An integrated method for validating long-term leaf area index products using global networks of site-based measurements

作者:Xu, BD (Xu, Baodong)[ 1,2,4 ] ; Li, J (Li, Jing)[ 1,3 ] ; Park, T (Park, Taejin)[ 2 ] ; Liu, QH (Liu, Qinhuo)[ 1,3 ] ; Zeng, YL (Zeng, Yelu)[ 1,5 ] ; Yin, GF (Yin, Gaofei)[ 6 ] ; Zhao, J (Zhao, Jing)[ 1 ] ; Fan, WL (Fan, Weiliang)[ 7 ] ; Yang, L (Yang, Le)[ 1 ] ; Knyazikhin, Y (Knyazikhin, Yuri)[ 2 ]  ; Myneni, RB (Myneni, Ranga B.)[ 2 ]

REMOTE SENSING OF ENVIRONMENT

卷: 209  页: 134-151

DOI: 10.1016/j.rse.2018.02.049

出版年: MAY 2018

文献类型:Article

摘要

Long-term ground LAI measurements from the global networks of sites (e.g. FLUXNET) have emerged as a promising data source to validate remotely sensed global LAI product time-series. However, the spatial scale-mismatch issue between site and satellite observations hampers the use of such invaluable ground measurements in validation practice. Here, we propose an approach (Grading and Upscaling of Ground Measurements, GUGM) that integrates a spatial representativeness grading criterion and a spatial upscaling strategy to resolve this scale-mismatch issue and maximize the utility of time-series of site-based LAI measurements. The performance of GUGM was carefully evaluated by comparing this method to both benchmark LAI and other widely used conventional approaches. The uncertainty of three global LAI products (i.e. MODIS, GLASS and GEOV1) was also assessed based on the LAI time-series validation dataset derived from GUGM. Considering all the evaluation results together, this study suggests that the proposed GUGM approach can significantly reduce the uncertainty from spatial scale mismatch and increase the size of the available validation dataset. In particular, the proposed approach outperformed other widely used approaches in these two respects. Furthermore, GUGM was successfully implemented to validate global LAI products in various ways with advantaging frequent time-series validation dataset. The validation results of the global LAI products show that GLASS has the lowest uncertainty, followed by GEOV1 and MODIS for the overall biome types. However, MODIS provides more consistent uncertainties across different years than GLASS and GEOV1. We believe that GUGM enables us to better understand the structure of LAI product uncertainties and their evolution across seasonal or annual contexts. In turn, this method can provide fundamental information for further LAI algorithm improvements and the broad application of LAI product time-series.

通讯作者地址: Li, J; Liu, QH (通讯作者)

Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China.

通讯作者地址: Park, T (通讯作者)

Boston Univ, Dept Earth & Environm, Boston, MA 02215 USA.

地址:

[ 1 ] Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China

[ 2 ] Boston Univ, Dept Earth & Environm, Boston, MA 02215 USA

[ 3 ] JCGCS, Beijing 100875, Peoples R China

[ 4 ] Univ Chinese Acad Sci, Beijing 100049, Peoples R China

[ 5 ] Carnegie Inst Sci, Dept Global Ecol, Stanford, CA 94305 USA

[ 6 ] Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Peoples R China

[ 7 ] Zhejiang A&F Univ, Sch Environm & Resources Sci, Linan 311300, Peoples R China