气候变化领域集成服务门户(CCPortal): A data-driven approach to estimate leaf area index for Landsat images over the contiguous US

CCPortal

DOI	10.1016/j.rse.2021.112383
	A data-driven approach to estimate leaf area index for Landsat images over the contiguous US
	Kang Y.; Ozdogan M.; Gao F.; Anderson M.C.; White W.A.; Yang Y.; Yang Y.; Erickson T.A.
发表日期	2021
ISSN	00344257
卷号	258
英文摘要	Leaf Area Index (LAI) is a fundamental vegetation biophysical variable serving as an essential input to many land surface and atmospheric models. Long-term LAI maps are typically generated with satellite images at moderate spatial resolution (0.25 to 1 km), such as those from the Moderate Resolution Imaging Spectroradiometer (MODIS). While useful for regional-scale land surface modeling, these moderate resolution products often cannot resolve spatial heterogeneity important for many agricultural and hydrological applications. This paper proposes an approach to map LAI at 30-m resolution based on Landsat images for the Contiguous US (CONUS) consistent with the MODIS product, aimed at multi-scale modeling applications. The algorithm was driven by 1.6 million spatially homogeneous samples derived from MODIS LAI and Landsat surface reflectance products from 2006 to 2018. Based on these samples, we trained separate random forest models to estimate LAI from Landsat surface reflectance for eight biomes of the National Land Cover Database (NLCD). A balanced sample design regarding the saturation status of MODIS LAI and a machine-learning-based noise detection technique were introduced to mitigate the trade-off in estimation accuracy between medium LAI (e.g., 3 to 4, unsaturated) and high LAI (e.g., 4–6, saturated). This approach was evaluated using ground measurements from 19 National Ecological Observatory Network (NEON) sites and eight independent sites from other sources. These sites comprise a representative sample of forests, grasslands, shrublands, and croplands across the US. For NEON sites, the LAI estimates show an overall Root Mean Squared Error (RMSE) of 0.8 with r2 of 0.88. For the eight independent sites, the Landsat LAI algorithm achieves RMSE between 0.52 and 0.91. The uncertainty in Landsat estimated LAI varies across biomes and locations. The proposed algorithm was implemented on the Google Earth Engine platform, allowing for the rapid generation of long-term high-resolution LAI records from the 1980s using Landsat images (code is available at https://github.com/yanghuikang/Landsat-LAI). Our findings also highlight the importance of sample balance on regression-based modeling in remote sensing applications. © 2021 Elsevier Inc.
英文关键词	Google earth engine; Landsat; Leaf area index; Machine learning; MODIS
语种	英语
scopus关键词	Decision trees; Economic and social effects; Learning algorithms; Machine learning; Mean square error; Radiometers; Reflection; Remote sensing; Surface measurement; Uncertainty analysis; Data-driven approach; Google earth engine; Land surface models; LANDSAT; Landsat images; Leaf Area Index; Machine-learning; Moderate-resolution imaging spectroradiometers; Root mean squared errors; Surface reflectance; Engines; algorithm; heterogeneity; image analysis; Landsat; leaf area index; observatory; remote sensing; spatial resolution; surface reflectance; trade-off; uncertainty analysis
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/178881
作者单位	Department of Geography, University of Wisconsin-Madison, 550 N. Park St, Madison, WI 53706, United States; Nelson Institute Center for Sustainability and the Global Environment, University of Wisconsin-Madison, 1710 University Avenue, Madison, WI 53726, United States; Hydrology and Remote Sensing Laboratory, USDA ARS, Beltsville, MD 20705, United States; Department of Forest and Wildlife Ecology, University of Wisconsin-Madison, 1630 Linden Drive, Madison, WI 53706, United States; Google Inc., Mountain View, CA 94043, United States
推荐引用方式 GB/T 7714	Kang Y.,Ozdogan M.,Gao F.,et al. A data-driven approach to estimate leaf area index for Landsat images over the contiguous US[J],2021,258.
APA	Kang Y..,Ozdogan M..,Gao F..,Anderson M.C..,White W.A..,...&Erickson T.A..(2021).A data-driven approach to estimate leaf area index for Landsat images over the contiguous US.Remote Sensing of Environment,258.
MLA	Kang Y.,et al."A data-driven approach to estimate leaf area index for Landsat images over the contiguous US".Remote Sensing of Environment 258(2021).