Leaf area index (LAI) has been investigated in multiple studies, either by means of visible/near-infrared and shortwave-infrared or thermal infrared remotely sensed data, with various degrees of accuracy. However, it is not yet known how the integration of visible/near and shortwave-infrared and thermal infrared data affect estimates of LAI. In this study, we examined the utility of Landsat-8 thermal infrared data together with its spectral data from the visible/near and shortwave-infrared region to quantify the LAI of a mixed temperate forest in Germany. A ﬁeld campaign was carried out in August 2015, in the Bavarian Forest National Park, concurrent with the time of the Landsat-8 overpass, and a number of forest structural parameters, including LAI and proportion of vegetation cover, were measured for 37 plots. Anormalised difference vegetation index threshold method was applied to calculate land surface emissivity and land surface temperature and their relations to LAI were investigated. Next, the relation between LAI and eight commonly used vegetation indices were examined using the visible/near-infrared and shortwave-infrared remote sensing data. Finally, the artiﬁcial neural network was used to predict the LAI using: (i) reﬂectance data from the Landsat-8 operational land imager (OLI) sensor; (ii) reﬂectance data from the OLI sensor and the land surface emissivity; and (iii) reﬂectance data from the OLI sensor and land surface temperature. A stronger relationship was observed between LAI and land surface emissivity compared to that between LAI and land surface temperature. In general, LAI was predicted with relatively low accuracy by means of the vegetation indices. Among the studied vegetation indices, the modiﬁed vegetation index had the highest accuracy for LAI prediction (R2CV = 0.33, RMSECV = 1.21 m2m−2). Nevertheless, using the visible/near-infrared and shortwave-infrared spectral data in the artiﬁcial neural network, the prediction accuracy of LAI increased (R2CV = 0.58, RMSECV = 0.83 m2m−2). The integration of reﬂectance and land surface emissivity signiﬁcantly improved the prediction accuracy of the LAI (R2CV = 0.81, RMSECV = 0.63 m2m−2). For the ﬁrst time, our results demonstrate that the combination of Landsat-8 reﬂectance spectral data from the visible/near-infrared and shortwave-infrared domain and thermal infrared data can boost the estimation accuracy of the LAI in a forest ecosystem. This ﬁnding has implication for the prediction of other vegetation biophysical, or possibly biochemical variables using thermal infrared satellite remote sensing data, as well as regional mapping of LAI when coupled with a canopy radiative transfer model.
|Date made available||22 Sep 2019|