Trend analysis for evaluating the consistency of Terra MODIS and SPOT VGT NDVI time series products in China

Abstract

The Normalized Difference Vegetation Index (NDVI) is an important vegetation greenness indicator. Compared to the AVHRR GIMMS NDVI data, the availability of two datasets with 1 km spatial resolution, i.e., Terra MODIS (MOD13A3) monthly composite and SPOT Vegetation (VGT) 10-day composite NDVI, extends the application dimensions at spatial and temporal scales. An overlapping period of 12 years between the datasets now makes it possible to investigate the consistency of the two datasets. Linear regression trend analysis was performed to compare the two datasets in this study. The results show greater consistency in regression slopes in the semi-arid regions of northern China. Alternatively, the results show only slight changes in the Terra MODIS NDVI regression slope in most areas of southern China whereas the SPOT VGT NDVI shows positive changes over a large area. The corresponding regression slope values between Terra MODIS and SPOT VGT NDVI datasets from the linear fit had a fair agreement in the spatial dimension. However, larger positive and negative differences were observed at the junction of the three regions (East China, Central China, and North China). These differences can be partially explained by the positive standard deviation differences distributed over a large area at the junction of these three regions. This study demonstrated that Terra MODIS and SPOT VGT NDVI have a relatively robust basis for characterizing vegetation changes in annual NDVI in most of the semi-arid and arid regions in northern China.

Keywords

Terra MODIS NDVI / SPOT VGT NDVI / trend analysis / correlation analysis

Youzhi An received his M.S. degree from Shanghai Normal University, Shanghai, China, in 2011. He is currently a Ph.D student in the Department of Geography at East China Normal University, Shanghai, China. His current research interests focus on remote sensing of vegetation ecology. E-mail: [e-mail]

Wei Gao is a senior research scientist and director of the USDA UV-B Monitoring and Research Program and the Center of Remote Sensing and Modeling for Agricultural Sustainability, Natural Resource Ecology Laboratory, Colorado State University. He is also a joint professor with the Department of Soil and Crop Sciences, Colorado State University. He received his Ph.D from Purdue University and did his Postdoctoral training at the National Center for Atmospheric Research. His research interests include atmospheric radiation and modeling, remote sensing applications, regional climate/ecosystem modeling, geographic information systems, UV radiation, and other climate stress factor influences on ecosystems and their impact on climate change. He has published numerous academic papers and edited numerous books, scientific proceedings, and special journal issues. He is a fellow of the International Society for Optical Engineering (SPIE). E-mail: [e-mail]

Zhiqiang Gao received his Ph.D from the Institute of Remote Sensing Applications, Chinese Academy of Sciences, Beijing, China in 1998. His major field of study is Cartography and Geographical Information Systems. He studied and worked at the Chinese Academy of Sciences for 19 years. His research includes applications of remote sensing, geographical information systems, land use/land cover cartography, ecosystem modeling, impacts of UV-B on crops using an eco-model, and applications for coupling of field (in-situ). He has published 102 scientific papers, owns 8 software copyrights, 2 patents, and 4 books. E-mail: [e-mail]

Chaoshun Liu received his Ph.D in atmospheric remote sensing science and technology from Nanjing University of Information Science and Technology in 2008. He has since been employed at the East China Normal University. His research involves atmospheric radiation and modeling, surface energy flux and terrestrial remote sensing, aerosol retrieval and climate effects, calibration and atmospheric correction, and other atmospheric parameter retrievals. E-mail: [e-mail]

Runhe Shi is an Associate Professor in the Department of Geography at East China Normal University, China. He is working at the Key Laboratory of Geographic Information Science, Ministry of Education, China, and serves as an Assistant Director. He obtained his B.S. in Geography from East China Normal University in 2001 and Ph.D in Cartography and Geographic Information Systems from the Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, in 2006. His primary area of research is quantitative remote sensing including retrieval of plant biochemistry, greenhouse gases, and particulate matters in the atmosphere. He has authored more than 50 refereed journal articles and conference papers. He is also the holder of two patents for data processing of remote sensing images. E-mail: [e-mail]

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Acknowledgements

This paper was supported by the Key Research Program of the Chinese Academy of Sciences (Grant No. KZZD-EW-14), the National Natural Science Foundation of China (Grant Nos. 41171334 and 41071278), Ecological Innovation & Breeding Project (Y254021031,355031061), USDA NIFA Project (2010-34263-21075), the Research Fund for the Doctoral Program of Higher Education (20100076120024), and the Fundamental Research Funds for the Central Universities (East China Normal University), Ministry of Education. The authors thank the NASA/MODIS Land Discipline Group and Spot Image/Vito for producing and sharing the MODIS LAND and SPOT VEGETATION data. We extend these thanks to Yuan Zhang for his assistance in reviewing the English version of this paper. We want to acknowledge suggestions from the anonymous reviewers, which helped to improve this manuscript.

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2014 Higher Education Press and Springer-Verlag Berlin Heidelberg