Global Land Cover Datasets Research Articles

Integrated Use of Existing Global Land Cover Datasets for Producing a New Global Land Cover Dataset with a Higher Accuracy: A Case Study in Eurasia

It has been commonly acknowledged that the current global mapping projects have encountered the accuracy challenge. By conducting a comparison among the four existing global land cover datasets (MODIS LC, GLC2000, GLCNMO and GLOBCOVER), it has been identified that certain areas’ accuracy has dragged down the overall accuracy of these global land cover datasets. In this paper, those areas have been defined as the “unreliable area”. This study has recollected the training data from the “unreliable area” within the above four mentioned datasets and reclassified the “unreliable area” by using two supervised classifications. The final result has shown that compared with any existing datasets, a relatively higher accuracy has been able to achieve.

Land Cover Map Delineation, for Agriculture Development, Case Study in North Sinai, Egypt Using SPOT4 Data and Geographic Information System

Land cover map for a part of North Sinai was produced using the FAO—Land Cover Classification System (LCCS) of 2004. The standard FAO classification scheme provides a standardized system of classification that can be used to analyze spatial and temporal land cover variability in the study area. This approach also has the advantage of facilitating the integration of Sinai land cover mapping products to be included with the regional and global land cover datasets. The total study area is 7450 km2 (1,773,842) feddans. The landscape classification was performed on SPOT4 data acquired in 2011 using combined multi-spectral bands of 20 meter spatial resolution. Geographic Information System (GIS) was used to edit the classification result in order to reach the maximum possible accuracy. GIS was also used to include all necessary information. The identified vegetative land cover classes of the study area are irrigated herbaceous crops, irrigated tree crops and rain fed tree crops. The non-vegetated land covers in the study area include: bare rock, bare soil, bare soil stony, bare soil very stony, bare soil salt crusts, loose and shifting sands and sand dunes. The water bodies were classified as artificial perennial water bodies (fish ponds and irrigated canals) and natural perennial water bodies as lakes (standing) and rivers (flowing). Artificial surfaces in the study area include linear and non-linear. The produced maps and the statistics of the different land covers are included in the following sub-sections.

Accuracy assessments of the GLOBCOVER dataset using global statistical inventories and FLUXNET site data

The spatio-temporal distribution of land cover provides fundamental data for global climate and environmental change research. In recent decades, five global land cover maps have been produced based on remote sensing data sources and methodologies. Related research have shown that the availability and quality of the first four global land cover datasets are poor at the regional or the continental scale for a variety of reasons. There is still no consensus on the accuracy of the latest global land cover map. Based on comparison of the land cover dataset with the statistical cropland data from FAO and the FLUXNET site data, this paper discusses the accuracy of the fifth global land cover map, namely, the GLOBCOVER dataset, at different spatial scales. At the global scale, the cropland area obtained from the GLOBCOVER dataset is greater than that of the FAO statistical data by 47.06–84.49%, and the land cover types of the GLOBCOVER dataset have a 65.02% consistency with that of the FLUXNET site data. At the continental scale, the difference between cropland areas obtained from the GLOBCOVER dataset and the statistical cropland area vary from −43.42% to 502.36%; continents that have a more accurate cropland area compared to the FAO statistical data tend to be less consistent with the FLUXNET site data. In general, North America has a higher accuracy and Oceania has a lower accuracy. At the country scale, the accuracy estimates vary sharply over a wide range: between −100.00% and 190670.37%. It is recommended that future studies should pay careful attention to the data validation step before using the GLOBCOVER dataset for any particular problem. Future studies are also required for the development of a universal land cover classification system and advanced algorithms for remote sensing classification of global land cover maps.

The rarest and least protected forests in biodiversity hotspots

The goal of biodiversity hotspots is to identify regions around the world where conservation priorities should be focused. We undertake a geographic information system and remote sensing analysis to identify the rarest and least protected forests in biodiversity hotspots. World Wildlife Fund ecoregions with terrestrial forest were subset from 34 biodiversity hotspots and forest cover calculated from GlobCover data at a 300 m pixel resolution. There were 276 ecoregions in 32 biodiversity hotspots classified as containing terrestrial forests. When the first quartile of forest ecoregions was subset based on smallest extent of forest cover in protected areas, there were 69 rare forests identified within 20 biodiversity hotspots. Most rare forest ecoregions (45) occurred on islands or island archipelagos and 47 rare forest ecoregions contained less than 10 % forest cover in protected areas. San Felix-San Ambrosio Islands Temperate Forests, Tubuai Tropical Moist Forests, Maldives-Lakshadweep-Chagos Archipelago Tropical Moist Forests, and Yap Tropical Dry Forests were identified as the least protected and possibly most vulnerable forests within biodiversity hotspots. These ecoregions cover less than 500 km2, forest cover is less than 50 km2, and there are no protected areas. There is a need to update classifications and boundaries of protected areas, insure that islands are included in global land cover datasets, and identify levels of endemism and endangerment within forest ecoregions. This should improve our ability to compare, prioritize, and monitor forests in biodiversity hotspots.

