Comparison of ERS wind-scatterometer and SSM/I data for Sahelian vegetation monitoring

Frison, Patrice; Mougin, E.; Jarlan, Lionel; Karam, M.A.; Hiernaux, Pierre

doi:10.1109/36.851763

Cited by 13 publications

(9 citation statements)

References 25 publications

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“…However, for heterogeneous agro-pastoral sites with low herbaceous cover, 45 • ERS-WSC data were found to be dominated by bare soil contribution (Zine et al 2005). The use of special sensor microwave radiometer/imager (SSM/I) data has also been tested, but poor results were obtained compared to ERS-WSC data, mainly due to strong atmospheric effects (Frison et al 2000). Moreover, SSM/I data were mainly linked to soil parameters, while WSC response was directly sensitive to the biomass (Frison et al 2000).…”

Section: Biomass Derivation For Semi-arid Areas Based On Radar Datamentioning

confidence: 95%

“…The use of special sensor microwave radiometer/imager (SSM/I) data has also been tested, but poor results were obtained compared to ERS-WSC data, mainly due to strong atmospheric effects (Frison et al 2000). Moreover, SSM/I data were mainly linked to soil parameters, while WSC response was directly sensitive to the biomass (Frison et al 2000). A systematic exploration of the relationship between ERS-WSC data, herbaceous AGB and soil moisture by Jarlan et al (2002) revealed that a wide range of admissible parameters exists and that additional data are necessary to narrow the parameter domain.…”

Section: Biomass Derivation For Semi-arid Areas Based On Radar Datamentioning

confidence: 98%

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Derivation of biomass information for semi-arid areas using remote-sensing data

Eisfelder

Kuenzer

Dech

2011

International Journal of Remote Sensing

191

129

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Section: Biomass Derivation For Semi-arid Areas Based On Radar Datamentioning

confidence: 95%

Section: Biomass Derivation For Semi-arid Areas Based On Radar Datamentioning

confidence: 98%

Derivation of biomass information for semi-arid areas using remote-sensing data

Eisfelder

Kuenzer

Dech

2011

International Journal of Remote Sensing

191

129

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“…In this study, the model will be used at the local scale. In previous studies, STEP has been coupled to radiative transfer models in the optical and active/passive microwave domain (Frison et al, 1998(Frison et al, , 2000, allowing an indirect comparison of satellite observations and modelling results of the vegetation growth (e.g. .…”

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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“…However, another important class of vegetation cover is grassland (less than 5% tree cover over the total surface of the study site) that has not received the attention it deserves with regards to its role in carbon cycle. Most of recent radar studies dealing with short vegetation pertain to cultural grassland (Touré et al 1994, Picard et al 2003, Taconet et al 1994, Bouman 1991, Ferrazzoli 1992 and only a handful in open literature have focused on natural grassland (Frison et al 1998, Jarlan et al 2002, Frison et al 2000, Saatchi et al 1994, Bakhtiari and Zoughi 1991. Dry and sub-humid lands are found throughout the world with total coverage of approximately 47% of the world's terrestrial area of which 74% is covered by grassland and savannah (White and Nackoney 2002).…”

Section: Introductionmentioning

confidence: 99%

“…Different models have been proposed employing either analytical or semi-empirical modelling to predict and understand the grassland dynamic (Touré et al 1994, Picard et al 2003, Ferrazzoli 1992, Stiles et al 2000bis, Jarlan et al 2002, Frison et al 2000. The structure of grass vegetation leads to many interesting phenomena that make the application of random media scattering techniques problematic.…”

Section: Introductionmentioning

confidence: 99%

Microwave electromagnetic modelling of Sahelian grassland

Monsiváis-Huertero

Chênerie

Sarabandi

et al. 2010

International Journal of Remote Sensing

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In this paper radar scattering models based on coherent and incoherent formulations for an African grassland (Sahelian) are examined. The coherent model is used to account for the structure of the grass plants and the results are compared with the same model assuming random placement and orientation of scatters, and the Radiative Transfer model. The validity of the three models applied to grass vegetation is determined by comparing the model predictions with ENVISAT ASAR data gathered in 2005 over Sahelian grassland. The Agoufou site, as defined in AMMA project, is selected as the test target and a set of ground data were collected during 2004 and 2005.Through a comprehensive data comparison, it is shown that the coherent scattering model with a generator considering botanical information is the best model to predict the backscattering data that matches ENVISAT measurements well (correlation = 0.92). At low incidence angles (<30°), the radar backscatter shows a strong dependence to soil moisture variations. The analysis of the different contributions leads to study the main scattering mechanisms. For high incidence angles, backscattering coefficient at HH polarization shows a marked seasonal variation associated to grass presence.

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Comparison of ERS wind-scatterometer and SSM/I data for Sahelian vegetation monitoring

Cited by 13 publications

References 25 publications

Derivation of biomass information for semi-arid areas using remote-sensing data

Derivation of biomass information for semi-arid areas using remote-sensing data

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

Microwave electromagnetic modelling of Sahelian grassland

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