气候变化领域集成服务门户(CCPortal): Improving leaf area index retrieval over heterogeneous surface mixed with water

CCPortal

DOI	10.1016/j.rse.2020.111700
	Improving leaf area index retrieval over heterogeneous surface mixed with water
	Xu B.; Li J.; Park T.; Liu Q.; Zeng Y.; Yin G.; Yan K.; Chen C.; Zhao J.; Fan W.; Knyazikhin Y.; Myneni R.B.
发表日期	2020
ISSN	00344257
卷号	240
英文摘要	Land cover mixture at moderate- to coarse-resolution is an important cause for the uncertainty of global leaf area index (LAI) products. The accuracy of LAI retrievals over land-water mixed pixels is adversely impacted because water absorbs considerable solar radiation and thus can greatly lower pixel-level reflectance especially in the near-infrared wavelength. Here we proposed an approach named Reduced Water Effect (RWE) to improve the accuracy of LAI retrievals by accounting for water-induced negative bias in reflectances. The RWE consists of three parts: water area fraction (WAF) calculation, subpixel water reflectance computation in land-water mixed pixels and LAI retrieval using the operational MODIS LAI algorithm. The performance of RWE was carefully evaluated using the aggregated Landsat ETM+ reflectance of water pixels over different regions and observation dates and the aggregated 30-m LAI reference maps over three sites in the moderate-resolution pixel grid (500-m). Our results suggest that the mean absolute errors of water endmember reflectance in red and NIR bands were both <0.016, which only introduced mean absolute (relative) errors of <0.15 (15%) for the pixel-level LAI retrievals. The validation results reveal that the accuracy of RWE LAI was higher than that of MODIS LAI over land-water mixed pixels especially for pixels with larger WAFs. Additionally, the mean relative difference between RWE LAI and aggregated 30-m LAI did not vary with WAF, indicating that water effects were significantly reduced by the RWE method. A comparison between RWE and MODIS LAI shows that the maximum absolute and relative differences caused by water effects were 0.9 and 100%, respectively. Furthermore, the impact of water mixed in pixels can induce the LAI underestimation and change the day selected for compositing the 8-day LAI product. These results indicate that RWE can effectively reduce water effects on the LAI retrieval of land-water mixed pixels, which is promising for the improvement of global LAI products. © 2020 Elsevier Inc.
英文关键词	Leaf area index (LAI); MODIS collection 6; Subpixel mixture; Uncertainty; Water effects
语种	英语
scopus关键词	Aggregates; Infrared devices; Mixtures; Radiometers; Reflection; Leaf Area Index; MODIS collection 6; Sub pixels; Uncertainty; Water effects; Pixels; accuracy assessment; land cover; Landsat; leaf area index; MODIS; pixel; solar radiation; 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/179415
作者单位	State Key Laboratory of Remote Sensing Science, Jointly Sponsored by Aerospace Information Research Institute, Chinese Academy of Sciences and Beijing Normal University, Beijing, 100101, China; Department of Earth and Environment, Boston University, Boston, MA 02215, United States; Macro Agriculture Research Institute, College of Resource and Environment, Huazhong Agricultural University, Wuhan, 430070, China; Department of Global Ecology, Carnegie Institution for Science, Stanford, CA 94305, United States; Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu, 610031, China; School of Land Science and Techniques, China University of Geosciences, Beijing, 100083, China; School of Environmental and Resources Science, Zhejiang A & F University, Lin'an, 311300, China
推荐引用方式 GB/T 7714	Xu B.,Li J.,Park T.,et al. Improving leaf area index retrieval over heterogeneous surface mixed with water[J],2020,240.
APA	Xu B..,Li J..,Park T..,Liu Q..,Zeng Y..,...&Myneni R.B..(2020).Improving leaf area index retrieval over heterogeneous surface mixed with water.Remote Sensing of Environment,240.
MLA	Xu B.,et al."Improving leaf area index retrieval over heterogeneous surface mixed with water".Remote Sensing of Environment 240(2020).