Next Article in Journal
Unmanned Aerial Vehicle (UAV) for Monitoring Soil Erosion in Morocco
Next Article in Special Issue
Reconstructing the Spatio-Temporal Development of Irrigation Systems in Uzbekistan Using Landsat Time Series
Previous Article in Journal
Lipa, B. et al. Tsunami Arrival Detection with High Frequency (HF) Radar. Remote Sens. 2012, 4, 1448-1461
Previous Article in Special Issue
Monitoring Biennial Bearing Effect on Coffee Yield Using MODIS Remote Sensing Imagery
Article

How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study

1
Geography Department, Humboldt-Universität zu Berlin, Unter den Linden 6, D-10099 Berlin, Germany
2
Environmental Remote Sensing and Geoinformatics Department, University of Trier, Behringstr. 15, D-54286 Trier, Germany
3
Department of Geography and Geology, University of Copenhagen, Øster Voldgade 10, DK-1350 Copenhagen, Denmark
*
Author to whom correspondence should be addressed.
Remote Sens. 2012, 4(11), 3364-3389; https://doi.org/10.3390/rs4113364
Received: 31 August 2012 / Revised: 24 October 2012 / Accepted: 31 October 2012 / Published: 6 November 2012
(This article belongs to the Special Issue Advances in Remote Sensing of Agriculture)
Detailed information from global remote sensing has greatly advanced ourunderstanding of Earth as a system in general and of agricultural processes in particular.Vegetation monitoring with global remote sensing systems over long time periods iscritical to gain a better understanding of processes related to agricultural change over longtime periods. This specifically relates to sub-humid to semi-arid ecosystems, whereagricultural change in grazing lands can only be detected based on long time series. Byintegrating data from different sensors it is theoretically possible to construct NDVI timeseries back to the early 1980s. However, such integration is hampered by uncertainties inthe comparability between different sensor products. To be able to rely on vegetationtrends derived from integrated time series it is therefore crucial to investigate whether vegetation trends derived from NDVI and phenological parameters are consistent acrossproducts. In this paper we analyzed several indicators of vegetation change for a range ofagricultural systems in Inner Mongolia, China, and compared the results across differentsatellite archives. Specifically, we compared two of the prime NDVI archives—AVHRR Global Inventory Modeling and Mapping Studies (GIMMS) and SPOT Vegetation (VGT)NDVI. Because a true accuracy assessment of long time series is not possible, we furthercompared SPOT VGT NDVI with NDVI from MODIS Terra as a benchmark. We foundhigh similarities in interannual trends, and also in trends of the seasonal amplitude andintegral between SPOT VGT and MODIS Terra (r > 0.9). However, we observedconsiderable disagreements in NDVI-derived trends between AVHRR GIMMS and SPOTVGT. We detected similar discrepancies for trends based on phenological parameters, suchas amplitude and integral of NDVI curves corresponding to seasonal vegetation cycles.Inconsistencies were partially related to land cover and vegetation density. Differentpre-processing schemes and the coarser spatial resolution of AVHRR GIMMS introducedfurther uncertainties. Our results corroborate findings from other studies that vegetationtrends derived from AVHRR GIMMS data not always reflect true vegetation changes. Amore thorough understanding of the factors introducing uncertainties in AVHRR GIMMStime series is needed, and we caution against using AVHRR GIMMS data in regionalstudies without applying regional sensitivity analyses. View Full-Text
Keywords: NDVI; trend analysis; AVHRR GIMMS; SPOT VGT; MODIS Terra; Inner Mongolia; agriculture NDVI; trend analysis; AVHRR GIMMS; SPOT VGT; MODIS Terra; Inner Mongolia; agriculture
Show Figures

MDPI and ACS Style

Yin, H.; Udelhoven, T.; Fensholt, R.; Pflugmacher, D.; Hostert, P. How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study. Remote Sens. 2012, 4, 3364-3389. https://doi.org/10.3390/rs4113364

AMA Style

Yin H, Udelhoven T, Fensholt R, Pflugmacher D, Hostert P. How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study. Remote Sensing. 2012; 4(11):3364-3389. https://doi.org/10.3390/rs4113364

Chicago/Turabian Style

Yin, He, Thomas Udelhoven, Rasmus Fensholt, Dirk Pflugmacher, and Patrick Hostert. 2012. "How Normalized Difference Vegetation Index (NDVI) Trendsfrom Advanced Very High Resolution Radiometer (AVHRR) and Système Probatoire d’Observation de la Terre VEGETATION (SPOT VGT) Time Series Differ in Agricultural Areas: An Inner Mongolian Case Study" Remote Sensing 4, no. 11: 3364-3389. https://doi.org/10.3390/rs4113364

Find Other Styles

Article Access Map by Country/Region

1
Only visits after 24 November 2015 are recorded.
Back to TopTop