Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Vischel, Théo; Pegram, Geoff; Sinclair, Scott; Wagner, W.; Bartsch, Annett

doi:10.5194/hess-12-751-2008

Cited by 38 publications

(25 citation statements)

References 39 publications

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“…For example, Kite and Droogers (2000) compared several hydrological models and satellite-based ET estimation methods, as well as field measurements. Spatial soil moisture fields derived from hydrological models have been compared to estimates derived from passive microwave observations (Liu et al, 2010) and radar (Vischel et al, 2008;Parajka et al, 2009). Biftu and Gan (2001) used AVHRR and Landsat LST and radar soil moisture to evaluate model results.…”

Section: Model Evaluation and Improvementmentioning

confidence: 99%

Water resource monitoring systems and the role of satellite observations

Dijk

Renzullo

2011

Hydrol. Earth Syst. Sci.

155

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Abstract.Spatial water resource monitoring systems (SWRMS) can provide valuable information in support of water management, but current operational systems are few and provide only a subset of the information required. Necessary innovations include the explicit description of water redistribution and water use from river and groundwater systems, achieving greater spatial detail (particularly in key features such as irrigated areas and wetlands), and improving accuracy as assessed against hydrometric observations, as well as assimilating those observations. The Australian water resources assessment (AWRA) system aims to achieve this by coupling landscape models with models describing surface water and groundwater dynamics and water use. A review of operational and research applications demonstrates that satellite observations can improve accuracy and spatial detail in hydrological model estimation. All operational systems use dynamic forcing, land cover classifications and a priori parameterisation of vegetation dynamics that are partially or wholly derived from remote sensing. Satellite observations are used to varying degrees in model evaluation and data assimilation. The utility of satellite observations through data assimilation can vary as a function of dominant hydrological processes. Opportunities for improvement are identified, including the development of more accurate and higher spatial and temporal resolution precipitation products, and the use of a greater range of remote sensing products in a priori model parameter estimation, model evaluation and data assimilation. Operational challenges include the continuity of research satellite missions and data services, and the need to find computationally-efficient data assimilation techniques. The successful use of observations critically deCorrespondence to: A. I. J. M. van Dijk (albert.vandijk@csiro.au) pends on the availability of detailed information on observational error and understanding of the relationship between remotely-sensed and model variables, as affected by conceptual discrepancies and spatial and temporal scaling.

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Section: Model Evaluation and Improvementmentioning

confidence: 99%

Water resource monitoring systems and the role of satellite observations

Dijk

Renzullo

2011

Hydrol. Earth Syst. Sci.

155

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“…A number of previous studies (Vischel et al, 2008;Brocca et al, 2010;Albergel et al, 2009;Gruhier et al, 2010;Rüdiger et al, 2009;Draper et al, 2009;Wagner et al, 2007) evaluated these passive and active microwave soil moisture products against in situ measurements and found that VUA-NASA passive microwave product performs better over sparsely vegetated regions, whereas the TU-Wien active microwave product shows better agreement for regions of moderate vegetation density. Over the sparsely to moderately vegetated regions, both products have similar performances.…”

Section: Introductionmentioning

confidence: 99%

Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

Liu

Parinussa

Dorigo

et al. 2011

Hydrol. Earth Syst. Sci.