Exploiting the Classification Performance of Support Vector Machines with Multi-Temporal Moderate-Resolution Imaging Spectroradiometer (MODIS) Data in Areas of Agreement and Disagreement of Existing Land Cover Products

Several studies have focused in the past on global land cover (LC) datasets harmonization and inter-comparison and have found significant inconsistencies. Despite the known discrepancies between existing products derived from medium resolution satellite sensor data, little emphasis has been placed on examining these disagreements to improve the overall classification accuracy of future land cover maps. This work evaluates the classification performance of a least square support vector machine (LS-SVM) algorithm with respect to areas of agreement and disagreement between two existing land cover maps. The approach involves the use of time series of Moderate-resolution Imaging Spectroradiometer (MODIS) 250-m Normalized Difference Vegetation Index (NDVI) (16-day composites) and gridded climatic indicators. LS-SVM is trained on reference samples obtained through visual interpretation of Google Earth (GE) high resolution imagery. The core of the training process is based on repeated random splits of the training dataset to select a small set of suitable support vectors optimizing class separability. A large number of independent validation samples spread over three contrasting regions in Europe (Eastern Austria, Macedonia and Southern France) are used to calculate classification accuracies for the LS-SVM NDVI-derived LC map and for two (globally available) LC products: GLC2000 and GlobCover. The LS-SVM LC map reported an overall accuracy of 70%. Classification accuracies ranged from 71% where GlobCover and GLC2000 agreed to 68% for areas of disagreement. Results indicate that existing LC products are as accurate as the LS-SVM LC map in areas of agreement (with little margin for improvements), while classification accuracy is substantially better for the LS-SVM LC map in areas of disagreement. On average, the LS-SVM LC map was 14% and 18% more accurate compared to GlobCover and GLC2000, respectively.

ACCURACY EVALUATION OF TWO GLOBAL LAND COVER DATA SETS OVER WETLANDS OF CHINA

Abstract. Although wetlands are well known as one of the most important ecosystems in the world, there are still few global wetland mapping efforts at present. To evaluate the wetland-related types of data accurately for both the Global Land Cover 2000 (GLC2000) data set and MODIS land cover data set (MOD12Q1), we used the China wetland map of 2000, which was interpreted manually based on Landsat TM images, to examine the precision of these global land cover data sets from two aspects (class area accuracy, and spatial agreement) across China. The results show that the area consistency coefficients of wetland-related types between the two global data sets and the reference data are 77.27% and 56.85%, respectively. However, the overall accuracy of relevant wetland types from GLC2000 is only 19.81% based on results of confusion matrix of spatial consistency, and similarly, MOD12Q1 is merely 18.91%. Furthermore, the accuracy of the peatlands is much lower than that of the water bodies according to the results of per-pixel comparison. The categories where errors occurred frequently mainly include grasslands, croplands, bare lands and part of woodland (deciduous coniferous forest, deciduous broadleaf forest and open shrubland). The possible reasons for the low precision of wetland-related land cover types include (1)the different aims of various products and therefore the inconsistent wetland definitions in their systems; (2) the coarse spatial resolution of satellite images used in global data; (3) Discrepancies in dates when images were acquired between the global data set and the reference data. Overall, the unsatisfactory results highlight that more attention should be paid to the application of these two global data products, especially in wetland-relevant types across China.

