文献类型：期刊文献

英文题名：Spatial Downscaling of Vegetation Productivity in the Forest From Deep Learning

作者：Yu, Tao[1,2] Pang, Yong[1,2] Sun, Rui[3,4,5] Niu, Xiaodong[1,2]

第一作者：余涛;Yu, Tao

通信作者：Pang, Y[1];Pang, Y[2]

机构：[1]Chinese Acad Forestry, Inst Forest Resource Informat Tech, Beijing 100091, Peoples R China;[2]Natl Forestry & Grassland Adm, Key Lab Forestry Remote Sensing & Informat Syst, Beijing 100091, Peoples R China;[3]Beijing Normal Univ, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China;[4]Chinese Acad Sci, Inst Remote Sensing & Digital Earth, State Key Lab Remote Sensing Sci, Beijing 100875, Peoples R China;[5]Beijing Normal Univ, Inst Remote Sensing Sci & Engn, Fac Geog Sci, Beijing Engn Res Ctr Global Land Remote Sensing P, Beijing 100875, Peoples R China

年份：2022

卷号：10

起止页码：104449-104460

外文期刊名：IEEE ACCESS

收录：;EI(收录号：20224112871229);Scopus(收录号：2-s2.0-85139453634);WOS:【SCI-EXPANDED(收录号:WOS:000866428000001)】；

基金：This work was supported in part by the Fundamental Research Funds of the Chinese Academy of Forestry (CAF) under Grant CAFYBB2021SY009 and Grant CAFYBB2022QA002, and in part by the National Natural Science Foundation of China under Grant 32101522.

语种：英文

外文关键词：Forestry; Spatial resolution; Remote sensing; Deep learning; Deep learning; Biological system modeling; Vegetation mapping; Land surface; Global navigation satellite system; Downscaling; vegetation productivity; deep learning; GLASS; validation

摘要：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.