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.
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References
Bai Z G, Dent D L, Olsson L, Schaepman M E (2008). Proxy global assessment of land degradation. Soil Use Manage, 24(3): 223–234
Bartalev S A, Belward A S, Erchov D V, Isaev A S (2003). A new SPOT4-VEGETATION derived land cover map of Northern Eurasia. Int J Remote Sens, 24(9): 1977–1982
Beck H E, McVicar T R, van Dijk A I J M, Schellekens J, de Jeu R A M, Bruijnzeel L A (2011). Global evaluation of four AVHRR-NDVI data sets: intercomparison and assessment against Landsat imagery. Remote Sens Environ, 115(10): 2547–2563
Fensholt R, Nielsen T T, Stisen S (2006). Evaluation of AVHRR PAL and GIMMS 10-day composite NDVI time series products using SPOT-4 vegetation data for the African continent. Int J Remote Sens, 27(13): 2719–2733
Fensholt R, Proud S R (2012). Evaluation of Earth Observation based global long term vegetation trends — Comparing GIMMS and MODIS global NDVI time series. Remote Sens Environ, 119: 131–147
Fensholt R, Rasmussen K, Nielsen T T, Mbow C (2009). Evaluation of earth observation based long term vegetation trends — Intercomparing NDVI time series trend analysis consistency of Sahel from AVHRR GIMMS, Terra MODIS and SPOT VGT data. Remote Sens Environ, 113(9): 1886–1898
Gao X, Huete A R, Ni W, Miura T (2000). Optical-biophysical relationships of vegetation spectra without background contamination. Remote Sens Environ, 74(3): 609–620
Heumann B W, Seaquist J, Eklundh L, Jönsson P (2007). AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982–2005. Remote Sens Environ, 108(4): 385–392
Hickler T, Eklundh L, Seaquist JW, Smith B, Ardö J, Olsson L, SykesM T, Sjöström M (2005). Precipitation controls Sahel greening trend. Geophys Res Lett, 32(21): L21415
Hu MQ, Mao F, Sun H, Hou Y Y (2011). Study of normalized difference vegetation index variation and its correlation with climate factors in the three-river-source region. Int J Appl Earth Observ Geoinf, 13(1): 24–33
Huete A, Didan K, Miura T, Rodriguez E P, Gao X, Ferreira L G (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ, 83(1–2): 195–213
Lucht W, Prentice I C, Myneni R B, Sitch S, Friedlingstein P, Cramer W, Bousquet P, Buermann W, Smith B (2002). Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 296(5573): 1687–1689
Maisongrande P, Duchemin B, Dedieu G (2004). VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products. Int J Remote Sens, 25(1): 9–14
Mao D H, Wang Z M, Luo L, Ren C Y (2012). Integrating AVHRR and MODIS data to monitor NDVI changes and their relationships with climatic parameters in Northeast China. Int J Appl Earth Observ Geoinf, 18: 528–536
Mildrexler D J, Zhao M, Running S W(2009). Testing a MODIS Global Disturbance Index across North America. Remote Sens Environ, 113(10): 2103–2117
Nemani R R, Keeling C D, Hashimoto H, Jolly W M, Piper S C, Tucker C J, Myneni R B, Running S W (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625): 1560–1563
Pettorelli N, Vik J O, Mysterud A, Gaillard J M, Tucker C J, Stenseth N C (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol, 20(9): 503–510
Rahman H, Dedieu G (1994). SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Int J Remote Sens, 15(1): 123–143
Savitzky A, Golay M J E (1964). Smoothing and differentiation of data by simplified least squares procedure. Anal Chem, 36(8): 1627–1639
Schowengerdt R A (2007). Remote sensing: models and methods for image processing (3rd ed). San Diego: Academic press, 19–20
Sellers P J (1985). Canopy reflectance, photosynthesis and transpiration. Int J Remote Sens, 6(8): 1335–1372
Song Y, Ma M, Veroustraete F (2010). Comparison and conversion of AVHRR GIMMS and SPOT VEGETATION NDVI data in China. Int J Remote Sens, 31(9): 2377–2392
Stöckli R, Vidale P L (2004). European plant phenology and climate as seen in a 20-year AVHRR land-surface parameter dataset. Int J Remote Sens, 25(17): 3303–3330
Symeonakis E, Drake N (2004). Monitoring desertification and land degradation over sub-Saharan Africa. Int J Remote Sens, 25(3): 573–592
Tucker C J (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 8(2): 127–150
Tucker C J, Slayback D A, Pinzon J E, Los S O, Myneni R B, Taylor M G (2001). Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. Int J Biometeorol, 45(4): 184–190
Wang Q, Adiku S, Tenhunen J, Granier A (2005). On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sens Environ, 94(2): 244–255
Wolfe R E, Nishihama M, Fleig A J, Kuyper J A, Roy D P, Storey J C, Patt F S (2002). Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sens Environ, 83(1–2): 31–49
Xiao X, Braswell B, Zhang Q, Boles S, Frolking S, Moore B III (2003). Sensitivity of vegetation indices to atmospheric aerosols: continentalscale observations in Northern Asia. Remote Sens Environ, 84(3): 385–392
Zhang X, Friedl M A, Schaaf C B (2006). Global vegetation phenology from Moderate Resolution Imaging Spectroradiometer (MODIS): evaluation of global patterns and comparison with in situ measurements. J Geophys Res, 111(G4): G04017
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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.
Wei Gao is a senior research scientist and director of the USDA UVB 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).
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 (insitu). He has published 102 scientific papers, owns 8 software copyrights, 2 patents, and 4 books.
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.
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.
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An, Y., Gao, W., Gao, Z. et al. Trend analysis for evaluating the consistency of Terra MODIS and SPOT VGT NDVI time series products in China. Front. Earth Sci. 9, 125–136 (2015). https://doi.org/10.1007/s11707-014-0428-9
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DOI: https://doi.org/10.1007/s11707-014-0428-9