globalchange  > 气候变化与战略
DOI: 10.1016/j.rse.2020.111700
Title:
Improving leaf area index retrieval over heterogeneous surface mixed with water
Author: 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.
Source Publication: Remote Sensing of Environment
ISSN: 344257
Publishing Year: 2020
Volume: 240
Language: 英语
Keyword: Leaf area index (LAI) ; MODIS collection 6 ; Subpixel mixture ; Uncertainty ; Water effects
Scopus Keyword: 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
English Abstract: 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.
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被引频次[WOS]:2   [查看WOS记录]     [查看WOS中相关记录]
Document Type: 期刊论文
Identifier: http://119.78.100.158/handle/2HF3EXSE/158606
Appears in Collections:气候变化与战略

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Affiliation: 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

Recommended Citation:
Xu B.,Li J.,Park T.,et al. Improving leaf area index retrieval over heterogeneous surface mixed with water[J]. Remote Sensing of Environment,2020-01-01,240
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