کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
4459326 | 1621282 | 2012 | 13 صفحه PDF | دانلود رایگان |
![عکس صفحه اول مقاله: Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery Remote sensing of canopy light use efficiency in temperate and boreal forests of North America using MODIS imagery](/preview/png/4459326.png)
Light use efficiency (LUE) is an important variable characterizing plant eco-physiological functions and refers to the efficiency at which absorbed solar radiation is converted into photosynthates. The estimation of LUE at regional to global scales would be a significant advantage for global carbon cycle research. Traditional methods for canopy level LUE determination require meteorological inputs which cannot be easily obtained by remote sensing. Here we propose a new algorithm that incorporates the enhanced vegetation index (EVI) and a modified form of land surface temperature (Tm) for the estimation of monthly forest LUE based on Moderate Resolution Imaging Spectroradiometer (MODIS) imagery. Results demonstrate that a model based on EVI × Tm parameterized from ten forest sites can provide reasonable estimates of monthly LUE for temperate and boreal forest ecosystems in North America with an R2 of 0.51 (p < 0.001) for the overall dataset. The regression coefficients (a, b) of the LUE–EVI × Tm correlation for these ten sites have been found to be closely correlated with the average EVI (EVI_ave, R2 = 0.68, p = 0.003) and the minimum land surface temperature (LST_min, R2 = 0.81, p = 0.009), providing a possible approach for model calibration. The calibrated model shows comparably good estimates of LUE for another ten independent forest ecosystems with an overall root mean square error (RMSE) of 0.055 g C per mol photosynthetically active radiation. These results are especially important for the evergreen species due to their limited variability in canopy greenness. The usefulness of this new LUE algorithm is further validated for the estimation of gross primary production (GPP) at these sites with an RMSE of 37.6 g C m− 2 month− 1 for all observations, which reflects a 28% improvement over the standard MODIS GPP products. These analyses should be helpful in the further development of ecosystem remote sensing methods and improving our understanding of the responses of various ecosystems to climate change.
► The potential of EVI as a proxy of LUE depends on the standard deviation in EVI.
► A new LUE algorithm is proposed for temperate and boreal forests in North America.
► The RMSE of the new model for ten independent sites is 0.055 g C mol− 1 PAR.
► A 28% improvement is observed in GPP using this LUE algorithm compared to MODIS GPP.
Journal: Remote Sensing of Environment - Volume 118, 15 March 2012, Pages 60–72