Self Cite

616

439

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Abstract. Combining information derived from satellitebased passive and active microwave sensors has the potential to offer improved estimates of surface soil moisture at global scale. We develop and evaluate a methodology that takes advantage of the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates to produce an improved soil moisture product. First, volumetric soil water content (m 3 m −3 ) from AMSR-E and degree of saturation (%) from ASCAT are rescaled against a reference land surface model data set using a cumulative distribution function matching approach. While this imposes any bias of the reference on the rescaled satellite products, it adjusts them to the same range and preserves the dynamics of original satellite-based products. Comparison with in situ measurements demonstrates that where the correlation coefficient between rescaled AMSR-E and ASCAT is greater than 0.65 ("transitional regions"), merging the different satellite products increases the number of observations while minimally changing the accuracy of soil moisture retrievals. These transitional regions also delineate the boundary between sparsely and moderately vegetated regions where rescaled AMSR-E and ASCAT, respectively, are used for the merged product. Therefore the merged product carries the advantages of better spatial coverage overall and increased number of observations, particularly for the transitional regions. The combination method developed has the potential to be applied Correspondence to: Y. Y. Liu (yi.y.liu@csiro.au) to existing microwave satellites as well as to new missions. Accordingly, a long-term global soil moisture dataset can be developed and extended, enhancing basic understanding of the role of soil moisture in the water, energy and carbon cycles.

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“…This is particularly apparent when comparing models developed to represent the same process at different scales and for which different qualities of input variables, parameterization/initialization, and data for evaluation will be available, for example, soil water balances at plot, farm, catchment, and region (e.g., Keating et al 2002;Vischel et al 2007). This has led to the development of application-specific testing of models and the idea of model benchmarking, by comparing simulation outputs from different models, where outputs from one simulation can also be accepted as a "standard" (based on previous evaluations, e.g., Vanclay 1994).…”

Section: Dependence On the Contextmentioning

confidence: 99%

Deliberative processes for comprehensive evaluation of agroecological models. A review

Bellocchi

Rivington

Matthews

et al. 2014

Agron. Sustain. Dev.

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The use of biophysical models in agroecology has increased in the last few decades for two main reasons: the need to formalize empirical knowledge and the need to disseminate model-based decision support for decision makers (such as farmers, advisors, and policy makers). The first has encouraged the development and use of mathematical models to enhance the efficiency of field research through extrapolation beyond the limits of site, season, and management. The second reflects the increasing need (by scientists, managers, and the public) for simulation experimentation to explore options and consequences, for example, future resource use efficiency (i.e., management in sustainable intensification), impacts of and adaptation to climate change, understanding market and policy responses to shocks initiated at a biophysical level under increasing demand, and limited supply capacity. Production concerns thus dominate most model applications, but there is a notable growing emphasis on environmental, economic, and policy dimensions. Identifying effective methods of assessing model quality and performance has become a challenging but vital imperative, considering the variety of factors influencing model outputs. Understanding the requirements of stakeholders, in respect of model use, logically implies the need for their inclusion in model evaluation methods. We reviewed the use of metrics of model evaluation, with a particular emphasis on the involvement of stakeholders to expand horizons beyond conventional structured, numeric analyses. Two major topics are discussed: (1) the importance of deliberative processes for model evaluation, and (2) the role computer-aided techniques may play to integrate deliberative processes into the evaluation of agroecological models. We point out that (i) the evaluation of agroecological models can be improved through stakeholder follow-up, which is a key for the acceptability of model realizations in practice, (ii) model credibility depends not only on the outcomes of well-structured, numerically based evaluation, but also on less tangible factors that may need to be addressed using complementary deliberative processes, (iii) comprehensive evaluation of simulation models can be achieved by integrating the expectations of stakeholders via a weighting system of preferences and perception, (iv) questionnaire-based surveys can help understand the challenges posed by the deliberative process, and (v) a benefit can be obtained if model evaluation is conceived in a decisional perspective and evaluation techniques are developed at the same pace with which the models themselves are created and improved. Scientific knowledge hubs are also recognized as critical pillars to advance good modeling practice in relation to model evaluation (including access to dedicated software tools), an activity which is frequently neglected in the context of time-limited framework programs.

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Comparison of soil moisture fields estimated by catchment modelling and remote sensing: a case study in South Africa

Cited by 38 publications

References 39 publications

Water resource monitoring systems and the role of satellite observations

Water resource monitoring systems and the role of satellite observations

Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

Deliberative processes for comprehensive evaluation of agroecological models. A review

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