A First Assessment of the SMOS Soil Moisture Product With In Situ and Modeled Data in Italy and Luxembourg

Lacava, Teodosio; Matgen, Patrick; Brocca, Luca; Bittelli, Marco; Pergola, Nicola; Moramarco, Tommaso; Tramutoli, Valerio

doi:10.1109/tgrs.2012.2186819

Cited by 79 publications

(66 citation statements)

References 46 publications

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“…Figure 3 shows a comparison between observed and modelled data at two sites in central Italy using the Soil Water Balance Model [67]. The agreement with observations highlights the good model performances, and similar results were obtained at several sites in Europe (e.g., [57,91]). In space, the same meteorological factors, i.e., precipitation and evapotranspiration, have a clear impact on soil moisture patterns at large scales (>500-1000 km 2 ).…”

Section: How Does the Soil Moisture Vary In Space And Time?supporting

confidence: 64%

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

Brocca

Ciabatta

Massari

et al. 2017

Water

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252

174

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Soil moisture is widely recognized as a key parameter in the mass and energy balance between the land surface and the atmosphere and, hence, the potential societal benefits of an accurate estimation of soil moisture are immense. Recently, scientific community is making great effort for addressing the estimation of soil moisture over large areas through in situ sensors, remote sensing and modelling approaches. The different techniques used for addressing the monitoring of soil moisture for hydrological applications are briefly reviewed here. Moreover, some examples in which in situ and satellite soil moisture data are successfully employed for improving hydrological monitoring and predictions (e.g., floods, landslides, precipitation and irrigation) are presented. Finally, the emerging applications, the open issues and the future opportunities given by the increased availability of soil moisture measurements are outlined.

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Section: How Does the Soil Moisture Vary In Space And Time?supporting

confidence: 64%

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

Brocca

Ciabatta

Massari

et al. 2017

Water

Self Cite

252

174

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“…The soil water balance model was extensively validated with actual soil moisture measurements in different studies already published in the scientific literature (Brocca et al, 2008(Brocca et al, , 2014bLacava et al, 2012). Specifically, in Brocca et al (2013) the model was validated exactly in the same study area by obtaining reliable and satisfactory results.…”

Section: Soil Water Balance Modelmentioning

confidence: 99%

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Todisco

Brocca

Termite

et al. 2015

Hydrol. Earth Syst. Sci.

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Abstract. The potential of coupling soil moisture and a Universal Soil Loss Equation-based (USLE-based) model for event soil loss estimation at plot scale is carefully investigated at the Masse area, in central Italy. The derived model, named Soil Moisture for Erosion (SM4E), is applied by considering the unavailability of in situ soil moisture measurements, by using the data predicted by a soil water balance model (SWBM) and derived from satellite sensors, i.e., the Advanced SCATterometer (ASCAT). The soil loss estimation accuracy is validated using in situ measurements in which event observations at plot scale are available for the period 2008-2013. The results showed that including soil moisture observations in the event rainfall-runoff erosivity factor of the USLE enhances the capability of the model to account for variations in event soil losses, the soil moisture being an effective alternative to the estimated runoff, in the prediction of the event soil loss at Masse. The agreement between observed and estimated soil losses (through SM4E) is fairly satisfactory with a determination coefficient (log-scale) equal to ∼ 0.35 and a root mean square error (RMSE) of ∼ 2.8 Mg ha −1 . These results are particularly significant for the operational estimation of soil losses. Indeed, currently, soil moisture is a relatively simple measurement at the field scale and remote sensing data are also widely available on a global scale. Through satellite data, there is the potential of applying the SM4E model for large-scale monitoring and quantification of the soil erosion process.

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“…Finally, the 8-20% interval (eight stations) presented a positive bias lower than the overall proportion (12.5% positive and 87.5% negative). Underestimation was identified by some authors during the validation procedures [1,6,24,44,47,48]. He et al [49] verified that the sun-glint, which is the reflected solar radiation from the land surface near the specular direction, can affect the brightness temperature by increasing emissivity and decreasing soil moisture, which result in negative BIAS.…”

Section: Discussionmentioning

confidence: 99%

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

et al. 2018

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Abstract:The goal of this study was to validate soil moisture data from Soil Moisture Ocean Salinity (SMOS) using two in situ databases for Pernambuco State, located in Northeast Brazil. The validation process involved two approaches, pixel-station comparison and areal average, for three regions in Pernambuco with different climatic characteristics. After validation, the SMOS data were used for drought assessment by calculating soil moisture anomalies for the available period of data. Four statistical criteria were used to verify the quality of the satellite data: Pearson correlation coefficient, Willmott index of agreement, BIAS, and root mean squared difference (RMSD). The average RMSD calculated from the daily time series in the pixel and the areal assessment were 0.071 m 3 ·m −3 and 0.04 m 3 ·m −3 , respectively. Those values are near to the expected 0.04 m 3 ·m −3 accuracy of the SMOS mission. The analysis of soil moisture anomalies enabled the assessment of the dry period between 2012 and 2017 and the identification of regions most impacted by the drought. The driest year for all regions was 2012, when the anomaly values achieved −50% in some regions. The use of SMOS data provided additional information that was used in conjunction with the precipitation data to assess drought periods. This may be particularly relevant for planning in agriculture and supporting decision makers and farmers.

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A First Assessment of the SMOS Soil Moisture Product With In Situ and Modeled Data in Italy and Luxembourg

Cited by 79 publications

References 46 publications

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

Use of satellite and modeled soil moisture data for predicting event soil loss at plot scale

Use of SMOS L3 Soil Moisture Data: Validation and Drought Assessment for Pernambuco State, Northeast Brazil

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