An Algorithm to Produce Temporally and Spatially Continuous MODIS-LAI Time Series

Gao, Feng; Morisette, Jeffrey T.; Wolfe, Robert E.; Ederer, G.; Pedelty, J. A.; Masuoka, E.; Myneni, Ranga B.; Tan, Bin; Nightingale, Joanne

doi:10.1109/lgrs.2007.907971

Cited by 204 publications

(127 citation statements)

References 14 publications

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“…Because raw MODIS 250 m data are noisy and have a large number of missing observations, we temporally smoothed the data using a modified asymmetric Gaussian filter within an augmented version of TIMESAT (Jonsson & Elklunh, 2002), and then fit a curve to the data that approximates the phenological pattern to fill data gaps (Gao et al, 2008;Tan et al, 2011). The result is a high-quality dataset shown to be suitable for both classification and direct assessment of EVI values (Tan et al, 2011).…”

Section: Remot E Sensing Dat Amentioning

confidence: 99%

Detecting change in urban areas at continental scales with MODIS data

Mertes

Schneider

Sulla‐Menashe

et al. 2015

Remote Sensing of Environment

151

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Abstract. Urbanization is one of the most important components of global environmentalchange, yet most of what we know about urban areas is at the local scale. Remote sensing of urban expansion across large areas provides information on the spatial and temporal patterns of growth that are essential for understanding differences in socioeconomic and political factors that spur different forms of development, as well the social, environmental, and climatic impacts that result. However, mapping urban expansion globally is challenging: urban areas have a small footprint compared to other land cover types, their features are small, they are heterogeneous in both material composition and configuration, and the form and rates of new development are often highly variable across locations. Here we demonstrate a new methodology for monitoring urban land expansion at continental to global scales using Moderate Resolution Imaging Spectroradiometer (MODIS) data. The new method focuses on resolving the spectral and temporal ambiguities between urban/non-urban land and stable/changed areas by: (1) spatially constraining the study extent to known locations of urban land; (2) integrating multi-temporal data from multiple satellite data sources to classify ca 2010 urban extent; and (3) mapping newly built areas (2000)(2001)(2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010) within the 2010 urban land extent using a multi-temporal composite change detection approach based on MODIS 250 m annual maximum enhanced vegetation index (EVI). We test the method in 15 countries in East-Southeast Asia experiencing different rates and manifestations of urban expansion. A two-tiered accuracy assessment shows that the approach characterizes urban change across a variety of socicoeconomic/political and ecological/climatic conditions with good accuracy (70-91% overall accuracy by country, 69-89% by biome). The 250 m EVI data not only improve the classification results, but are capable of distinguishing between change and no-change areas in urban areas. Over 80% of ii the error in the change detection is related to human decision making or error propagation, rather than algorithm error. As such, these methods hold great potential for routine monitoring of urban change, as well as provide a consistent and up-to-date dataset on urban extent and expansion for a rapidly evolving region.

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Section: Remot E Sensing Dat Amentioning

confidence: 99%

Detecting change in urban areas at continental scales with MODIS data

Mertes

Schneider

Sulla‐Menashe

et al. 2015

Remote Sensing of Environment

151

View full text Add to dashboard Cite

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“…2006 gap-filled and smoothed MODIS LAI and FPAR data MOD15A2GFS at 1 km resolution and every 8 days [Gao et al, 2008;Myneni et al, 2011] from the U.S. North American Carbon Program (NACP) are processed and regridded onto the WRF/CMAQ 12 km grid cells. LAI FLUXNET measurements [Baldocchi, 2008], which are often very limited and generally only available for a few selected days each year, are obtained from three FLUXNET sites (US-Ha1 = Harvard Forest in Massachusetts, US-Ne1 = Mead irrigated maize in Nebraska, and US-Ton = Tonzi Ranch in California).…”

Section: Evaluation Of Current Wrf/cmaqmentioning

confidence: 99%

Sensitivity of the Weather Research and Forecast/Community Multiscale Air Quality modeling system to MODIS LAI, FPAR, and albedo

et al. 2015

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This study aims to improve land surface processes in a retrospective meteorology and air quality modeling system through the use of Moderate Resolution Imaging Spectroradiometer (MODIS) vegetation and albedo products for more realistic vegetation and surface representation. MODIS leaf area index (LAI), fraction of absorbed photosynthetically active radiation (FPAR), and albedo are incorporated into the Pleim-Xiu land surface model (PX LSM) used in a combined meteorology and air quality modeling system. The current PX LSM intentionally exaggerates vegetation coverage and LAI in western dry lands so that its soil moisture nudging scheme is more effective in simulating surface temperature and mixing ratio. Reduced vegetation coverage from the PX LSM with MODIS input results in hotter and dryer daytime conditions with reduced ozone dry deposition velocities in much of western North America. Evaluations of the new system indicate greater error and bias in temperature, but reduced error and bias in moisture with the MODIS vegetation input. Hotter daytime temperatures and reduced dry deposition result in greater ozone concentrations in the western arid regions even with deeper boundary layer depths. MODIS albedo has much less impact on the meteorology simulations than MODIS LAI and FPAR. The MODIS vegetation and albedo input does not have much influence in the east where differences in vegetation and albedo parameters are less extreme. Evaluation results showing increased temperature errors with more accurate representation of vegetation suggests that improvements are needed in the model surface physics, particularly the soil processes in the PX LSM.

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“…For each pixel, we checked how many possible observations were available and shortened the maximum time series length accordingly. As an example: the vegetation phase at 75˝N was shortened from 36 (365 days) to 18 (184 days) composites, because observations were only possible between Composites 9 (21-31 March) and 26 (11)(12)(13)(14)(15)(16)(17)(18)(19)(20). In this case, Composites 1-8 and 27-36 were set to LAI = 0.0.…”

Section: Adjustment Of the Time Series Lengthmentioning

confidence: 99%

Global Gap-Free MERIS LAI Time Series (2002–2012)

et al. 2016

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This article describes the principles used to generate global gap-free Leaf Area Index (LAI) time series from 2002-2012, based on MERIS (MEdium Resolution Imaging Spectrometer) full-resolution Level1B data. It is produced as a series of 10-day composites in geographic projection at 300-m spatial resolution. The processing chain comprises geometric correction, radiometric correction, pixel identification, LAI calculation with the BEAM (Basic ERS & Envisat (A)ATSR and MERIS Toolbox) MERIS vegetation processor, re-projection to a global grid and temporal aggregation selecting the measurement closest to the mean value. After the LAI pre-processing, we applied time series analysis to fill data gaps and to filter outliers using the technique of harmonic analysis (HA) in combination with mean annual and multiannual phenological data. Data gaps are caused by clouds, sensor limitations due to the solar zenith angle (<10˝), topography and intermittent data reception. We applied our technique for the whole period of observation (July 2002-March 2012. Validation, carried out with VALERI (Validation of Land European Remote Sensing Instruments) and BigFoot data, revealed a high degree (R 2 : 0.88) of agreement on a global scale.

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An Algorithm to Produce Temporally and Spatially Continuous MODIS-LAI Time Series

Cited by 204 publications

References 14 publications

Detecting change in urban areas at continental scales with MODIS data

Detecting change in urban areas at continental scales with MODIS data

Sensitivity of the Weather Research and Forecast/Community Multiscale Air Quality modeling system to MODIS LAI, FPAR, and albedo

Global Gap-Free MERIS LAI Time Series (2002–2012)

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