气候变化领域集成服务门户(CCPortal): Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data

CCPortal

DOI	10.1016/j.rse.2019.111615
	Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data
	Kimm H.; Guan K.; Jiang C.; Peng B.; Gentry L.F.; Wilkin S.C.; Wang S.; Cai Y.; Bernacchi C.J.; Peng J.; Luo Y.
发表日期	2020
ISSN	00344257
卷号	239
英文摘要	Leaf area index (LAI) is a key variable for characterizing crop growth conditions and estimating crop productivity. Despite continuing efforts to develop LAI estimation algorithms, LAI datasets still need improvement at spatial and temporal resolutions to meet the requirements of agricultural applications. Advancements in data fusion technique and the emergence of new satellite data provide opportunities for LAI data at higher resolutions in both space and time. In this study, we derived new LAI estimations by leveraging novel satellite remote sensing datasets, STAIR fusion (MODIS-Landsat fusion) and Planet Labs' CubeSat data (through a reprocessed pipeline) for a typical agricultural landscape in the U.S. Corn Belt. The STAIR fused data and our reprocessed CubeSat data have both fine spatial resolutions (30 m and 3.125 m, respectively) and high frequencies (daily for both). To reliably estimate LAI from these advanced satellite datasets, we used two methods: inversion of a radiative transfer model (RTM), and empirical relationship with vegetation index (VI) calibrated from field measured LAI. Compared to the ground-truth LAI collected at 36 sites across the study region, reliable approximations were achieved by both LAI estimations based on PROSAIL RTM (STAIR: R2 = 0.69 and root mean squared error (RMSE) = 1.12 (m2 m− 2), CubeSat: R2 = 0.76 and RMSE = 1.09 (m2 m− 2)), and LAI estimations based on Green Wide Dynamic Range Vegetation Index (GrWDRVI) (STAIR: R2 = 0.75, RMSE = 1.10 (m2 m− 2), CubeSat: R2 = 0.76, RMSE = 1.08 (m2 m− 2), where validation ground-truth is independent from calibration data). Newly estimated high-resolution LAI data were aggregated at 500 m resolution and compared with MODIS and VIIRS LAI products, revealing substantial uncertainties and biases in these two products. We also demonstrated phenology stage estimation at fine spatial resolutions based on our high-frequency LAI data. The proposed LAI estimation methods at both high spatial resolution and temporal frequency can be applied to the entire U.S. Corn Belt and provide significant advancement to crop monitoring and precision agriculture. © 2019 Elsevier Inc.
英文关键词	Camera network; CubeSat; Leaf area index; Planet Labs; PROSAIL; STAIR fusion; Vegetation index
语种	英语
scopus关键词	Crops; Data fusion; Frequency estimation; Mean square error; Radiative transfer; Radiometers; Remote sensing; Small satellites; Stairs; Uncertainty analysis; Vegetation; Camera network; Leaf Area Index; PROSAIL; Radiative transfer model; Root mean squared errors; Spatial and temporal resolutions; Spatio-temporal resolution; Vegetation index; Satellites; agricultural ecosystem; data assimilation; image resolution; leaf area index; sensor; spatiotemporal analysis; Corn Belt; United States; Zea mays
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/179463
作者单位	College of Agricultural, Consumer and Environmental Sciences, University of Illinois at Urbana-Champaign, Urbana, IL, United States; National Center for Supercomputing Applications, University of Illinois at Urbana-Champaign, Urbana, IL, United States; Illinois Corn Growers Association, Bloomington, IL, United States; Illinois Farm Business and Farm Management Associations, Urbana, IL, United States; USDA-ARS Global Change and Photosynthesis Research Unit, Urbana, IL, United States; Department of Plant Biology, the University of Illinois at Urbana-Champaign, Urbana, IL, United States; Department of Computer Science, the University of Illinois at Urbana-Champaign, Urbana, IL, United States
推荐引用方式 GB/T 7714	Kimm H.,Guan K.,Jiang C.,et al. Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data[J],2020,239.
APA	Kimm H..,Guan K..,Jiang C..,Peng B..,Gentry L.F..,...&Luo Y..(2020).Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data.Remote Sensing of Environment,239.
MLA	Kimm H.,et al."Deriving high-spatiotemporal-resolution leaf area index for agroecosystems in the U.S. Corn Belt using Planet Labs CubeSat and STAIR fusion data".Remote Sensing of Environment 239(2020).