Comparison of Water-Related Land Cover Types in Six 1-km Global Land Cover Datasets

Abstract Land cover classification is a fundamental and vital activity that is helpful for understanding natural dynamics and the human impacts of land surface processes. Available multiple 1-km global land cover datasets have been compared to identify classification accuracy and uncertainties for vegetation land cover types, but they have not been adequately compared for water-related land cover types. Six 1-km global land cover datasets were comprehensively examined by focusing on three water-related land cover types (snow and ice, wetlands, and open water). The global mean per-pixel agreement measured by the class-specific consistency is high for snow and ice, medium for open water, and low for wetlands. The agreement is low for snow and ice in low latitudes and high for open water and snow and ice in high latitudes. Areas classified as wetlands in a pixel in one dataset are rarely classified as wetlands in the same pixel in the other five datasets. These areas are most often classified as forest, wetland, or shrub. Areas of snow and ice and open water in some regions are not always chronologically consistent among the datasets because nonsatellite data and different algorithms are used to determine the areas. Further research is necessary to reduce uncertainty in the water-related land cover classification and to develop an advanced classification algorithm that can detect water under a vegetation canopy for improvement in wetland classification. Chronological inconsistency between 1-km land cover datasets and satellite observation periods must also be addressed.

A land-use systems approach to represent land-use dynamics at continental and global scales

Most of the current global land cover datasets and global scale land-use models use a classification of land cover based on the dominant land cover type within a distinct region or pixel. Such a classification disregards the diversity and intensity of human influence on land systems. In this paper we propose a novel way of classification and modeling land-use using a classification based on land-use systems (LUSs) that represent specific combinations of human-environment interactions. A cluster analysis was used to identify and map these LUSs. The analysis accounted for population density, accessibility to market places, land-use/cover types and livestock densities. A conceptual framework was developed to model dynamics in LUSs accounting for both land cover and land management changes. LUSs changes were simulated based on changes in both local socio-economic and biophysical conditions and regional-scale changes in demand for agricultural products. The new land-use systems change model was used in the context of the integrated assessment model IMAGE.

Geo-Wiki: An online platform for improving global land cover

Land cover derived from remotely sensed products is an important input to a number of different global, regional and national scale applications including resource assessments and economic land use models. During the last decade three global land cover datasets have been created, i.e. the GLC-2000, MODIS and GlobCover, but comparison studies have shown that there are large spatial discrepancies between these three products. One of the reasons for these discrepancies is the lack of sufficient in-situ data for the development of these products. To address this issue, a crowdsourcing tool called Geo-Wiki has been developed. Geo-Wiki has two main aims: to increase the amount of in-situ land cover data available for training, calibration and validation, and to create a hybrid global land cover map that provides more accurate land cover information than any current individual product. This paper outlines the components that comprise Geo-Wiki and how they are integrated in the architectural design. An overview of the main functionality of Geo-Wiki is then provided along with the current usage statistics and the lessons learned to date, in particular the need to add a mechanism for feedback and interaction as part of community building, and the need to address issues of data quality. The tool is located at geo-wiki.org.

Comparison and assessment of coarse resolution land cover maps for Northern Eurasia

Information on land cover at global and continental scales is critical for addressing a range of ecological, socioeconomic and policy questions. Global land cover maps have evolved rapidly in the last decade, but efforts to evaluate map uncertainties have been limited, especially in remote areas like Northern Eurasia. Northern Eurasia comprises a particularly diverse region covering a wide range of climate zones and ecosystems: from arctic deserts, tundra, boreal forest, and wetlands, to semi-arid steppes and the deserts of Central Asia. In this study, we assessed four of the most recent global land cover datasets: GLC-2000, GLOBCOVER, and the MODIS Collection 4 and Collection 5 Land Cover Product using cross-comparison analyses and Landsat-based reference maps distributed throughout the region. A consistent comparison of these maps was challenging because of disparities in class definitions, thematic detail, and spatial resolution. We found that the choice of sampling unit significantly influenced accuracy estimates, which indicates that comparisons of reported global map accuracies might be misleading. To minimize classification ambiguities, we devised a generalized legend based on dominant life form types (LFT) (tree, shrub, and herbaceous vegetation, barren land and water). LFT served as a necessary common denominator in the analyzed map legends, but significantly decreased the thematic detail. We found significant differences in the spatial representation of LFT's between global maps with high spatial agreement (above 0.8) concentrated in the forest belt of Northern Eurasia and low agreement (below 0.5) concentrated in the northern taiga-tundra zone, and the southern dry lands. Total pixel-level agreement between global maps and six test sites was moderate to fair (overall agreement: 0.67–0.74, Kappa: 0.41–0.52) and increased by 0.09–0.45 when only homogenous land cover types were analyzed. Low map accuracies at our tundra test site confirmed regional disagreements and difficulties of current global maps in accurately mapping shrub and herbaceous vegetation types at the biome borders of Northern Eurasia. In comparison, tree dominated vegetation classes in the forest belt of the region were accurately mapped, but were slightly overestimated (10%–20%), in all maps. Low agreement of global maps in the northern and southern vegetation transition zones of Northern Eurasia is likely to have important implications for global change research, as those areas are vulnerable to both climate and socio-economic changes.

