Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model

Jarlan, Lionel; Mangiarotti, Sylvain; Mougin, E.; Mazzéga, Pierre; Hiernaux, Pierre; Dantec, Valérie Le

doi:10.1016/j.rse.2007.02.041

Cited by 77 publications

(61 citation statements)

References 26 publications

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“…In this study, the SPOT-VGT products (Tarnavsky et al 2008;Jarlan et al 2008;Zhong et al 2010) are used for NDVI analysis. A 10-day composite with 1-km spatial resolution is obtained from the website (http://free.vgt.vito.be), available from April 1998 to October 2010.…”

Section: Methodsmentioning

confidence: 99%

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Jiang

Zhang

et al. 2013

Theor Appl Climatol

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Based on the SPOT/VEGETATION Normalized Difference Vegetation Index (NDVI) data and daily precipitation data of 357 meteorological stations, the spatial and temporal variability of vegetation cover, measured by NDVI, and precipitation as well as their relationships are investigated in Eastern China, which is portioned into three subregions (regions I, II, and III), for the period 1998-2010. The results show that high NDVI values appear mainly in Northeastern China and in August while high precipitation (PRETOT) occurs in Southeastern China and in July (June for Southern China). Extreme precipitation days (RD95p) and amount (EPRETOT) coincide well with PRETOT. Extreme precipitation intensity (RINTEN) has a similar spatial variability to PRETOT but with a smaller seasonal variation than PRETOT. Growing season NDVI is positively correlated with PRETOT in 11.7 % of the study area (mostly in arid to subhumid regions of Northern China), where precipitation is a limiting factor for vegetation growth. In contrast, a negative correlation between growing season NDVI and PRETOT is found in 4.8 % of the study area, mostly in areas around the Yangtze River and deep Northeastern China. No significant correlations between these two variables are found for the other regions because vegetation response to precipitation is affected by other factors such as temperature, radiation, and human disturbance. On a monthly scale, there is a positive correlation between NDVI and PRETOT in May (for region II) and September (all subregions except region I). NDVI variations lag 1 month behind PRETOT in June (for region I) and October. Correlations between NDVI and RD95p, EPRETOT are similar to that with PRETOT, but the relationships between NDVI and RINTEN are relatively weaker than with PRETOT. This study provides the technical basis for agriculture development and ecological construction in Eastern China.

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Section: Methodsmentioning

confidence: 99%

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Jiang

Zhang

et al. 2013

Theor Appl Climatol

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“…The performance of the STEP process model in predicting herbage mass variation over time and herbage yield along a north-south bio-climatic gradient within the Sahel was tested along a 15-year period, and gave high correlation coefficients between model and measurements when the model was calibrated for each site (Tracol et al, 2006). Modifications brought to the first version of the model have been given in Jarlan et al (2008). The regional-scale use of the model is illustrated in Jarlan et al (2003) and Pierre et al (2012).…”

Section: Stepmentioning

confidence: 99%

“…The quantity of nitrogen in the soil is derived from the quantity of carbon using the C / N ratio. A more detailed description of STEP can be found in Mougin et al (1995), Tracol et al (2006) and Jarlan et al (2008). Information such as the quantity of faecal and herbal masses are transferred as inputs to GENDEC, the litter decomposition model.…”

Section: Stepmentioning

confidence: 99%

Modelling the effect of soil moisture and organic matter degradation on biogenic NO emissions from soils in Sahel rangeland (Mali)

