Estimating time-series leaf area index based on recurrent nonlinear autoregressive neural networks with exogenous inputs
作者： Linna Chai , Yonghua Qu , Lixin Zhang , Shunlin Liang , Jindi Wang
DOI: 10.1080/01431161.2012.671553
关键词:
摘要: The leaf area index LAI is a key parameter in many meteorological, environmental and agricultural models. At present, global products from several sensors have been released. These single sensor-based are generally discontinuous time cannot characterize the status of natural vegetation growth very well. In this study, by fusing Moderate Resolution Imaging Spectroradiometer MODIS Satellite Pour l'Observation de la Terre SPOT VEGETATION products, time-series LAIs were used to train recurrent nonlinear autoregressive neural networks with exogenous inputs NARXNNs for six typical types. included reflectances red, near-infrared shortwave infrared bands as well corresponding sun-viewing angles. subsequently served predict LAI. validation results show that predicted NARXNN not only more continuous stable than function but also much closer ground truth. Thus, proposed method may be helpful improving quality
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