IMHE OpenIR  > 数字山地与遥感应用中心
Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China
Xinyao Xie1,2; Ainong Li1; Huaan Jin1; Gaofei Yin1; Jinhu Bian1
Corresponding AuthorAinong Li
2018
Source PublicationRemote Sens
ISSN 2072-4292
Volume10Issue:4Pages:647
SubtypeArticle
Contribution Rank1
Abstract

Due to the spatial heterogeneity of land surfaces, downscaling is an important issue in the development of carbon cycle models when evaluating the role of ecosystems in the global carbon cycle. In this study, a downscaling algorithm was developed to model gross primary productivity (GPP) at 500 m in a time series over rugged terrain, which considered the effects of spatial heterogeneity on carbon flux simulations. This work was carried out for a mountainous area with an altitude ranging from 2606 to 4744 m over the Gongga Mountain (Sichuan Province, China). In addition, the Moderate Resolution Imaging Spectroradiometer (MODIS) GPP product at 1 km served as the primary dataset for the downscaling algorithm, and the 500 m MODIS GPP product was used as the reference dataset to evaluate the downscaled GPP results. Moreover, in order to illustrate the advantages and benefits of the proposed downscaling method, the downscaled results in this work, along with ordinary kriging downscaled results, spline downscaled results and inverse distance weighted (IDW) downscaled results, were compared to the MODIS GPP at 500 m. The results showed that (1) the GPP difference between the 500 m MODIS GPP and the proposed downscaled GPP results was primarily in the range of [−1, 1], showing that both vegetation heterogeneity factors (i.e., LAI) and topographic factors (i.e., altitude, slope and aspect) were useful for GPP downscaling; (2) the proposed downscaled results (R2 = 0.89, RMSE = 1.03) had a stronger consistency with the 500 m MODIS GPP than those of the ordinary kriging downscaled results (R2 = 0.43, RMSE = 1.36), the spline downscaled results (R2 = 0.40, RMSE = 1.50) and the IDW downscaled results (R2 = 0.42, RMSE = 1.10) for all Julian days; and (3) the inconsistency between MODIS GPP at 500 m and 1 km increased with the increase in altitude and slope. The proposed downscaling algorithm could provide a reference when considering the effects of spatial heterogeneity on carbon flux simulations and retrieving other fine resolution ecological-physiology parameters (e.g., net primary productivity and evaporation) over topographically complex terrains.

 

KeywordDownscaling Gpp Spatial Heterogeneity Remote Sensing Subpixel Information
DOI10.3390/rs10040647
Indexed BySCI
Language英语
WOS IDWOS:000435187500159
Citation statistics
Cited Times:4[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/21456
Collection数字山地与遥感应用中心
Corresponding AuthorAinong Li
Affiliation1.Research Center for Digital Mountain and Remote Sensing Application, Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu 610041, China;
2.University of Chinese Academy of Sciences, Beijing 100049, China
First Author Affilication中国科学院水利部成都山地灾害与环境研究所
Corresponding Author Affilication中国科学院水利部成都山地灾害与环境研究所
Recommended Citation
GB/T 7714
Xinyao Xie,Ainong Li,Huaan Jin,et al. Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China[J]. Remote Sens,2018,10(4):647.
APA Xinyao Xie,Ainong Li,Huaan Jin,Gaofei Yin,&Jinhu Bian.(2018).Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China.Remote Sens,10(4),647.
MLA Xinyao Xie,et al."Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China".Remote Sens 10.4(2018):647.
Files in This Item:
File Name/Size DocType Version Access License
Spatial Downscaling (2622KB)期刊论文出版稿开放获取CC BY-NC-SAView Application Full Text
Related Services
Recommend this item
Bookmark
Usage statistics
Export to Endnote
Google Scholar
Similar articles in Google Scholar
[Xinyao Xie]'s Articles
[Ainong Li]'s Articles
[Huaan Jin]'s Articles
Baidu academic
Similar articles in Baidu academic
[Xinyao Xie]'s Articles
[Ainong Li]'s Articles
[Huaan Jin]'s Articles
Bing Scholar
Similar articles in Bing Scholar
[Xinyao Xie]'s Articles
[Ainong Li]'s Articles
[Huaan Jin]'s Articles
Terms of Use
No data!
Social Bookmark/Share
File name: Spatial Downscaling of Gross Primary Productivity Using Topographic and Vegetation Heterogeneity Information: A Case Study in the Gongga Mountain Region of China.pdf
Format: Adobe PDF
All comments (0)
No comment.
 

Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.