Multi-scale soil moisture measurements at the Gourma meso-scale site in Mali

Rosnay, Patricia de; Gruhier, Claire; Timouk, Franck; Baup, Frédéric; Mougin, E.; Hiernaux, Pierre; Kergoat, Laurent; Ledantec, V.

doi:10.1016/j.jhydrol.2009.01.015

Cited by 110 publications

(62 citation statements)

References 35 publications

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“…Data on soil moisture at different levels and different places (top, middle and bottom locations of dune slope), and soil temperatures at different levels are also available, except for year 2004. A detailed description of the soil moisture network and methodology and of the meteorological station is given in De Rosnay et al (2009) and Mougin et al (2009). Meteorological and vegetation data are available in the AMMA database: http://bd.amma-catch.org/ amma-catch2/main.jsf (Redelsperger et al, 2006).…”

Section: Meteorological and Vegetation Datamentioning

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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Section: Meteorological and Vegetation Datamentioning

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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“…Qin et al [9,14] found that a Bayesian linear regression-based model performs better than the ordinary least square linear regression-based or the block kriging-based models when upscaling in situ soil moisture data with MODIS-based apparent thermal inertia (ATI) data. Based on the strategy of temporal stability and the high frequency of in situ soil moisture observations [15], stations with temporally continuous measurements have been selected to build a linear regression model based on in situ soil moisture data. However, the complexity and nonlinearity of the relationships makes it impossible to obtain large-scale estimates using traditional statistical regression methods.…”

Section: Introductionmentioning

confidence: 99%

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Zhang

Huang

et al. 2017

IJGI

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Abstract:In current upscaling of in situ surface soil moisture practices, commonly used novel statistical or machine learning-based regression models combined with remote sensing data show some advantages in accurately capturing the satellite footprint scale of specific local or regional surface soil moisture. However, the performance of most models is largely determined by the size of the training data and the limited generalization ability to accomplish correlation extraction in regression models, which are unsuitable for larger scale practices. In this paper, a deep learning model was proposed to estimate soil moisture on a national scale. The deep learning model has the advantage of representing nonlinearities and modeling complex relationships from large-scale data. To illustrate the deep learning model for soil moisture estimation, the croplands of China were selected as the study area, and four years of Visible Infrared Imaging Radiometer Suite (VIIRS) raw data records (RDR) were used as input parameters, then the models were trained and soil moisture estimates were obtained. Results demonstrate that the estimated models captured the complex relationship between the remote sensing variables and in situ surface soil moisture with an adjusted coefficient of determination of R 2 = 0.9875 and a root mean square error (RMSE) of 0.0084 in China. These results were more accurate than the Soil Moisture Active Passive (SMAP) active radar soil moisture products and the Global Land data assimilation system (GLDAS) 0-10 cm depth soil moisture data. Our study suggests that deep learning model have potential for operational applications of upscaling in situ surface soil moisture data at the national scale.

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“…The concept of temporal stability of spatial patterns of SWC was first introduced by Vachaud et al (1985), who defined it as "the time-invariant association between spatial location and classical statistical parameters of a given soil property". The temporal stability of SWC has been investigated for different soil depths (Zhang and Shao, 2013;Heathman, 2009), land uses (Hu et al, 2010a;Williams et al, 2009;Lin, 2006), scales (Martínez-Fernández and Ceballos, 2003;Jia and Shao, 2013;Hu et al, 2010b;Gao et al, 2011), regions (Biswas and Si, 2011b;Jia et al, 2013a;Zhang and Shao, 2013), measurement periods (Guber et al, 2008;de Rosnay, et al, 2009;Zhao, et al, 2010;Biswas and Si, 2011b;Liu and Shao, 2014), and measuring instruments (Jacobs et al, 2004;Gao and Shao, 2012b;Wang et al, 2013;Penna et al, 2013). The characteristics of the temporal stability of SWC vary with depth, but little is known about the precise changes of SWC and its temporal stability in soil profiles due to coarse division of previous soil profiling.…”

Section: Introductionmentioning

confidence: 99%

Profile distribution of soil–water content and its temporal stability along a 1340-m long transect on the Loess Plateau, China

Shao

Jia

et al. 2016

CATENA

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Information on the profile characteristics of soil-water content (SWC) and its temporal stability is essential for an accurate understanding of hydrological processes. This study investigated changes of spatial variation and temporal stability of SWC in soil profiles and estimated mean SWC based on direct and indirect methods. SWCs were measured at 20-cm intervals in the soil profiles to a depth of 3 m using neutron probes at 135 locations along a 1340-m long transect on 18 sampling dates from 2012 to 2013 on the Loess Plateau in China. The coefficient of variation over space (CV S ) of SWC first decreased and then increased vertically. The coefficient of variation over time (CV T ) of SWC decreased along the soil profiles. The spatial patterns of SWC strongly persisted vertically and temporally. Mean Spearman's rank correlation coefficients decreased from a depth of 10 to a depth of 20 cm, fluctuated from 20 to 180 cm, and then increased below 200 cm. Temporal stability increased along the soil profiles based on the standard deviation of mean relative difference (SDRD) and the mean absolute bias error (MABE). The number of locations with an SDRD and/or MABE b 5% increased along the soil profile, and the number of locations with a mean relative difference within ±5% and/or representative locations were variably dependent on depth. Both direct and indirect methods could accurately estimate the mean SWC for each depth based on the mean absolute relative errors and root mean square errors. The driest and wettest locations tended to remain representative for more depths than did locations with mean-moistures. The driest locations were more likely to be the most temporally stable. These findings should improve our understanding of soil-water dynamics in soil profiles.

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Multi-scale soil moisture measurements at the Gourma meso-scale site in Mali

Cited by 110 publications

References 35 publications

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

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

Upscaling of Surface Soil Moisture Using a Deep Learning Model with VIIRS RDR

Profile distribution of soil–water content and its temporal stability along a 1340-m long transect on the Loess Plateau, China

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