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

Remote Sensing

Yuechan Shi³  Jindi Wang³  Jun Qin²  Yonghua Qu³  Alfredo R. Huete¹ 

[1] State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing 100875, China;;Laboratory of Tibetan Environment Changes and Land Surface Processes, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China; E-Mail:;State Key Laboratory of Remote Sensing Science, Research Center for Remote Sensing and GIS, and School of Geography, Beijing Normal University, Beijing 100875, China; E-Mails:

关键词: upscaling algorithm;    leaf area index (LAI);    in situ measurement;    ground truth;    Heihe River basin;   

DOI  :  10.3390/rs71012887

来源: mdpi

【 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.

【 授权许可】

CC BY   
© 2015 by the authors; licensee MDPI, Basel, Switzerland.