eMODIS: A User-Friendly Data Source

Jenkerson, Calli B.; Maiersperger, T.; Schmidt, Gail E.

doi:10.3133/ofr20101055

Cited by 104 publications

(80 citation statements)

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“…The eMODIS product consists of 10-day maximum value NDVI composites at 250m resolution (Jenkerson et al 2010). Although the composites are produced every five days resulting in six temporally overlapping composites per month, we only used the composites for day 1-10, 11-20, and 21-last day of each month.…”

Section: Study Area and Datamentioning

confidence: 99%

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Vrieling

Meroni

Mude

et al. 2016

Remote Sensing of Environment

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Section: Study Area and Datamentioning

confidence: 99%

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Vrieling

Meroni

Mude

et al. 2016

Remote Sensing of Environment

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“…A report issued by the USGS in 2010 [10] described the eMODIS processing flow in detail; however, subsequent changes have been implemented into the system. This paper provides documentation for the current status of the eMODIS processing system, especially as it pertains to the conterminous U.S. data stream.…”

Section: Objectivesmentioning

confidence: 99%

Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition

et al. 2015

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Monitoring systems benefit from high temporal frequency image data collected from the Moderate Resolution Imaging Spectroradiometer (MODIS) system. Because of near-daily global coverage, MODIS data are beneficial to applications that require timely information about vegetation condition related to drought, flooding, or fire danger. Rapid satellite data streams in operational applications have clear benefits for monitoring vegetation, especially when information can be delivered as fast as changing surface conditions. An "expedited" processing system called "eMODIS" operated by the U.S. Geological Survey provides rapid MODIS surface reflectance data to operational applications in less than 24 h offering tailored, consistently-processed information products that complement standard MODIS products. We assessed eMODIS quality and consistency by comparing to standard MODIS data. Only land data with known high quality were analyzed in a central U.S. study area. When compared to standard MODIS (MOD/MYD09Q1), the eMODIS Normalized Difference Vegetation Index (NDVI) maintained a strong, significant relationship to standard MODIS NDVI, whether from morning (Terra) or afternoon (Aqua) orbits. The Aqua eMODIS data were more prone to noise than the Terra data, likely due to differences in the internal cloud mask used in MOD/MYD09Q1 or compositing rules. Post-processing temporal smoothing decreased noise in eMODIS data.

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“…We selected the carbon flux light response and vapor pressure, or "non-rectangular hyperbolic method" flux partitioning models [2][3][4] over Q 10 and short-term exponential fit models (which model Re as a function of temperature based on night-time data) and rectangular hyperbolic fit models (which use the relationship between photosynthetic active radiation (PAR) and daytime NEP to model Re) because it produces the most reasonable C flux estimates and data gap filling [25], particularly in non-forest, low canopy height systems. NEP data from the flux towers were summarized into weekly periods aligning with 7-day expedited Moderate Resolution Imaging Spectroradiometer (eMODIS) Normalized Difference Degetation Index (NDVI) composites [26]. The training of the grassland and cropland NEP mapping models for weekly NEP was focused on records from 30 flux towers-15 measuring CO 2 exchange (CFlux) in cropland sites and 15 measuring CFlux in grassland sites ( Figure 1, Table S1).…”

Section: Flux Tower Datamentioning

confidence: 99%

“…These input data sets provided the models with samples of how NEP behaves in accordance with variability of various spatial and temporal environmental characteristics. The first group of input spatial data used in model training included weekly eMODIS NDVI [26] and a simple temporal dataset with values ranging from 1 to 52 for the week of the year. NDVI is derived from visible and near-infrared (NIR) light reflectance measurements (Equation (1)) and correlates with the photosynthetic potential of vegetation [27].…”

Section: Input Spatial Datamentioning

confidence: 99%

“…NDVI is derived from visible and near-infrared (NIR) light reflectance measurements (Equation (1)) and correlates with the photosynthetic potential of vegetation [27]. The eMODIS NDVI product [26] is processed using the same data source and atmospheric correction algorithms as the standard MODIS collection 5 data product [28]. However, the eMODIS process uses a compositing algorithm which is largely dependent on maximum value-NDVI compositing, scan angle, and data quality flags to filter through input reflectance with bad quality, negative values, clouds, snow cover, or low view angles.…”

Section: Input Spatial Datamentioning

confidence: 99%

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Grassland and Cropland Net Ecosystem Production of the U.S. Great Plains: Regression Tree Model Development and Comparative Analysis

et al. 2016

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This paper presents the methodology and results of two ecological-based net ecosystem production (NEP) regression tree models capable of up scaling measurements made at various flux tower sites throughout the U.S. Great Plains. Separate grassland and cropland NEP regression tree models were trained using various remote sensing data and other biogeophysical data, along with 15 flux towers contributing to the grassland model and 15 flux towers for the cropland model. The models yielded weekly mean daily grassland and cropland NEP maps of the U.S. Great Plains at 250 m resolution for 2000-2008. The grassland and cropland NEP maps were spatially summarized and statistically compared. The results of this study indicate that grassland and cropland ecosystems generally performed as weak net carbon (C) sinks, absorbing more C from the atmosphere than they released from 2000 to 2008. Grasslands demonstrated higher carbon sink potential (139 g C·m −2 ·year −1 ) than non-irrigated croplands. A closer look into the weekly time series reveals the C fluctuation through time and space for each land cover type.

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eMODIS: A User-Friendly Data Source

Cited by 104 publications

References 0 publications

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Early assessment of seasonal forage availability for mitigating the impact of drought on East African pastoralists

Application-Ready Expedited MODIS Data for Operational Land Surface Monitoring of Vegetation Condition

Grassland and Cropland Net Ecosystem Production of the U.S. Great Plains: Regression Tree Model Development and Comparative Analysis

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