Abstract
A sampling strategy to define elementary sampling units (ESUs) for an entire site at the kilometer scale is an important step in the validation process for moderate-resolution leaf area index (LAI) products. Current LAI-sampling strategies are unable to consider the vegetation seasonal changes and are better suited for single-day LAI product validation, whereas the increasingly used wireless sensor network for LAI measurement (LAINet) requires an optimal sampling strategy across both spatial and temporal scales. In this study, we developed an efficient and robust LAI Sampling strategy based on Multi-temporal Prior knowledge (SMP) for long-term, fixed-position LAI observations. The SMP approach employed multi-temporal vegetation index (VI) maps and the vegetation classification map as a priori knowledge. The SMP approach minimized the multi-temporal bias of the VI frequency histogram between the ESUs and the entire site and maximized the nearest-neighbor index to ensure that ESUs were dispersed in the geographical space. The SMP approach was compared with four sampling strategies including random sampling, systematic sampling, sampling based on the land-cover map and a sampling strategy based on vegetation index prior knowledge using the PROSAIL model-based simulation analysis in the Heihe River basin. The results indicate that the ESUs selected using the SMP method spread more evenly in both the multi-temporal feature space and geographical space over the vegetation cycle. By considering the temporal changes in heterogeneity, the average root-mean-square error (RMSE) of the LAI reference maps can be reduced from 0.12 to 0.05, and the relative error can be reduced from 6.1% to 2.2%. The SMP technique was applied to assign the LAINet ESU locations at the Huailai Remote Sensing Experimental Station in Beijing, China, from 4 July to 28 August 2013, to validate three MODIS C5 LAI products. The results suggest that the average R2, RMSE, bias and relative uncertainty for the three MODIS LAI products were 0.60, 0.33, −0.11, and 12.2%, respectively. The MCD15A2 product performed best, exhibiting a RMSE of 0.20, a bias of −0.07 and a relative uncertainty of 7.4%. Future efforts are needed to obtain more long-term validation datasets using the SMP approach on different vegetation types for validating moderate-resolution LAI products in time series.
Highlights
The leaf area index (LAI) is defined as half of the total foliage area per unit ground surface area [1,2].The LAI is an important biophysical variable in land-surface processes, such as photosynthesis, transpiration and energy balance [3]
The frequency distribution histograms of the 30 selected elementary sampling units (ESUs) generated by different sampling strategies and the histograms of the entire site were compared from June to September (Figure 4)
From July to September, the ESUs were over-sampled by 13.5% at interval 4.5–5.5 in August, but these ESUs were under-sampled by 16.0% at interval 4.5–5.5 in July
Summary
The leaf area index (LAI) is defined as half of the total foliage area per unit ground surface area [1,2].The LAI is an important biophysical variable in land-surface processes, such as photosynthesis, transpiration and energy balance [3]. To capture the surface heterogeneity, more efficient sampling strategies are designed with available a priori knowledge, such as vegetation types, vegetation index or soil types [21,22,23,24]. These methods are widely applied in the VALERI project field campaigns, the Ruokolahti forest observations in Finland and the Barrax cropland observations in Spain [25,26,27]
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