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Frontiers of Earth Science

ISSN 2095-0195

ISSN 2095-0209(Online)

CN 11-5982/P

Postal Subscription Code 80-963

2018 Impact Factor: 1.205

Front. Earth Sci.    2015, Vol. 9 Issue (1) : 125-136    https://doi.org/10.1007/s11707-014-0428-9
RESEARCH ARTICLE
Trend analysis for evaluating the consistency of Terra MODIS and SPOT VGT NDVI time series products in China
Youzhi AN1, Wei GAO1,2, Zhiqiang GAO2,3(), Chaoshun LIU1, Runhe SHI1
1. Key Laboratory of Geographic Information Science, Ministry of Education, East China Normal University, Joint Laboratory for Environmental Remote Sensing and Data Assimilation, ECNU & CEODE, CAS, Shanghai 200062, China
2. Natural Resource Ecology Laboratory, Colorado State University, Fort Collins CO 80523, USA
3. Yantai Institute of Coastal Zone Research, Chinese Academy of Sciences, Yantai 264003, China
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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     
Corresponding Author(s): Zhiqiang GAO   
Issue Date: 01 January 2023
 Cite this article:   
Youzhi AN,Wei GAO,Zhiqiang GAO, et al. Trend analysis for evaluating the consistency of Terra MODIS and SPOT VGT NDVI time series products in China[J]. Front. Earth Sci., 2015, 9(1): 125-136.
 URL:  
https://academic.hep.com.cn/fesci/EN/10.1007/s11707-014-0428-9
https://academic.hep.com.cn/fesci/EN/Y2015/V9/I1/125
1 Z G Bai, D L Dent, L Olsson, M E Schaepman (2008). Proxy global assessment of land degradation. Soil Use Manage, 24(3): 223–234
https://doi.org/10.1111/j.1475-2743.2008.00169.x
2 S A Bartalev, A S Belward, D V Erchov, A S Isaev (2003). A new SPOT4-VEGETATION derived land cover map of Northern Eurasia. Int J Remote Sens, 24(9): 1977–1982
https://doi.org/10.1080/0143116031000066297
3 H E Beck, T R McVicar, A I J M van Dijk, J Schellekens, R A M de Jeu, L A Bruijnzeel (2011). Global evaluation of four AVHRR–NDVI data sets: intercomparison and assessment against Landsat imagery. Remote Sens Environ, 115(10): 2547–2563
https://doi.org/10.1016/j.rse.2011.05.012
4 R Fensholt, T T Nielsen, S Stisen (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
https://doi.org/10.1080/01431160600567761
5 R Fensholt, S R Proud (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
https://doi.org/10.1016/j.rse.2011.12.015
6 R Fensholt, K Rasmussen, T T Nielsen, C Mbow (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
https://doi.org/10.1016/j.rse.2009.04.004
7 X Gao, A R Huete, W Ni, T Miura (2000). Optical–biophysical relationships of vegetation spectra without background contamination. Remote Sens Environ, 74(3): 609–620
https://doi.org/10.1016/S0034-4257(00)00150-4
8 B W Heumann, J Seaquist, L Eklundh, P Jönsson (2007). AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982–2005. Remote Sens Environ, 108(4): 385–392
https://doi.org/10.1016/j.rse.2006.11.025
9 T Hickler, L Eklundh, J W Seaquist, B Smith, J Ardö, L Olsson, M T Sykes, M Sjöström (2005). Precipitation controls Sahel greening trend. Geophys Res Lett, 32(21): L21415
https://doi.org/10.1029/2005GL024370
10 M Q Hu, F Mao, H Sun, Y Y Hou (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
https://doi.org/10.1016/j.jag.2010.06.003
11 A Huete, K Didan, T Miura, E P Rodriguez, X Gao, L G Ferreira (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sens Environ, 83(1–2): 195–213
https://doi.org/10.1016/S0034-4257(02)00096-2
12 W Lucht, I C Prentice, R B Myneni, S Sitch, P Friedlingstein, W Cramer, P Bousquet, W Buermann, B Smith (2002). Climatic control of the high-latitude vegetation greening trend and Pinatubo effect. Science, 296(5573): 1687–1689