Assessing effects of temporal compositing and varying observation periods for large-area land-cover mapping in semi-arid ecosystems: Implications for global monitoring

Land-cover is an important parameter in analyzing the state and dynamics of natural and anthropogenic terrestrial ecosystems. Land-cover classes related to semi-arid savannas currently exhibit among the greatest uncertainties in available global land cover datasets. This study focuses on the Kalahari in northeastern Namibia and compares the effects of different composite lengths and observation periods with class-wise mapping accuracies derived from multi-temporal MODIS time series classifications to better understand and overcome quality gaps in mapping semi-arid land-cover types. We further assess the effects of precipitation patterns on mapping accuracy using Tropical Rainfall Measuring Mission (TRMM) observation data. Botanical field samples, translated into the UN Land Cover Classification System (LCCS), were used for training and validation. Different sets of composites (16-day to three-monthly) were generated from MODIS (MOD13Q1) data covering the sample period from 2004 to 2007. Land-cover classifications were performed cumulatively based on annual and inter-annual feature sets with the use of random forests. Woody vegetation proved to be more stable in terms of omission and commission errors compared to herbaceous vegetation types. Generally, mapping accuracy increases with increasing length of the observation period. Analyses of variance (ANOVA) verified that inter-annual classifications significantly improved class-wise mapping accuracies, and confirmed that monthly composites achieved the best accuracy scores for both annual and inter-annual classifications. Correlation analyses using piecewise linear models affirmed positive correlations between cumulative mapping accuracy and rainfall and indicated an influence of seasonality and environmental cues on the mapping accuracies. The consideration of the inter-seasonal variability of vegetation activity and phenology cycling in the classification process further increases the overall classification performance of savanna classes in large-area land-cover datasets. Implications for global monitoring frameworks are discussed based on a conceptual model of the relationship between observation period and accuracy.

Uncertainty in land cover datasets for global land‐surface models derived from 1‐km global land cover datasets

AbstractThe influence of the uncertainties or differences in 1‐km global land cover datasets on a land cover dataset used in land‐surface modelling is explored. The uncertainties in six 1‐km global land cover datasets were found to be transferred to land cover datasets derived by either the dominant land cover type method (DLM) or the area ratio method (ARM). The agreement among the DLM‐derived land cover datasets (the DLM agreement) was higher than the per‐pixel agreement among the six 1‐km global land cover datasets owing to the spatial aggregation effect. The agreement among the ARM‐derived land cover datasets using the ARM (the ARM agreement) was higher than the DLM agreement because of the area ratio retention effect. The area ratios of all land cover types affect the ARM agreement, whereas only the dominant land cover type affects the DLM agreement. The DLM and ARM agreements were both strongly correlated with the per‐pixel agreement among the 1‐km global land cover datasets. Therefore, reducing the uncertainty in the 1‐km global land cover datasets is the key to reducing the uncertainty in the land cover datasets used in land‐surface models. Improving the land cover classification, especially in areas with small homogeneous regions or in transition zones between major land cover types, is also important for reducing the uncertainty in the datasets used for land‐surface models. These sources of uncertainty should be taken into account when interpreting the land‐surface model results. Copyright © 2011 John Wiley & Sons, Ltd.

Production of global land cover data – GLCNMO

Global land cover is one of the fundamental contents of Digital Earth. The Global Mapping project coordinated by the International Steering Committee for Global Mapping has produced a 1-km global land cover dataset – Global Land Cover by National Mapping Organizations. It has 20 land cover classes defined using the Land Cover Classification System. Of them, 14 classes were derived using supervised classification. The remaining six were classified independently: urban, tree open, mangrove, wetland, snow/ice, and water. Primary source data of this land cover mapping were eight periods of 16-day composite 7-band 1-km MODIS data of 2003. Training data for supervised classification were collected using Landsat images, MODIS NDVI seasonal change patterns, Google Earth, Virtual Earth, existing regional maps, and expert's comments. The overall accuracy is 76.5% and the overall accuracy with the weight of the mapped area coverage is 81.2%. The data are available from the Global Mapping project website (http://www.iscgm.org/). The MODIS data used, land cover training data, and a list of existing regional maps are also available from the CEReS website. This mapping attempt demonstrates that training/validation data accumulation from different mapping projects must be promoted to support future global land cover mapping.

