Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning

Accurately estimating vegetation productivity in the forest areas is important for studying the terrestrial ecosystem and carbon cycles. Global LAnd Surface Satellite (GLASS) vegetation production datasets provide new long-term basic products of gross primary production (GPP) and net primary production (NPP) for monitoring the issues related with carbon exchange and carbon storage. But the coarse spatial resolution of the GLASS GPP/NPP products have limited their application in ecosystem service assessment in regional scales. In this paper, a spatial downscaling method based on GLASS vegetation production datasets and four typical deep learning methods (deep neural network, convolutional neural network, back propagation neural network and recurrent neural network) was proposed to generate high resolution GPP/NPP in the forest areas in the upper Luanhe River basin in the north of Hebei Province in China. Then the downscaled GPP/NPP were validated with ground measurement data and reference high resolution GPP/NPP data, and the accuracy of downscaled GPP/NPP from different deep learning methods was compared. Results of this paper indicated the applicability and feasibility of deep learning methods in downscaling GPP/NPP. Direct validation and cross validation demonstrated that downscaled GPP/NPP using convolutional neural network obtained the highest accuracy.

Automated summary

In this paper, a spatial downscaling method based on deep learning methods was used to generate high resolution GPP/NPP data in the forest areas of the upper Luanhe River basin in China. The results showed that the downscaled GPP/NPP using convolutional neural network achieved the highest accuracy.