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

Youzhi AN , Wei GAO , Zhiqiang GAO , Chaoshun LIU , Runhe SHI

Front. Earth Sci. ›› 2015, Vol. 9 ›› Issue (1) : 125 -136.

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Front. Earth Sci. ›› 2015, Vol. 9 ›› Issue (1) : 125 -136. DOI: 10.1007/s11707-014-0428-9
RESEARCH ARTICLE
RESEARCH ARTICLE

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

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Youzhi AN, Wei GAO, Zhiqiang GAO, Chaoshun LIU, Runhe SHI. Trend analysis for evaluating the consistency of Terra MODIS and SPOT VGT NDVI time series products in China. Front. Earth Sci., 2015, 9(1): 125-136 DOI:10.1007/s11707-014-0428-9

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References

[1]

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

[2]

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

[3]

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

[4]

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

[5]

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

[6]

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

[7]

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

[8]

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

[9]

Hickler T, Eklundh L, Seaquist J W, Smith B, Ardö J, Olsson L, Sykes M T, Sjöström M (2005). Precipitation controls Sahel greening trend. Geophys Res Lett, 32(21): L21415

[10]

Hu M Q, 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

[11]

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

[12]

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

[13]

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

[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

[15]

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

[16]

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

[17]

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

[18]

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

[19]

Savitzky A, Golay M J E (1964). Smoothing and differentiation of data by simplified least squares procedure. Anal Chem, 36(8): 1627–1639

[20]

Schowengerdt R A (2007). Remote sensing: models and methods for image processing (3rd ed). San Diego: Academic press, 19–20

[21]

Sellers P J (1985). Canopy reflectance, photosynthesis and transpiration. Int J Remote Sens, 6(8): 1335–1372

[22]

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

[23]

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

[24]

Symeonakis E, Drake N (2004). Monitoring desertification and land degradation over sub-Saharan Africa. Int J Remote Sens, 25(3): 573–592

[25]

Tucker C J (1979). Red and photographic infrared linear combinations for monitoring vegetation. Remote Sens Environ, 8(2): 127–150

[26]

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

[27]

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

[28]

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

[29]

Xiao X, Braswell B, Zhang Q, Boles S, Frolking S, Moore B Ⅲ (2003). Sensitivity of vegetation indices to atmospheric aerosols: continental-scale observations in Northern Asia. Remote Sens Environ, 84(3): 385–392

[30]

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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