Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

Yuan, Hua; Dai, Yongjiu; Xiao, Zhiqiang; Ji, Duoying; Shangguan, Wei

doi:10.1016/j.rse.2011.01.001

Cited by 360 publications

(284 citation statements)

References 53 publications

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“…This has also been reported in other works where the SG adaptive filter has been successfully used in minimizing noise in NDVI time-series [33] and [12] and Leaf area index time-series [11]. …”

Section: Analysis Of MVI Time-series Filteringsupporting

confidence: 78%

“…Local fitting methods are based on surrounding value of data in a time-series determined by median smoothing approaches [13] or by Savitzky-Golay filter approach [14] [15]. Global approaches fit the data to long time scale of observations such as Fourier transform used in frequency analysis of a signal (for an extended review of filtering methods, refer to [11]). The elimination of noise by filtering process allow san estimation of vegetation seasonal patterns such as the length of the season and the seasonal amplitude.…”

Section: Introductionmentioning

confidence: 99%

“…This tool was used in various time-series filtering (e.g. [11] [12] [19]). Our main objective was to compare different smoothing algorithms The specific objectives of the present work are: 1) to make a time-series filtering of various moisture vegetation indexes (MVI) derived from the BRDF product MOD09A1 using the TIMESAT tool; 2) to evaluate the most performing filtering functions to make necessary corrections of the imperfections of the used data; 3) to determine the vegetation seasonality parameters for the years of the time-series of the study; 4) to investigate the relationship between seasonal parameters derived from filtered MVI time-series and the drought meteorological index SPI within the study ecosystem.…”

Section: Introductionmentioning

confidence: 99%

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Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Chakroun¹

2017

OJAppS

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Drought Monitoring by remotely sensed moisture vegetation indexes is being an active research subject as the vegetation spectral responses are showed to be highly correlated to water content. The MODIS (MODerate resolution Imaging Spectro-radiometer) sensor of the Terra satellite provides MOD09A1 product of BRDF (Bidirectional Reflectance Distribution Function) used in computing moisture vegetation indexes (MVI). The exploration of an MVI time-series in the Kroumirie forest in Northern Tunisia showed important noise due to both clouds contamination and sensor defaults that had to be removed. Amongst methods for removing these imperfections, TIMESAT tool was designed for correcting time-series of satellite data and also to retrieve seasonal parameters from smoothed vegetation indexes. The methodology of smoothing functions to fit the time series data is based on two stages. First, a least square fit to the upper envelope of the vegetation indices series is applied. The second stage is achieved by local and adaptive fitting functions. The corrections have been made by spikes removal due to abrupt change of MVI variations and by fitting the MVI time-series to the upper envelop to correct the negative biases of remote sensing vegetation indexes. The adaptive SavitskyGolay function filter compared to local filtering process produces variations that conserve local variations for all the tested MVI. Seasonal vegetation parameters were extracted for each year of the time-series analysis and compared to the Standardized Precipitation Index (SPI) calculated at meteorological station level and for different time scales. Positive relations were found between SPI and the seasonal parameters expressed by the length and the amplitude of the season, indicating MODIS derived MVI sensitivity to water deficit or surplus conditions. The 6-month SPI showed the best performance when related to water sensitive indexes suggesting that MODIS derived indexes are more correlated to the precipitation variations over seasons.

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Section: Analysis Of MVI Time-series Filteringsupporting

confidence: 78%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Chakroun¹

2017

OJAppS

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“…In addition, forest LAI is the key factor influencing carbon sequestration [22], and it directly affects regional or global climate conditions through regulating the partition of sensible and latent heat fluxes [23][24][25]. Therefore, reliable forest LAI data is a prerequisite to improving the reliability of ecological and land surface process models, and reducing uncertainties in carbon source and sink estimates [26][27][28][29].…”

mentioning

confidence: 99%

Spatial and temporal variations of forest LAI in China during 2000–2010

Liu

Chen

et al. 2012

Chin. Sci. Bull.

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Forests are crucial terrestrial ecosystems. Their leaf area index (LAI) is a key parameter determining the exchange of matter and energy between the atmosphere and the ground surface. In this study, MOD 09A1 and MCD 43A1 data were input into an inversion model based on the 4-scale geometric optical model to retrieve 8-d 500 m LAI products in China during the period 2000 to 2010. The resulting LAI product was validated using LAI measured in 6 typical areas. The spatial and temporal variations of forest LAI and its relationships with temperature and precipitation were analyzed. The results show that the accuracy of the 500 m LAI product was above 70% in the 6 typical areas, indicating the reliability of this product.

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“…For IPCC AR5 global climate simulations, the MODIS LAI product is used in CABLE within the ACCESS global circulation model. Since the aim of this paper is to provide better information on the sensitivity of CABLE to LAI, we use the same MODIS LAI product (Yuan et al 2011) for our control simulation . This is carried out by prescribing monthly mean climatological LAI at each grid cell, based on monthly averages over the period of availability of the MODIS LAI data .…”

Section: Simulationsmentioning

confidence: 99%

Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes

Kala

Decker

Exbrayat

et al. 2014

Journal of Hydrometeorology

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Leaf area index (LAI), the total one-sided surface area of leaf per ground surface area, is a key component of land surface models. The authors investigate the influence of differing, plausible LAI prescriptions on heat, moisture, and carbon fluxes simulated by the Community Atmosphere Biosphere Land Exchange version 1.4b (CABLEv1.4b) model over the Australian continent. A 15-member ensemble monthly LAI dataset is generated using the Moderate Resolution Imaging Spectroradiometer (MODIS) LAI product and gridded observations of temperature and precipitation. Offline simulations lasting 29 years are carried out at 25-km resolution with the composite monthly means from the MODIS LAI product (control simulation) and compared with simulations using each of the 15-member ensemble monthly varying LAI datasets generated. The imposed changes in LAI did not strongly influence the sensible and latent fluxes, but the carbon fluxes were more strongly affected. Croplands showed the largest sensitivity in gross primary production with differences ranging from 290% to 60%. Plant function types (PFTs) with high absolute LAI and low interannual variability, such as evergreen broadleaf trees, showed the least response to the different LAI prescriptions, while those with lower absolute LAI and higher interannual variability, such as croplands, were more sensitive. The authors show that reliance on a single LAI prescription may not accurately reflect the uncertainty in the simulation of terrestrial carbon fluxes, especially for PFTs with high interannual variability. The study highlights that accurate representation of LAI in land surface models is key to the simulation of the terrestrial carbon cycle. Hence, this will become critical in quantifying the uncertainty in future changes in primary production.

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Reprocessing the MODIS Leaf Area Index products for land surface and climate modelling

Cited by 360 publications

References 53 publications

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Quality Assessment of MODIS Time Series Images and the Effect on Drought Monitoring

Spatial and temporal variations of forest LAI in China during 2000–2010

Influence of Leaf Area Index Prescriptions on Simulations of Heat, Moisture, and Carbon Fluxes

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