An Artificial Intelligence Approach to Predict Gross Primary Productivity in the Forests of South Korea Using Satellite Remote Sensing Data  
[PERSON]  
1 Forest Ecology and Climate Change Division, National Institute of Forest Science, Seoul 02455, Korea; [EMAIL_ADDRESS] (B.L. ); [EMAIL_ADDRESS] (E.-S.K. ); [EMAIL_ADDRESS] (K.J. ); [EMAIL_ADDRESS] (J.-H.L.) [PERSON]  
2 Geomatics Research Institute, Pukyong National University, Busan 48513, Korea; [EMAIL_ADDRESS] 2  
[PERSON]  
1 Forest Ecology and Climate Change Division, National Institute of Forest Science, Seoul 02455, Korea; [EMAIL_ADDRESS] (B.L. ); [EMAIL_ADDRESS] (E.-S.K. ); [EMAIL_ADDRESS] (K.J. ); [EMAIL_ADDRESS] (J.-H.L.) [PERSON]  
1 Forest Ecology and Climate Change Division, National Institute of Forest Science, Seoul 02455, Korea; [EMAIL_ADDRESS] (B.L. ); [EMAIL_ADDRESS] (E.-S.K. ); [EMAIL_ADDRESS] (K.J. ); [EMAIL_ADDRESS] (J.-H.L.) [PERSON]  
3 National Center for Agromoteeorology, Seoul 08826, Korea; [EMAIL_ADDRESS] 3  
[PERSON]  
1 Forest Ecology and Climate Change Division, National Institute of Forest Science, Seoul 02455, Korea; [EMAIL_ADDRESS] (B.L. ); [EMAIL_ADDRESS] (E.-S.K. ); [EMAIL_ADDRESS] (K.J. ); [EMAIL_ADDRESS] (J.-H.L.) [PERSON]  
4 Department of Applied Plant Science, Chonnam National University, Gwangju 61186, Korea; [EMAIL_ADDRESS] 4  
[PERSON]  
5 Department of Spatial Information Engineering, Pukyong National University, Busan 48513, Korea 5  
Footnote 4: email: {[EMAIL_ADDRESS], jh@korea. ecosystem physiology, such as nitrogen and proteins supporting leaf carboxylation capacity, the plant structure for exposing photosynthetically active materials to light, and the stomatal restrictions on CO\({}_{2}\) diffusion [6; 7]. For measurement and prediction of forest GPP, eddy covariance (EC) flux tower observations, satellite observations, and process-based models are often used. The EC method enables the calculation of forest GPP using the net ecosystem exchange (NEE) derived from direct measurements of the vertical turbulence transport of the atmosphere according to meteorological conditions [8]. More than 500 EC flux observation sites are operated worldwide and provide reasonably accurate estimates of carbon exchange. However, the point-based observations are not sufficient to cover spatially continuous areas [9; 10]. Satellite observations and process-based models are used to overcome the limitation of spatial continuity of the EC measurements [11]. However, satellite GPP products may not fully ensure the reliability of data, thus they require a more advanced calibration process suitable for local-scale applications [12]. Process-based models for forest GPP were designed to take account of physiological and environmental factors of forest ecosystems at various scales [13; 14; 15]. However, it is difficult for the process-based models to handle complex and nonlinear aspects of biological, physiological, and chemical mechanisms of plant organisms under different climatic and topographic conditions. Furthermore, a number of input parameters related to physiology and phenology of vegetation should be precisely calibrated for the process-based models [16; 17], which is a very challenging task for local-scale applications. Forest GPP is a complex and nonlinear problem because its relationships with photosynthetic rates are spatially and temporally heterogeneous. Nowadays, artificial intelligence (AI) approaches are adopted because AI can efficiently handle the problems of nonlinearity and complexity related to forest ecosystems [18]. AI includes machine learning models, such as support vector machine (SVM) and random forest (RF), and neural network models, such as artificial neural network (ANN) and deep neural network (DNN). Previous forest studies using AI methods include the classification of tree species [19; 20; 21; 22] and the estimation of carbon or heat fluxes [23; 24; 25]. They have sufficiently shown the potential of AI for forest applications.