Deriving Maximum Light Use Efficiency from Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation
Citation
Dong, T., Liu, J., Qian, B., Jing, Q., Croft, H., Chen, J., Wang, J., Huffman, T., Shang, J., Chen, P. (2017). Deriving Maximum Light Use Efficiency from Crop Growth Model and Satellite Data to Improve Crop Biomass Estimation. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, [online] 10(1), 104-117. http://dx.doi.org/10.1109/JSTARS.2016.2605303
Plain language summary
Maximum light use efficiency is an important parameter in some biomass estimation models, such as the Production Efficiency Models using remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of estimating this parameter from satellite remote sensing data. The parameter at the plot level was estimated first by assimilating field measured green leaf area index and biomass into a crop model, the Simple Algorithm for Yield estimate model, and was then correlated with a few Landsat-8 vegetation indices to develop regression models. The parameter was then mapped using the best regression model from remote sensing images. Our results indicate that the maximum light use efficiency is affected by environmental stresses, such as leaf nitrogen deficiency, and could be estimated from vegetation indices. For instance, it can be estimated using remote sensing data acquired at the milk stage, using the two-band enhanced vegetation index for winter wheat and the green chlorophyll index for maize. Using a variable maximum light use efficiency in a light use efficiency model can improve biomass estimation by about 15%.
Abstract
Maximum light use efficiency (LUEmax ) is an important parameter in biomass estimation models (e.g., the Production Efficiency Models (PEM)) based on remote sensing data; however, it is usually treated as a constant for a specific plant species, leading to large errors in vegetation productivity estimation. This study evaluates the feasibility of deriving spatially variable crop LUEmax from satellite remote sensing data. LUEmax at the plot level was retrieved first by assimilating field measured green leaf area index and biomass into a crop model (the Simple Algorithm for Yield estimate model), and was then correlated with a few Landsat-8 vegetation indices (VIs) to develop regression models. LUEmax was then mapped using the best regression model from a VI. The influence factors on LUEmax variability were also assessed. Contrary to a fixed LUEmax , our results suggest that LUEmax is affected by environmental stresses, such as leaf nitrogen deficiency. The strong correlation between the plot-level LUEmax and VIs, particularly the two-band enhanced vegetation index for winter wheat (Triticum aestivum) and the green chlorophyll index for maize (Zea mays) at the milk stage, provided a potential to derive LUEmax from remote sensing observations. To evaluate the quality of LUEmax derived from remote sensing data, biomass of winter wheat and maize was compared with that estimated using a PEM model with a constant LUEmax and the derived variable LUEmax . Significant improvements in biomass estimation accuracy were achieved (by about 15.0% for the normalized root-mean-square error) using the derived variable LUEmax . This study offers a new way to derive LUEmax for a specific PEM and to improve the accuracy of biomass estimation using remote sensing.