气候变化领域集成服务门户(CCPortal): Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest

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DOI	10.1016/j.rse.2020.111865
	Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest
	Wang J.; Yang D.; Detto M.; Nelson B.W.; Chen M.; Guan K.; Wu S.; Yan Z.; Wu J.
发表日期	2020
ISSN	00344257
卷号	246
英文摘要	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 3 m 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. Our results highlight the potential of integrating multi-scale satellite observations to extend fine-scale leaf phenology monitoring beyond the spatial limits of phenocams. © 2020 Elsevier Inc.
英文关键词	BRDF correction; Individual tree crowns; Leaf phenology; MODIS; Multi-scale satellite observations; Non-photosynthetic vegetation; PlanetScope; Reflectance cross-calibration
语种	英语
scopus关键词	Biogeochemistry; Drought; Ecosystems; Radiometers; Reflection; Remote sensing; Satellites; Tropics; Vegetation; Biogeochemical cycle; Linear spectral unmixing; Non-photosynthetic vegetation; Satellite observations; Satellite remote sensing; Spectral reflectances; Temporal resolution; Tropical evergreen forests; Forestry; calibration; dry season; evergreen forest; Landsat; MODIS; phenology; pixel; remote sensing; satellite data; surface reflectance; tropical forest; vegetation cover; Brazil; Para [Brazil]; Tapajos National Forest; Nucleopolyhedrovirus
来源期刊	Remote Sensing of Environment
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/179285
作者单位	University of Hong Kong, School of Biological Sciences, Hong Kong; Stony Brook University, Ecology and Evolution, Stony Brook, NY, United States; Princeton University, Princeton, NJ, United States; National Institute for Amazon Research (INPA), Manaus, Brazil; Pacific Northwest National Laboratory, Joint Global Change Research Institute, College Park, MD, United States; University of Illinois at Urbana Champaign, College of Agricultural, Consumer and Environmental Sciences, Urbana, IL, United States
推荐引用方式 GB/T 7714	Wang J.,Yang D.,Detto M.,et al. Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest[J],2020,246.
APA	Wang J..,Yang D..,Detto M..,Nelson B.W..,Chen M..,...&Wu J..(2020).Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest.Remote Sensing of Environment,246.
MLA	Wang J.,et al."Multi-scale integration of satellite remote sensing improves characterization of dry-season green-up in an Amazon tropical evergreen forest".Remote Sensing of Environment 246(2020).