Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部成都山地灾害与环境研究所 > 数字山地与遥感应用中心

	Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO
	Yin, Gaofei1,2 ; Li, Ainong1 ; Jin, Huaan1 ; Zhao, Wei1 ; Bian, Jinhu1,3 ; Qu, Yonghua 4,5,6; Zeng, Yelu 2; Xu, Baodong 2,3
刊名	AGRICULTURAL AND FOREST METEOROLOGY
	2017-02-15
卷号	233 页码:209-221
关键词	Leaf area index (LAI) Validation Temporal continuity LAINet observation system Consistent Adjustment of the Climatology to Actual Observations (CACAO)
ISSN号	0168-1923
通讯作者	Ainong Li
英文摘要	Leaf area index (LAI) products are now routinely generated from remote sensing and widely used in most land surface process models. Assessing the uncertainties associated with these LAI products is essential for their proper application. In validation activities, LAI reference maps serve as the bridge to upscale the field measurements to coarse resolution. Currently, the temporally continuous validation is attracting increasing attention. Therefore, there is an urgent requirement for the temporally continuous LAI reference maps. However, two main problems hinder the generation of temporally continuous LAI reference maps: 1) How to obtain temporally continuous field measurements, effectively and inexpensively? 2) How to obtain temporally continuous and fine spatial resolution satellite images which are synchronous with the field measurements? This paper proposed a method to address the above two problems based on the combination of the wireless sensor network technology and a data blending approach. Firstly, the temporally continuous effective LAI was obtained through the analysis of multi-angle gap fraction measured by LAINet observation system (developed with wireless sensor network technology), and the temporally continuous NDVI was reconstructed through CACAO (Consistent Adjustment of the Climatology to Actual Observations, a data blending approach). Then, a transfer function relating reconstructed NDVI to field measured LAI was calibrated through exponential function fitting. Finally, the temporally continuous LAI reference maps were generated by applying the calibrated transfer function to the reconstructed NDVI. Performances of the proposed method were evaluated over a crop site. Results show that the reconstructed LAI reference maps agree well with the original LAI reference maps derived from the Landsat-8 OLI NDVI (R-2 = 0.90, RMSE = 0.27 at 30 m resolution, R-2 = 0.97, RMSE = 0.09 at 1 km resolution). The derived temporally continuous LAI reference maps were then used as benchmark to validate the MOD15A2 LAI product. Generally, the MOD15A2 LAI has a relatively high accuracy (R-2=0.53, RMSE= 0.31), and captures the overall vegetation phenology well. But its value shows an obvious underestimation with a bias of -0.30. The results of this study contribute to the assessment of temporal dynamics of uncertainty in LAI products, which will benefit the long-term vegetation monitoring and data assimilation. (C) 2016 Elsevier B.V. All rights reserved.
WOS标题词	Science & Technology ; Life Sciences & Biomedicine ; Physical Sciences
类目[WOS]	Agronomy ; Forestry ; Meteorology & Atmospheric Sciences
研究领域[WOS]	Agriculture ; Forestry ; Meteorology & Atmospheric Sciences
关键词[WOS]	LEAF-AREA INDEX ; WIRELESS SENSOR NETWORK ; LANDSAT SURFACE REFLECTANCE ; ESSENTIAL CLIMATE VARIABLES ; IN-SITU MEASUREMENTS ; HEIHE RIVER-BASIN ; TIME-SERIES ; VEGETATION INDEXES ; MODIS DATA ; SEASONAL-VARIATION
收录类别	SCI
语种	英语
WOS记录号	WOS:000393259400019
内容类型	期刊论文
源URL	[http://ir.imde.ac.cn/handle/131551/18484]
专题	成都山地灾害与环境研究所_数字山地与遥感应用中心
作者单位	1.Chinese Acad Sci, Inst Mt Hazards & Environm, Res Ctr Digital Mt & Remote Sensing Applicat, Chengdu 610041, Peoples R China 2.Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 3.Univ Chinese Acad Sci, Coll Resources & Environm, Beijing 100049, Peoples R China 4.Beijing Normal Univ, State Key Lab Remote Sensing Sci, Res Ctr Remote Sensing, Beijing 100875, Peoples R China 5.Beijing Normal Univ, GIS, Beijing 100875, Peoples R China 6.Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China
推荐引用方式 GB/T 7714	Yin, Gaofei,Li, Ainong,Jin, Huaan,et al. Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO[J]. AGRICULTURAL AND FOREST METEOROLOGY,2017,233:209-221.
APA	Yin, Gaofei.,Li, Ainong.,Jin, Huaan.,Zhao, Wei.,Bian, Jinhu.,...&Xu, Baodong.(2017).Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO.AGRICULTURAL AND FOREST METEOROLOGY,233,209-221.
MLA	Yin, Gaofei,et al."Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO".AGRICULTURAL AND FOREST METEOROLOGY 233(2017):209-221.