Abstract
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.
Highlights
The leaf area index (LAI), defined as half the total developed area of green leaves per unit ground horizontal surface area [1], is an essential vegetation parameter and plays an important role in crop growth monitoring, yield estimation, ecosystem productivity, and land surface modeling [2,3,4,5,6,7,8]
We proposed an upscaling algorithm to obtain the representative ground truth of LAI time series from in situ LAI data measured by wireless sensor network (WSN) observation system
A two-step upscaling algorithm was proposed to obtain the representative ground truth of LAI time series in heterogeneous surfaces based on in situ LAI data measured by the WSN observation system
Summary
The leaf area index (LAI), defined as half the total developed area of green leaves per unit ground horizontal surface area [1], is an essential vegetation parameter and plays an important role in crop growth monitoring, yield estimation, ecosystem productivity, and land surface modeling [2,3,4,5,6,7,8]. Remote sensing methods provide an effective way to acquire LAI with different spatial and temporal resolutions [9]. The LAI products must be validated against in situ measurements to provide the uncertainties associated with these products, which are essential for their application and improvement of the LAI inversion algorithm [13,14]. Validation of LAI products is a very difficult task, regarding their kilometric spatial resolution and vegetation dynamic changes [14]. The remote sensing footprint is much larger than the representative area of in situ LAI measurement due to surface heterogeneity; a pragmatic approach is required to upscale in situ measurements to the scale of the satellite footprint. There are two major upscaling schemes for validation of LAI products. The simplest is to average a number of in situ
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