Evaluation of four remote sensing based land cover products over China

Precise global/regional land cover mapping is of fundamental importance in studies of land surface processes and modelling. Quantitative assessments of the map quality and classification accuracy for existing land cover maps will help to improve accuracy in future land cover mapping. We compare and evaluate four land cover datasets over China. The datasets include the Version 2 global land cover dataset of IGBP, MODIS land cover map 2001, a global land cover map produced by the University of Maryland, and the land cover map produced by the global land cover for the year 2000 (GLC 2000) project coordinated by the Global Vegetation Monitoring Unit of the European Commission Joint Research Centre. The four maps used different classification systems, which made the comparison difficult. So we first aggregated these maps by reclassifying them using a unified legend system. A large-scale, i.e. 1:100 000 land cover map of China was used as the reference data to validate the four maps. The results show that the GLC2000 land cover map represents the highest accuracy. However, it has obvious local labelling errors and a zero labelling accuracy for the wetland type. The MODIS land cover map ranks second for type area consistency and third for sub-fraction overall accuracy compared with reference data, which may be affected by the local labelling error. The IGBP land cover map has good labelling accuracy, although it has a local labelling error and third consistency for type area. The labelling accuracy and type area consistency for the reference data of UMd land cover map is low. We conclude that the accuracies of all the datasets cannot meet the requirements of land surface modelling. For the reference data, i.e. the 1:100 000 land cover map, the classification system needs to be transferred to a well recognized one that has been used commonly in land surface modelling. In addition, we propose an information fusion strategy to produce a more accurate land cover map of China whose classification system should be compatible with the well-accepted classification system used in land surface modelling.

Accuracy assessment of four global land cover datasets in China

Accuracy assessment of four global land cover datasets in China

Life cycle inventory modelling of land use induced by crop consumption

Background, aims and scope Most life cycle inventory data for crops do not include the ultimate (marginal) land use induced by crop consumption. The aims of this study were to present, document and discuss a method which can solve this problem and, furthermore, to present concrete examples for wheat consumption in Brazil, China, Denmark and the USA. A global scope is applied and the simulated adaptation to increased wheat demand corresponds to a long-term temporal scope under present market conditions with present technology. Materials and methods The economic general equilibrium model, Global Trade Analysis Project (GTAP) is modified and applied. Agricultural statistics and a number of global land use and land cover datasets are used in the modification and the processing of the model output. Some of the land use datasets are processed by use of a geographic information system tool. Results The net expansion of the global agricultural area per tonne of wheat consumed in Brazil, China, Denmark and the USA is estimated at 2,000, 260, 1,700, and 3,200 m 2 , respectively. For Brazil, the expansion mainly affects tropical evergreen forest. For China and the USA, the expansion mainly affects boreal deciduous forest, savanna, open shrubland and tropical evergreen forest, and for Denmark, it mainly affects savanna, tropical evergreen forest and dense shrubland. The areas affected are quantified in the land use life cycle inventory (LCI) produced for the four countries. Discussion The method applied allows for an even more detailed land use LCI than the one presented in this study. Results are influenced by existing global trade patterns and their inertia. Such aspects should be acknowledged in life cycle assessment (LCA). The method takes its starting point in consumption rather than production. Conclusions The method presented makes it possible to simulate the main mechanisms of the global agricultural system and thereby construct an LCI containing the land use induced by crop consumption in a given region and the nature types (biomes) affected. The results are sensitive to changes in the so-called Armington elasticities representing the inertia of global trade patterns. It is considered reasonable to double the standard elasticities in the GTAP model for the construction of LCI data. Wheat consumption in different countries result in different land use consequences due to differences in trade patterns, which are governed by transport and trade costs, among other factors. Recommendations and perspectives The modelling could be improved by incorporating a mechanism simulating