et al. 2015

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Abstract. This work is an attempt to provide seasonal variation of biogenic NO emission fluxes in a Sahelian rangeland in Mali (Agoufou, 15.34 • N, 1.48 • W) for years 2004, 2005, 2006, 2007 and 2008. Indeed, NO is one of the most important precursors for tropospheric ozone, and previous studies have shown that arid areas potentially display significant NO emissions (due to both biotic and abiotic processes). Previous campaigns in the Sahel suggest that the contribution of this region in emitting NO is no longer considered as negligible. However, very few data are available in this region, therefore this study focuses on model development. The link between NO production in the soil and NO release to the atmosphere is investigated in this modelling study, by taking into account vegetation litter production and degradation, microbial processes in the soil, emission fluxes, and environmental variables influencing these processes, using a coupled vegetation-litter decompositionemission model. This model includes the Sahelian Transpiration Evaporation and Productivity (STEP) model for the simulation of herbaceous, tree leaf and faecal masses, the GENDEC model (GENeral DEComposition) for the simulation of the buried litter decomposition and microbial dynamics, and the NO emission model (NOFlux) for the simulation of the NO release to the atmosphere. Physical parameters (soil moisture and temperature, wind speed, sand percentage) which affect substrate diffusion and oxygen supply in the soil and influence the microbial activity, and biogeochemical parameters (pH and fertilization rate related to N content) are necessary to simulate the NO flux. The reliability of the simulated parameters is checked, in order to assess the robustness of the simulated NO flux. Simulated yearly average of NO flux ranges from 2.09 to 3.04 ng(N) m −2 s −1 (0.66 to 0.96 kg(N) ha −1 yr −1 ), and wet season average ranges from 3.36 to 5.48 ng(N) m −2 s −1 (1.06 to 1.73 kg(N) ha −1 yr −1 ). These results are of the same order as previous measurements made in several sites where the vegetation and the soil are comparable to the ones in Agoufou. This coupled vegetationlitter decomposition-emission model could be generalized at the scale of the Sahel region, and provide information where few data are available.

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“…vgt.vito.be/). VGT-S10 NDVI were synthesized from S1 (1-day resolution) NDVI products using a maximum value composite (MVC) algorithm (Jarlan et al 2008). The influence of cloud cover was well reduced because the highest signal data within the 10-day observation was used as a pixel value for the 10-day synthesis data.…”

Section: Dependent Variable: Ndvimentioning

confidence: 99%

Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression

Zhao

Gao

Wang

et al. 2014

Theor Appl Climatol

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At landscape scale, the normalized difference vegetation index (NDVI) can be used to indicate the vegetation's dynamic characteristics and has been widely employed to develop correlated and dependent relationships with the climatic and environmental factors. However, studies show that NDVI-environment relationships always emerge with complex features such as nonlinearity, scale dependency, and nonstationarity, especially in highly heterogeneous areas. In this study, we used geographically weighted regression (GWR), a local modeling technique to estimate regression models with spatially varying relationships, to investigate the spatially nonstationary relationships between NDVI and climatic factors at multiple scales in North China. The results indicate that all GWR models with appropriate bandwidth represented significant improvements of model performance over the ordinary least squares (OLS) models. The spatial relationships between NDVI and climatic factors varied significantly over space and were more significant and sensitive in the ecogeographical transition zone. Clear spatial patterns of slope parameters and local coefficient of determination (R 2 ) were found from the results of the GWR models. Moreover, the spatial patterns of the local R 2 of NDVI-precipitation are much clearer than the R 2 of NDVI-temperature in the semiarid and subhumid areas, which mean that precipitation has more significant influence on vegetation in these areas. In conclusion, the study revealed detailed site information on the variable relationships in different parts of the study area, especially in the ecogeographical transition zone, and the GWR model can improve model ability to address spatial, nonstationary, and scale-dependent problems in landscape ecology.

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Assimilation of SPOT/VEGETATION NDVI data into a sahelian vegetation dynamics model

Cited by 77 publications

References 26 publications

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Interannual variability and correlation of vegetation cover and precipitation in Eastern China

Modelling the effect of soil moisture and organic matter degradation on biogenic NO emissions from soils in Sahel rangeland (Mali)

Exploring spatially variable relationships between NDVI and climatic factors in a transition zone using geographically weighted regression

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