https://doi.org/10.1126/science.1071828 pmid: 12040194
13 P Maisongrande, B Duchemin, G Dedieu (2004). VEGETATION/SPOT: an operational mission for the Earth monitoring; presentation of new standard products. Int J Remote Sens, 25(1): 9–14
https://doi.org/10.1080/0143116031000115265
14 D H Mao, Z M Wang, L Luo, C Y Ren (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
https://doi.org/10.1016/j.jag.2011.10.007
15 D J Mildrexler, M Zhao, S W Running (2009). Testing a MODIS Global Disturbance Index across North America. Remote Sens Environ, 113(10): 2103–2117
https://doi.org/10.1016/j.rse.2009.05.016
16 R R Nemani, C D Keeling, H Hashimoto, W M Jolly, S C Piper, C J Tucker, R B Myneni, S W Running (2003). Climate-driven increases in global terrestrial net primary production from 1982 to 1999. Science, 300(5625): 1560–1563
https://doi.org/10.1126/science.1082750 pmid: 12791990
17 N Pettorelli, J O Vik, A Mysterud, J M Gaillard, C J Tucker, N C Stenseth (2005). Using the satellite-derived NDVI to assess ecological responses to environmental change. Trends Ecol Evol, 20(9): 503–510
https://doi.org/10.1016/j.tree.2005.05.011 pmid: 16701427
18 H Rahman, G Dedieu (1994). SMAC: a simplified method for the atmospheric correction of satellite measurements in the solar spectrum. Int J Remote Sens, 15(1): 123–143
https://doi.org/10.1080/01431169408954055
19 A Savitzky, M J E Golay (1964). Smoothing and differentiation of data by simplified least squares procedure. Anal Chem, 36(8): 1627–1639
https://doi.org/10.1021/ac60214a047
20 R A Schowengerdt (2007). Remote sensing: models and methods for image processing (3rd ed). San Diego: Academic press, 19–20
21 P J Sellers (1985). Canopy reflectance, photosynthesis and transpiration. Int J Remote Sens, 6(8): 1335–1372
https://doi.org/10.1080/01431168508948283
22 Y Song, M Ma, F Veroustraete (2010). Comparison and conversion of AVHRR GIMMS and SPOT VEGETATION NDVI data in China. Int J Remote Sens, 31(9): 2377–2392
https://doi.org/10.1080/01431160903002409
23 R Stöckli, P L Vidale (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
https://doi.org/10.1080/01431160310001618149
24 E Symeonakis, N Drake (2004). Monitoring desertification and land degradation over sub-Saharan Africa. Int J Remote Sens, 25(3): 573–592
https://doi.org/10.1080/0143116031000095998
25 C J Tucker (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 8(2): 127–150
https://doi.org/10.1016/0034-4257(79)90013-0
26 C J Tucker, D A Slayback, J E Pinzon, S O Los, R B Myneni, M G Taylor (2001). Higher northern latitude normalized difference vegetation index and growing season trends from 1982 to 1999. Int J Biometeorol, 45(4): 184–190
https://doi.org/10.1007/s00484-001-0109-8 pmid: 11769318
27 Q Wang, S Adiku, J Tenhunen, A Granier (2005). On the relationship of NDVI with leaf area index in a deciduous forest site. Remote Sens Environ, 94(2): 244–255
https://doi.org/10.1016/j.rse.2004.10.006
28 R E Wolfe, M Nishihama, A J Fleig, J A Kuyper, D P Roy, J C Storey, F S Patt (2002). Achieving sub-pixel geolocation accuracy in support of MODIS land science. Remote Sens Environ, 83(1–2): 31–49
https://doi.org/10.1016/S0034-4257(02)00085-8
29 X Xiao, B Braswell, Q Zhang, S Boles, S Frolking, B Ⅲ Moore (2003). Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia. Remote Sens Environ, 84(3): 385–392
https://doi.org/10.1016/S0034-4257(02)00129-3
30 X Zhang, M A Friedl, C B Schaaf (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
https://doi.org/10.1029/2006JG000217
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