Remotely sensed estimation of cropland in China: a comparison of the maps derived from four global land cover datasets

the University of Maryland Dataset (UMD), the International Geosphere-Biosphere Programme Data and Information System Cover (IGBP-DISCover), the Moderate Resolution Imaging Spectrometer (MODIS) dataset, and the Global Land Cover 2000 (GLC2000). Although produced primarily for global applications, these global land cover datasets are often used for national or regional applications. The objective of this study was to examine the similarities and differences between these datasets in mapping Chinese cropland and to highlight their strengths and weaknesses when used at the national and regional scales. First, the total areas (in ha) of the cropland mapped by the four datasets were compared at the Chinese national, regional, and provincial levels. Second, we performed a pixel-by-pixel comparison of the spatial locations of cropland mapped by the four datasets. These datasets were then separately compared with the Chinese 1996 National Land Survey Dataset (NLSD-1996) to assess their accuracy in cropland mapping. The results show that, despite some agreement, there are discrepancies between the datasets, and the type and extent varies between regions. There are many reasons for the discrepancies, the main ones being the diverse land cover classification schemes, coarse spatial resolution, per-pixel classification approach, and landscape heterogeneity.

Investigating the rank‐size relationship of urban areas using land cover maps

We investigated the possibility that the rank‐size rule can be applied to the relationship between urban size and rank order. Accordingly, using a global land cover data set, we clustered contiguous urban grid cells, calculated the area in each cluster, and ranked urban areas in each of the countries studied. This research revealed that Zipf's law can be applied to the relationship between urban area and rank order as well as to city populations. Comparisons were made in some countries, and it was shown that the urban area rank‐size rule was free from administrative boundaries. Finally, in Japan, using land‐use maps for several times in recent history, changes in rank‐size were investigated. As a result, it was found that the slopes for urban areas did not change vis‐à‐vis their rank in a double logarithmic graph and that only the x and y interception changed.

Using satellite‐based rainfall estimates for streamflow modelling: Bagmati Basin

AbstractIn this study, we have described a hydrologic modelling system that uses satellite‐based rainfall estimates and weather forecast data for the Bagmati River Basin of Nepal. The hydrologic model described is the US Geological Survey (USGS) Geospatial Stream Flow Model (GeoSFM). The GeoSFM is a spatially semidistributed, physically based hydrologic model. We have used the GeoSFM to estimate the streamflow of the Bagmati Basin at Pandhera Dovan hydrometric station. To determine the hydrologic connectivity, we have used the USGS Hydro1k DEM dataset. The model was forced by daily estimates of rainfall and evapotranspiration derived from weather model data. The rainfall estimates used for the modelling are those produced by the National Oceanic and Atmospheric Administration Climate Prediction Centre and observed at ground rain gauge stations. The model parameters were estimated from globally available soil and land cover datasets – the Digital Soil Map of the World by FAO and the USGS Global Land Cover dataset. The model predicted the daily streamflow at Pandhera Dovan gauging station. The comparison of the simulated and observed flows at Pandhera Dovan showed that the GeoSFM model performed well in simulating the flows of the Bagmati Basin.

Development of a two-band enhanced vegetation index without a blue band

The enhanced vegetation index (EVI) was developed as a standard satellite vegetation product for the Terra and Aqua Moderate Resolution Imaging Spectroradiometers (MODIS). EVI provides improved sensitivity in high biomass regions while minimizing soil and atmosphere influences, however, is limited to sensor systems designed with a blue band, in addition to the red and near-infrared bands, making it difficult to generate long-term EVI time series as the normalized difference vegetation index (NDVI) counterpart. The purpose of this study is to develop and evaluate a 2-band EVI (EVI2), without a blue band, which has the best similarity with the 3-band EVI, particularly when atmospheric effects are insignificant and data quality is good. A linearity-adjustment factor β is proposed and coupled with the soil-adjustment factor L used in the soil-adjusted vegetation index (SAVI) to develop EVI2. A global land cover dataset of Terra MODIS data extracted over land community validation and FLUXNET test sites is used to develop the optimal parameter (L, β and G) values in EVI2 equation and achieve the best similarity between EVI and EVI2. The similarity between the two indices is evaluated and demonstrated with temporal profiles of vegetation dynamics at local and global scales. Our results demonstrate that the differences between EVI and EVI2 are insignificant (within ± 0.02) over a very large sample of snow/ice-free land cover types, phenologies, and scales when atmospheric influences are insignificant, enabling EVI2 as an acceptable and accurate substitute of EVI. EVI2 can be used for sensors without a blue band, such as the Advanced Very High Resolution Radiometer (AVHRR), and may reveal different vegetation dynamics in comparison with the current AVHRR NDVI dataset. However, cross-sensor continuity relationships for EVI2 remain to be studied.