doi.org/10.3390/rs11131517
Journal volume & issue: Vol. 11, no. 13
p. 1517

Abstract

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Leaf area index (LAI) and fraction of photosynthetically active radiation (FPAR) absorbed by vegetation are two of the essential biophysical variables used in most global models of climate, hydrology, biogeochemistry, and ecology. Most LAI/FPAR products are retrieved from non-geostationary satellite observations. Long revisit times and cloud/cloud shadow contamination lead to temporal and spatial gaps in such LAI/FPAR products. 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 and at higher temporal resolution—something that is feasible with geostationary satellite data. 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 (NDVI) and moderate resolution imaging spectroradiometer (MODIS) LAI/FPAR products for each biome type. Daily cycles of the estimated AHI LAI/FPAR products indicate that these are stable at 10-min frequency during the day. Comprehensive evaluations were carried out for the different biome types at different spatial and temporal scales by utilizing the MODIS LAI/FPAR products and the available field measurements. These suggest that the generated Himawari-8 AHI LAI/FPAR fields were spatially and temporally consistent with the benchmark MODIS LAI/FPAR products. We also evaluated the AHI LAI/FPAR products for their potential to accurately monitor the vegetation phenology—the results show that AHI LAI/FPAR products closely match the phenological development captured by the MODIS products.

Published in Remote Sensing

ISSN: 2072-4292 (Online)
Publisher: MDPI AG
Country of publisher: Switzerland
LCC subjects: Science
Website: http://www.mdpi.com/journal/remotesensing/

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