An Upscaling Algorithm to Obtain the Representative Ground Truth of LAI Time Series in Heterogeneous Land Surface

作者:Shi, YC (Shi, Yuechan)[ 1,2,3 ] ; Wang, JD (Wang, Jindi)[ 1,2,3 ] ; Qin, J (Qin, Jun)[ 4 ] ; Qu, YH (Qu, Yonghua)[ 1,2,3 ]

Remote Sensing

卷: 7  期: 10  页: 12887-12908

DOI: 10.3390/rs71012887

出版年: OCT 2015

摘要

Upscaling in situ leaf area index (LAI) measurements to the footprint scale is important for the validation of medium resolution remote sensing products. However, surface heterogeneity and temporal variation of vegetation make this difficult. In this study, a two-step upscaling algorithm was developed to obtain the representative ground truth of LAI time series in heterogeneous surfaces based on in situ LAI data measured by the wireless sensor network (WSN) observation system. Since heterogeneity within a site usually arises from the mixture of vegetation and non-vegetation surfaces, the spatial heterogeneity of vegetation and land cover types were separately considered. Representative LAI time series of vegetation surfaces were obtained by upscaling in situ measurements using an optimal weighted combination method, incorporating the expectation maximum (EM) algorithm to derive the weights. The ground truth of LAI over the whole site could then be determined using area weighted combination of representative LAIs of different land cover types. The algorithm was evaluated using a dataset collected in Heihe Watershed Allied Telemetry Experimental Research (HiWater) experiment. The proposed algorithm can effectively obtain the representative ground truth of LAI time series in heterogeneous cropland areas. Using the normal method of an average LAI measurement to represent the heterogeneous surface produced a root mean square error (RMSE) of 0.69, whereas the proposed algorithm provided RMSE = 0.032 using 23 sampling points. The proposed ground truth derived method was implemented to validate four major LAI products.

通讯作者地址: Wang, JD (通讯作者)

Beijing Normal Univ, Res Ctr Remote Sensing & GIS, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China.

地址:

[ 1 ] Beijing Normal Univ, Res Ctr Remote Sensing & GIS, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China

[ 2 ] Beijing Normal Univ, Sch Geog, Beijing 100875, Peoples R China

[ 3 ] Beijing Normal Univ, Beijing Key Lab Remote Sensing Environm & Digital, Beijing 100875, Peoples R China

[ 4 ] Chinese Acad Sci, Inst Tibetan Plateau Res, Lab Tibetan Environm Changes & Land Surface Proc, Beijing 100101, Peoples R China