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Title: Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest

Abstract

In tropical forests, leaf phenology-particularly the pronounced dry-season green-up-strongly regulates biogeochemical cycles of carbon and water fluxes. However, uncertainties remain in the understanding of tropical forest leaf phenology at different spatial scales. Phenocams accurately characterize leaf phenology at the crown and ecosystem scales but are limited to a few sites and time spans of a few years. Time-series satellite observations might fill this gap, but the commonly used satellites (e.g. MODIS, Landsat and Sentinel-2) have resolutions too coarse to characterize single crowns. To resolve this observational challenge, we used the PlanetScope constellation with a 3m resolution and near daily nadir-view coverage. We first developed a rigorous method to cross-calibrate PlanetScope surface reflectance using daily BRDF-adjusted MODIS as the reference. We then used linear spectral unmixing of calibrated PlanetScope to obtain dry-season change in the fractional cover of green vegetation (GV) and non-photosynthetic vegetation (NPV) at the PlanetScope pixel level. We used the Central Amazon Tapajos National Forest k67 site, as all necessary data (from field to phenocam and satellite observations) was available. For this proof of concept, we chose a set of 22 dates of PlanetScope measurements in 2018 and 16 in 2019, all from the six drier months ofmore » the year to provide the highest possible cloud-free temporal resolution. Our results show that MODIS-calibrated dry-season PlanetScope data (1) accurately assessed seasonal changes in ecosystem-scale and crown-scale spectral reflectance; (2) detected an increase in ecosystem-scale GV fraction (and a decrease in NPV fraction) from June to November of both years, consistent with local phenocam observations with R2 around 0.8; and (3) monitored large seasonal trend variability in crown-scale NPV fraction. Finally, our results highlight the potential of integrating multi-scale satellite observations to extend fine-scale leaf phenology monitoring beyond the spatial limits of phenocams.« less

Authors:
; ; ; ; ; ORCiD logo; ORCiD logo; ; ORCiD logo
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Science (SC), Biological and Environmental Research (BER)
OSTI Identifier:
1630464
Alternate Identifier(s):
OSTI ID: 1618402
Report Number(s):
BNL-215923-2020-JAAM
Journal ID: ISSN 0034-4257; S0034425720302352; 111865; PII: S0034425720302352
Grant/Contract Number:  
SC001704; SC0012704
Resource Type:
Publisher's Accepted Manuscript
Journal Name:
Remote Sensing of Environment
Additional Journal Information:
Journal Name: Remote Sensing of Environment Journal Volume: 246 Journal Issue: C; Journal ID: ISSN 0034-4257
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; multi-scale satellite observations; PlanetScope; MODIS; BRDF correction; reflectance cross-calibration; leaf phenology; non-photosynthesis vegetation; individual tree crowns

Citation Formats

Wang, Jing, Yang, Dedi, Detto, Matteo, Nelson, Bruce W., Chen, Min, Guan, Kaiyu, Wu, Shengbiao, Yan, Zhengbing, and Wu, Jin. Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest. United States: N. p., 2020. Web. https://doi.org/10.1016/j.rse.2020.111865.
Wang, Jing, Yang, Dedi, Detto, Matteo, Nelson, Bruce W., Chen, Min, Guan, Kaiyu, Wu, Shengbiao, Yan, Zhengbing, & Wu, Jin. Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest. United States. https://doi.org/10.1016/j.rse.2020.111865
Wang, Jing, Yang, Dedi, Detto, Matteo, Nelson, Bruce W., Chen, Min, Guan, Kaiyu, Wu, Shengbiao, Yan, Zhengbing, and Wu, Jin. Tue . "Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest". United States. https://doi.org/10.1016/j.rse.2020.111865.
@article{osti_1630464,
title = {Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest},
author = {Wang, Jing and Yang, Dedi and Detto, Matteo and Nelson, Bruce W. and Chen, Min and Guan, Kaiyu and Wu, Shengbiao and Yan, Zhengbing and Wu, Jin},
abstractNote = {In tropical forests, leaf phenology-particularly the pronounced dry-season green-up-strongly regulates biogeochemical cycles of carbon and water fluxes. However, uncertainties remain in the understanding of tropical forest leaf phenology at different spatial scales. Phenocams accurately characterize leaf phenology at the crown and ecosystem scales but are limited to a few sites and time spans of a few years. Time-series satellite observations might fill this gap, but the commonly used satellites (e.g. MODIS, Landsat and Sentinel-2) have resolutions too coarse to characterize single crowns. To resolve this observational challenge, we used the PlanetScope constellation with a 3m resolution and near daily nadir-view coverage. We first developed a rigorous method to cross-calibrate PlanetScope surface reflectance using daily BRDF-adjusted MODIS as the reference. We then used linear spectral unmixing of calibrated PlanetScope to obtain dry-season change in the fractional cover of green vegetation (GV) and non-photosynthetic vegetation (NPV) at the PlanetScope pixel level. We used the Central Amazon Tapajos National Forest k67 site, as all necessary data (from field to phenocam and satellite observations) was available. For this proof of concept, we chose a set of 22 dates of PlanetScope measurements in 2018 and 16 in 2019, all from the six drier months of the year to provide the highest possible cloud-free temporal resolution. Our results show that MODIS-calibrated dry-season PlanetScope data (1) accurately assessed seasonal changes in ecosystem-scale and crown-scale spectral reflectance; (2) detected an increase in ecosystem-scale GV fraction (and a decrease in NPV fraction) from June to November of both years, consistent with local phenocam observations with R2 around 0.8; and (3) monitored large seasonal trend variability in crown-scale NPV fraction. Finally, our results highlight the potential of integrating multi-scale satellite observations to extend fine-scale leaf phenology monitoring beyond the spatial limits of phenocams.},
doi = {10.1016/j.rse.2020.111865},
journal = {Remote Sensing of Environment},
number = C,
volume = 246,
place = {United States},
year = {2020},
month = {9}
}

Journal Article:
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https://doi.org/10.1016/j.rse.2020.111865

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