Study of multifrequency sensitivity to soil moisture variations in the lower Bermejo basin

Vittucci, Cristina; Guerriero, L.; Ferrazzoli, P.; Rahmoune, R.; Barraza, Verónica Guadalupe Jiménez; Grings, F.

doi:10.5721/eujrs20134646

Cited by 10 publications

(9 citation statements)

References 21 publications

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“…Surface soil moisture (SM) has a clear impact on the energy partition and water on the land surface and also acts as a critical control on the interaction between land and atmosphere, biogeochemical cycles, and hydrology (Brocca et al, 2016;Paloscia et al, 2012). Therefore, surface SM is essential in many studies, such as flood forecasting, climate simulation, and land surface modeling (Brocca et al, 2017;Vittucci et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

“…Passive remote sensing is more sensitive to surface SM content and therefore the most widely used way to map global or regional SM (Jackson et al, 1999). Over the past few decades, passive remote sensing at different frequencies including the X, C, and L bands has been widely used to evaluate soil moisture (Kolassa et al, 2016;Schmugge et al, 1974;Vittucci et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

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Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

Mousa

Hong

2020

Earth and Space Science

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The limited number of in situ stations of surface soil moisture (SM) in Africa creates a shortage in the validation of SM satellite products. Therefore, this study investigates the performance of Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and the H113 product from the Advanced Scatterometer (ASCAT) on the regional scale over Africa through these goals: (1) validate of satellite SM products against in situ stations and SM data from the ERA-Interim atmospheric reanalysis product, (2) study the spatiotemporal variability of satellite SM products on the regional scale, and (3) evaluate the regional scale error patterns and investigate regions where the assimilation of satellites SM data may add improvement to ERA-Interim. Standard statistical metrics, hovmöller diagrams, and the Triple Collocation (TC) model were used to achieve these goals. Land cover data, Normalized Difference Vegetation Index, and precipitation data were used to interpret results. The validation results based on statistical metrics and TC indicate that over the desert and shrub, passive products showed better performance than ASCAT, while over moderate vegetation areas (grassland), SMAP had the best among SM products. Over high densely vegetated regions, ASCAT showed a high comparatively performance than passive products. The potential regions for assimilation of satellite data sets were selected to be over savannas and grassland regions for ASCAT, and over shrub and grassland regions for SMAP. In particular, SMAP and ASCAT SM data sets are considered more stable than SMOS for data assimilation and capturing the spatial distribution of SM on the regional scale over Africa.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

Mousa

Hong

2020

Earth and Space Science

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“…The selection of this area allowed studying the effects of soil moisture variation during flooding events, which may extend far away the watershed. Although soil moisture measurements were not available for the site under study, in [36], a clear correlation between the rain occurrence and the soil moisture content provided by ECMWF over the same area has been observed. For this reason, in Fig.…”

Section: Hydrometric Datamentioning

confidence: 69%

River Water Level Prediction Using Passive Microwave Signatures—A Case Study: The Bermejo Basin

Vittucci

Guerriero

Ferrazzoli

et al. 2014

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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The aim of this work is to investigate the exploitation of radiometric acquisitions from satellite sensors at different microwave frequencies in view of the prediction of river water level. A case study has been identified in the Bermejo basin, in northern Argentina. This river is seasonally affected by severe flooding events in the lower part, mostly due to rains occurring in the upper basin, that produce sediment loadings flushing down along the lower basin thus changing the watercourse. While the effectiveness of microwave radiometry at Ka band for flood monitoring is consolidated in the literature, this study also considers X and C bands (provided by the Advanced Microwave Scanning Radiometer (AMSR) series together with the higher frequency) and highlights the better sensitivity to soil conditions of L band data (made recently available, thanks to SMOS) over moderately and densely vegetated areas. This study confirms, first, the well-known capability of passive microwave remote sensing instruments to record brightness temperature variations due to rainfall and floods occurred near river edges under different seasonal conditions. For this purpose, a multifrequency comparative analysis is conducted. Second, it investigates whether these properties can be exploited for flood forecasting: a model which directly links the daily satellite measurements to the river water level has been tested, considering 1-to 7-day forecast horizons. The results show that forecasting models can take advantage of the sensitivity of low frequencies to soil moisture conditions in order to predict flood peaks, despite the instrument's low resolutions.

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“…• The range of the parameter s 2 is constrained by ensuring that the resulting range of N remains physically plausible. Reported estimates of vertically polarized directional emissivity (θ) at similar frequencies [32][33][34] (which can be related to the directional hemispherical reflectivity via Kirchhoffs law [35] (R(θ) = 1 − (θ))) suggest a range of 0 N 0.1. Based on the reference-dataset, the range for SM is set to SM 0.45, and therefore the boundaries for s 2 within the fit-procedure were set to 0.1 ≤ s 2 ≤ 0.3.…”

Section: Connection To Biophysical Variablesmentioning

confidence: 99%

A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations

et al. 2019

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We present the application of a generic, semi-empirical first-order radiative transfer modelling approach for the retrieval of soil- and vegetation related parameters from coarse-resolution space-borne scatterometer measurements ( σ 0 ). It is shown that both angular- and temporal variabilities of ASCAT σ 0 measurements can be sufficiently represented by modelling the scattering characteristics of the soil-surface and the covering vegetation-layer via linear combinations of idealized distribution-functions. The temporal variations are modelled using only two dynamic variables, the vegetation optical depth ( τ ) and the nadir hemispherical reflectance (N) of the chosen soil-bidirectional reflectance distribution function ( B R D F ). The remaining spatial variabilities of the soil- and vegetation composition are accounted for via temporally constant parameters. The model was applied to series of 158 selected test-sites within France. Parameter estimates are obtained by using ASCAT σ 0 measurements together with auxiliary Leaf Area Index ( L A I ) and soil-moisture ( S M ) datasets provided by the Interactions between Soil, Biosphere, and Atmosphere (ISBA) land-surface model within the SURFEX modelling platform for a time-period from 2007–2009. The resulting parametrization was then used used to perform S M and τ retrievals both with and without the incorporation of auxiliary L A I and S M datasets for a subsequent time-period from 2010 to 2012.

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Study of multifrequency sensitivity to soil moisture variations in the lower Bermejo basin

Cited by 10 publications

References 21 publications

Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

Spatial Evaluation and Assimilation of SMAP, SMOS, and ASCAT Satellite Soil Moisture Products Over Africa Using Statistical Techniques

River Water Level Prediction Using Passive Microwave Signatures—A Case Study: The Bermejo Basin

A Generic First-Order Radiative Transfer Modelling Approach for the Inversion of Soil and Vegetation Parameters from Scatterometer Observations

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