Abstract
Global products of leaf area index (LAI) usually show large uncertainties in sparsely vegetated areas because the understory contribution is not negligible in reflectance modeling for the case of low to intermediate canopy cover. Therefore, many efforts have been made to include understory properties in LAI estimation algorithms. Compared with the conventional data bank method, estimation of forest understory properties from satellite data is superior in studies at a global or continental scale over long periods. However, implementation of the current remote sensing method based on multi-angular observations is complicated. As an alternative, a simple method to retrieve understory NDVI (NDVIu) for sparse boreal forests was proposed in this study. The method is based on the fact that the bidirectional variation in NDVIu is smaller than that in canopy-level NDVI. To retrieve NDVIu for a certain pixel, linear extrapolation was applied using pixels within a 5 × 5 target-pixel-centered window. The NDVI values were reconstructed from the MODIS BRDF data corresponding to eight different solar-view angles. NDVIu was estimated as the average of the NDVI values corresponding to the position in which the stand NDVI had the smallest angular variation. Validation by a noise-free simulation data set yielded high agreement between estimated and true NDVIu, with R2 and RMSE of 0.99 and 0.03, respectively. Using the MODIS BRDF data, we achieved an estimate of NDVIu close to the in situ measured value (0.61 vs. 0.66 for estimate and measurement, respectively) and reasonable seasonal patterns of NDVIu in 2010 to 2013. The results imply a potential application of the retrieved NDVIu to improve the estimation of overstory LAI for sparse boreal forests and ultimately to benefit studies on carbon cycle modeling over high-latitude areas.
Highlights
Leaf area index (LAI), defined as one-half the total green leaf area per unit of horizontal ground surface area, is an important parameter for land-surface and climate modeling
The simulation samples with leaf area index of overstory canopy (LAIo) between 0.5 and 2.0 were used for the regression analysis. This is used to mimic the situation we might encounter in the study area: the canopy leaf area index (LAI) varies in the range of 0.5 to 2.0 within a 5 × 5 window
The proposed method was applied to the MODIS bidirectional reflectance distribution function (BRDF) data for 2010 to 2013 with day of year (DOY) of 145 to 257, periods during which almost no snow cover was observed in the study area
Summary
Leaf area index (LAI), defined as one-half the total green leaf area per unit of horizontal ground surface area, is an important parameter for land-surface and climate modeling. It describes the vegetation canopy structure and can be related to the energy absorption capacity of vegetation [1]. Previous validation studies have determined that these products show high uncertainties in sparsely vegetated and savanna areas (e.g., [6]) This is because the understory cannot be neglected in reflectance modeling in the case of low to intermediate canopy cover [7]. Large spatial and temporal variations in understory layers have been observed, even among the same species [11,12,13]
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