Exploring the potential of DSCOVR EPIC data to retrieve clumping index in Australian terrestrial ecosystem research network observing sites
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Pisek, Jan
Arndt, Stefan K.
Erb, Angela
Pendall, Elise
Schaaf, Crystal
Wardlaw, Timothy J.
Woodgate, William
Knyazikhin, Yuri
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J. Pisek, S.K. Arndt, A. Erb, E. Pendall, C. Schaaf, T.J. Wardlaw, W. Woodgate, Y. Knyazikhin. "Exploring the Potential of DSCOVR EPIC Data to Retrieve Clumping Index in Australian Terrestrial Ecosystem Research Network Observing Sites." Frontiers in Remote Sensing, Volume 2, https://doi.org/10.3389/frsen.2021.652436
Abstract
Vegetation foliage clumping significantly alters the radiation environment and affects vegetation growth as well as water, carbon cycles. The clumping index (CI) is useful in ecological and meteorological models because it provides new structural information in addition to the effective leaf area index. Previously generated CI maps using a diverse set of Earth Observation multi-angle datasets across a wide range of scales have all relied on the single approach of using the normalized difference hotspot and darkspot (NDHD) method. We explore an alternative approach to estimate CI from space using the unique observing configuration of the Deep Space Climate Observatory Earth Polychromatic Imaging Camera (DSCOVR EPIC) and associated products at 10 km resolution. The performance was evaluated with in situ measurements in five sites of the Australian Terrestrial Ecosystem Research Network comprising a diverse range of canopy structure from short and sparse to dense and tall forest. The DSCOVR EPIC data can provide meaningful CI retrievals at the given spatial resolution. Independent but comparable CI retrievals obtained with a completely different sensor and new approach were encouraging for the general validity and compatibility of the foliage clumping information retrievals from space. We also assessed the spatial representativeness of the five TERN sites with respect to a particular point in time (field campaigns) for satellite retrieval validation. Our results improve our understanding of product uncertainty both in terms of the representativeness of the field data collected over the TERN sites and its relationship to Earth Observation data at different spatial resolutions.
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Copyright © 2021 Pisek, Arndt, Erb, Pendall, Schaaf, Wardlaw, Woodgate and Knyazikhin. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.