Long-term SMOS soil moisture products: A comprehensive evaluation across scales and methods in the Duero Basin (Spain)

González-Zamora, Ángel; Sánchez, Nilda; Martínez-Fernández, José; Gumuzzio, A.; Piles, María; Olmedo, Estrella

doi:10.1016/j.pce.2015.05.009

Cited by 55 publications

(67 citation statements)

References 57 publications

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“…Soil moisture monitoring supplies fundamental information about interactions between soil, vegetation, and atmosphere to improve the accuracy of meteorological forecasting [3,4]. These data also help improve agricultural productivity and flood and drought risk management, contributing to supporting actions that mitigate the effects of water scarcity [1,5,6]. Soil moisture has been considered an important variable for establishing drought severity.…”

Section: Introductionmentioning

confidence: 99%

“…Soil moisture can be obtained by in situ measurements, which is expensive for large areas [4,9], or by sensors on board satellites for global monitoring. Given the limitations of in situ measurements, remote sensing has become an efficient alternative for collecting data on a large scale for studies where time and space are relevant aspects [2,5,6].…”

Section: Introductionmentioning

confidence: 99%

“…This microwave imaging radiometer using aperture synthesis (MIRAS), used in the present work, operates on the L-band at frequency 1.4 GHz and polarization horizontal-vertical (H-V) [2,6,13,18]. These products are available with a daily time resolution for three-day, nine-day, monthly, and yearly periods [19] and have an average ground resolution of 43 km [13], but can be better depending on the level of processing (40 km for L2, 25 km for L3, and 1 km for L4).…”

Section: Introductionmentioning

confidence: 99%

“…Some of these networks include: Soil Moisture Measurement Stations Network of the University of Salamanca (REMEDHUS), Spain [5,6]; Wales Soil Moisture Network (WSMN), UK [21]; and OzNet in Australia [14,20]. South America is an example of a region that lacks in situ data, mainly due to its large territory where data collection is often unviable.…”

Section: Introductionmentioning

confidence: 99%

“…The SMOS products represent the heterogeneity of a surface with only one pixel, complicating validation [6,22], whereas the in situ observation networks provide data for validation on several scales [6,22,23]. The density of the in situ stations is crucial for the satisfactory representation of wet and dry periods in the region represented by the pixel.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

et al. 2018

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Root-zone plant available water estimation using the SMOS-derived soil water index

González-Zamora

Sánchez

Martínez-Fernández

et al. 2016

Advances in Water Resources

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Currently, there are several space missions capable of measuring surface soil moisture, owing to the relevance of this variable in meteorology, hydrology and agriculture. However, the Plant Available Water (PAW), which in some fields of application could be more important than the soil moisture itself, cannot be directly measured by remote sensing. Considering the root zone as the first 50 cm of the soil, in this study, the PAW at 25 cm and 50 cm and integrated between 0 and 50 cm of soil depth was estimated using the surface soil moisture provided by the Soil Moisture Ocean Salinity (SMOS) mission. For this purpose, the Soil Water Index (SWI) has been used as a proxy of the root-zone soil moisture, involving the selection of an optimal T (T opt ), which can be interpreted as a characteristic soil water travel time. In this research, several tests using the correlation coefficient (R), the Nash-Sutcliffe score (NS), several error estimators and bias as predictor metrics were applied to obtain the T opt , making a comprehensive study of the T parameter. After analyzing the results, some differences were found between the T opt obtained using R and NS as decision metrics, and that obtained using the errors and bias, but the SWI showed good results as an estimator of the root-zone soil moisture. This index showed good agreement, with an R between 0.60 and 0.88. The method was tested from January 2010 to December 2014, using the database of the Soil Moisture Measurements Stations Network of the University of Salamanca (REMEDHUS) in Spain. The PAW estimation showed good agreement with the in situ measurements, following closely the dry-downs and wetting-up events, with R ranging between 0.60 and 0.92, and error values lower than 0.05 m 3 m -3 . A slight underestimation was observed for both the PAW and root-zone soil moisture at the different depths; this could be explained by the underestimation pattern observed with the SMOS L2 soil moisture product, in line with previous studies. Nevertheless, the results obtained in this research showed an encouraging improvement of the PAW estimation. Despite the need for more research on this issue, the results of this study show that this methodology can be useful for applications of great interest in agriculture and hydrology.

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Global soil moisture trend analysis using microwave remote sensing data and an automated polynomial-based algorithm

Mohseni,

Jamali,

Ghorbanian

et al. 2023

Global and Planetary Change

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Long-term SMOS soil moisture products: A comprehensive evaluation across scales and methods in the Duero Basin (Spain)

Cited by 55 publications

References 57 publications

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

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

Root-zone plant available water estimation using the SMOS-derived soil water index

Global soil moisture trend analysis using microwave remote sensing data and an automated polynomial-based algorithm

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