Many process-based models for carbon flux predictions have faced a wide range of uncertainty issues. The complex interactions between the atmosphere and the forest ecosystems can lead to uncertainties in the model result. On the other hand, artificial intelligence (AI) techniques, which are novel methods to resolve complex and nonlinear problems, have shown a possibility for forest ecological applications. This study is the first step to present an objective comparison between multiple AI models for the daily forest gross primary productivity (GPP) prediction using satellite remote sensing data. We built the AI models such as support vector machine (SVM), random forest (RF), artificial neural network (ANN), and deep neural network (DNN) using in-situ observations from an eddy covariance (EC) flux tower and satellite remote sensing data such as albedo, aerosol, temperature, and vegetation index. We focused on the Gwangneung site from the Korea Regional Flux Network (KoFlux) in South Korea, 2006–2015. As a result, the DNN model outperformed the other three models through an intensive hyperparameter optimization, with the correlation coefficient (CC) of 0.93 and the mean absolute error (MAE) of 0.68 g m⁻² d⁻¹ in a 10-fold blind test. We showed that the DNN model also performed well under conditions of cold waves, heavy rain, and an autumnal heatwave. As future work, a comprehensive comparison with the result of process-based models will be necessary using a more extensive EC database from various forest ecosystems.

中文翻译：

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利用卫星遥感数据预测韩国森林总初级生产力的人工智能方法

许多基于过程的碳通量预测模型都面临着各种各样的不确定性问题。大气层与森林生态系统之间复杂的相互作用可能导致模型结果的不确定性。另一方面，人工智能（AI）技术是解决复杂和非线性问题的新颖方法，已经显示出森林生态应用的可能性。这项研究是第一步，提出了使用卫星遥感数据进行每日森林总初级生产力（GPP）预测的多个AI模型之间的客观比较。我们建立了AI模型，例如支持向量机（SVM），随机森林（RF），人工神经网络（ANN），深度神经网络（DNN），它使用涡流协方差（EC）通量塔和卫星遥感数据（如反照率，气溶胶，温度和植被指数）进行的原位观测。2006-2015年，我们关注了韩国韩国通量网络（KoFlux）的光陵站点。结果，DNN模型经过密集的超参数优化，优于其他三个模型，相关系数（CC）为0.93，平均绝对误差（MAE）为0.68 gm在十倍盲试验中为^-2 d ^-1。我们证明了DNN模型在冷浪，大雨和秋季热浪的条件下也表现良好。作为未来的工作，有必要使用来自各种森林生态系统的更广泛的EC数据库，将基于过程的模型结果进行全面比较。