MODIS Vegetation Indices

Huete, Alfredo; Didan, Kamel; Leeuwen, Willem J. D. van; Miura, Tomoaki; Glenn, E.

doi:10.1007/978-1-4419-6749-7_26

Cited by 170 publications

(168 citation statements)

References 75 publications

Supporting

Mentioning

127

Contrasting

Unclassified

Order By: Relevance

“…The differences in behaviour between the NDVI and EVI curves, such as the saturation of NDVI in dense vegetation and its higher sensitivity to less intense vegetation such as that of Peatlands in addition the more defined growing season peaks of EVI, is in agreement with the observations of Huete et al (2011). These effects are also evident when the two different Grassland types are distinguished.…”

Section: Vegetation Index Time-seriessupporting

confidence: 84%

“…Thus, the MODIS Terra 16-day composites (MOD13Q1), with a spatial resolution of 250m were chosen as the major data source for this study, with the optimal image date inside the fixed 16-day period determined by a constrained view-angle maximum-value-composite (CV-MVC) algorithm (Huete et al, 2011). The MOD13Q1 product contains two vegetation indices (NDVI and EVI); as well as four spectral bands: blue, red, near-infrared (NIR) and mid-infrared (MIR).…”

Section: Modis Datamentioning

confidence: 99%

“…Although universally applied, NDVI has its drawbacks. It is susceptible to soil and atmospheric influences and saturates in dense vegetation canopies (Huete et al, 2011). Sensor specific VIs such as the MODIS Enhanced Vegetation Index (EVI) captures vegetation phenology more accurately and can demonstrate sharper growing season peaks and greater sensitivity to canopy structure differences (Xiao et al, 2003;Huete et al, 2011).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

Nitze

Barrett

Cawkwell

2015

International Journal of Applied Earth Observation and Geoinfor

119

View full text Add to dashboard Cite

Abstract:The analysis and classification of land cover is one of the principal applications in terrestrial remote sensing. Due to the seasonal variability of different vegetation types, the ability to discriminate different land cover types changes over time. Multi-temporal classification can help to improve the classification accuracies, but different constraints, such as financial restrictions or atmospheric conditions, may limit their application. The optimisation of image acquisition timing and frequencies can help to increase the effectiveness of the classification process. For this purpose, the Feature Importance measure of the state-of-the art machine learning method Random Forest was used to determine the optimal image acquisition periods for a general (Grassland, Forest, Water, Settlement, Peatland) and grassland specific (Improved Grassland, Semi-Improved Grassland) land cover classification in central Ireland on a 9-year time-series of MODIS Terra 16 day composite data (MOD13Q1). Feature Importances for each acquisition period of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) were calculated for both classification scenarios. In the general land cover classification, the months December and January showed the highest, and July and August the lowest separability for both VIs over the entire nine-year period. This temporal separability was reflected in the classification accuracies, where the optimal choice of image dates outperformed the worst image date by 13% using NDVI and 5% using EVI on a mono-temporal analysis. With the addition of the next best image periods to the data input the classification accuracies converged quickly to their limit at around 8 to 10 images. The binary classification schemes, using two classes only, showed a stronger seasonal dependency with a higher intra-annual, but lower inter-annual variation. Nonetheless anomalous weather conditions, such as the cold winter of 2009/2010 can alter the temporal separability pattern significantly. Due to the extensive use of the NDVI for land cover discrimination, the findings of this study should be transferrable to data from other optical sensors. However, the high impact of outliers from the general climatic pattern highlights the limitation of spatial transferability to locations with different climatic and land cover conditions. The use of high-temporal, moderate resolution data such as MODIS in conjunction with machine-learning techniques proved to be a good base for the prediction of image acquisition timing for optimal land cover classification results.2

show abstract

Section: Vegetation Index Time-seriessupporting

confidence: 84%

Section: Modis Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

Nitze

Barrett

Cawkwell

2015

International Journal of Applied Earth Observation and Geoinfor

119

View full text Add to dashboard Cite

show abstract

“…The enhanced vegetation index (EVI) was introduced to overcome these limitations, as it includes a visible blue band to reduce atmospheric effects, calibration terms to reduce the effects of soil background, and does not saturate as severely as NDVI at high LAI (Huete et al, 2002). EVI has also been used in a wide array of global change studies, but post 2000, when the Moderate-Resolution Imaging Spectroradiometric (MODIS) satellite sensor began retrieving visible blue reflectance (see Huete et al, 2010 for a review).…”

Section: Published By Copernicus Publications On Behalf Of the Europementioning

confidence: 99%

Global assessment of Vegetation Index and Phenology Lab (VIP) and Global Inventory Modeling and Mapping Studies (GIMMS) version 3 products

et al. 2016

View full text Add to dashboard Cite

Abstract. Earth observation-based long-term global vegetation index products are used by scientists from a wide range of disciplines concerned with global change. Intercomparison studies are commonly performed to keep the user community informed on the consistency and accuracy of such records as they evolve. In this study, we compared two new records: (1) Global Inventory Modeling and Mapping Studies (GIMMS) normalized difference vegetation index version 3 (NDVI3g) and (2) Vegetation Index and Phenology Lab (VIP) version 3 NDVI (NDVI3v) and enhanced vegetation index 2 (EVI3v). We evaluated the two records via three experiments that addressed the primary use of such records in global change research: (1) leaf area index (LAI), (2) vegetation climatology, and (3) trend analysis of the magnitude and timing of vegetation productivity. Unlike previous global studies, a unique Landsat 30 m spatial resolution and in situ LAI database for major crop types on five continents was used to evaluate the performance of not only NDVI3g and NDVI3v but also EVI3v. The performance of NDVI3v and EVI3v was worse than NDVI3g using the in situ data, which was attributed to the fusion of GIMMS and MODIS data in the VIP record. EVI3v has the potential to contribute biophysical information beyond NDVI3g and NDVI3v to global change studies, but we caution its use due to the poor performance of EVI3v in this study. Overall, the records were most consistent at northern latitudes during the primary growing season and southern latitudes and the tropics throughout much of the year, while the records were less consistent at northern latitudes during green-up and senescence, and in the great deserts of the world throughout much of the year. These patterns led to general agreement (disagreement) between trends in the magnitude (timing) of NDVI over the study period. Bias in inter-calibration of the VIP record at northernmost latitudes was suspected to contribute most to these discrepancies.

show abstract

“…Even for pixels where this backup algorithm had not been applied, atmospheric correction cannot fully eliminate this atmospheric effect. Therefore, EVI values generated under high and climatological aerosol contamination may remain biased (Huete et al, 2011;Kaufman et al, 2005). Residual effects and artifacts will persist, and aerosol miscorrections will still occur where high aerosol pixels are present, which cannot be corrected to the same accuracy as low aerosol pixels can.…”

Section: Aerosol Contamination On Evi Anomaly Analysismentioning

confidence: 99%

Monitoring the impact of aerosol contamination on the drought-induced decline of gross primary productivity

Zhang

Zhu

et al. 2015

International Journal of Applied Earth Observation and Geoinfor

View full text Add to dashboard Cite

MODIS Vegetation Indices

Cited by 170 publications

References 75 publications

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

Temporal optimisation of image acquisition for land cover classification with Random Forest and MODIS time-series

Global assessment of Vegetation Index and Phenology Lab (VIP) and Global Inventory Modeling and Mapping Studies (GIMMS) version 3 products

Monitoring the impact of aerosol contamination on the drought-induced decline of gross primary productivity

Contact Info

Product

Resources

About