Spatio-temporal Calibration of EO Data for Estimating Forest NPP based on the Diagnostic Prediction Model in the Mid-Latitude Region

Forests play a key role in the global carbon cycle as the largest carbon sink, which accounts for about a quarter of the global greenhouse gases. Extreme weather and meteorological disasters are expected to considerably impact the forest and agriculture. These events will be amplified in both scale and frequency, and negative impacts on the forests are expected in the mid-to-long-term timeline. However, estimating forest NPP on a large scale still implies several limitations such as data collection and quality level. Given the importance of global NPP estimates to guide strategies for improving carbon sequestration, there remains a need to develop new frameworks that are broadly applicable to a large scale. Since forest NPP has spatiotemporal heterogeneity due to different regional status and their complex interactions, fusion modeling is needed to effectively represent the complex effects on forest NPP.
Therefore, this study applied a diagnostic prediction model (DPM) process for predicting forest NPP in Mid-Latitude Region (MLR). The diagnostic prediction model (DPM) is an advanced data fusion method that reflects both the semantic and structural features of earth observation datasets which are foreseeable climate data and precise land observational satellite data. In order to predict forest NPP, the Standardized Precipitation Evapotranspiration Index (SPEI), annual temperature, topographic indices, soil indices, and MODIS NPP images were used, and multi-linear regression and random forest algorithms were applied. Then, the time-series fitting error function was applied in the diagnostic process for maximizing predictive performance. As a result, the calibration results of DPM outperformed the results, which exploit only meteorological and environmental data, in both spatiotemporal and temporal accuracy. 
Through the applicability assessment of DPM for estimating forest productivity and time-series function for the advanced diagnostic processes, this study quantitatively identified forest productivity by only using physical environmental factors based on meteorological and satellite data in MLR where forest resource data is insufficient. This study can provide valuable information to decision-makers for establishing future climate change and forest policy.