Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations

Albergel, Clément; Rosnay, Patricia de; Gruhier, Claire; Muñoz‐Sabater, Joaquín; Hasenauer, S.; Isaksen, Lars; Kerr, Yann H.; Wagner, Wolfgang

doi:10.1016/j.rse.2011.11.017

Cited by 441 publications

(334 citation statements)

References 40 publications

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“…The r value is negative if x increases, then y decreases. All correlations were submitted to the Student's t test with a level of 5% considered significant (p < 0.05) [38]. The Willmott index (d) indicates the level of agreement between the estimated values in relation to the observed data.…”

Section: Analysis Criteriamentioning

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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Section: Analysis Criteriamentioning

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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“…The remotely sensed ASCAT surface soil wetness measurements are representative of a thin surface layer of soil 1 cm thick (Albergel et al 2012). However, the CosmOz observations and JASMIN analyses used in this study are for a significantly deeper layer of soil.…”

Section: Mentsmentioning

confidence: 99%

JASMIN: A prototype high resolution soil moisture analysis system for Australia

Dharssi¹,

Kumar²

2017

Bureau Research Report

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“…To obtain water balance components on a larger scale, one might use systems that require the application of relatively complex physical principles and techniques, such as Bowen ratio-energy balance, eddy covariance, and scintillometers (Allen et al, 2011). Furthermore, recent developments in large-scale soil moisture monitoring-including satellite data and global soil moisture networks, for instance-reveal opportunities to advance science and practice in this regard (Albergel et al, 2012;Ochsner et al, 2013). According to recent literature, irrigation water management is more and more considering remotely sensed data, partly in combination with model approaches, in order to obtain spatial information (e.g., Guermazi et al, 2016;Toureiro et al, 2016).…”

Section: Quantifying Soil Water Balance Componentsmentioning

confidence: 99%

A review on the quantification of soil water balance components as a basis for agricultural water management with a focus on weighing lysimeters and soil water sensors / Ein Überblick über die Ermittlung von Wasserhaushaltsgrößen als Basis für die landeskulturelle Wasserwirtschaft mit Fokus auf Lysimeter und Bodenwassersensoren

Nolz

2016

Die Bodenkultur: Journal of Land Management, Food and Environment

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SummaryKnowing the components of a soil water balance-for example, evapotranspiration, soil water content, and precipitation-is the basis for agricultural water management. Weighing lysimeters and soil water sensors are commonly used to quantify these components. Data can be used to validate common models to estimate evapotranspiration based on meteorological data, for instance. As every measurement device has its own characteristics, it is helpful to assess and improve the performance of a system to obtain best possible data. Recent developments in the processing of lysimeter data allow determining both evapotranspiration and precipitation directly from lysimeter data. Resulting datasets are characterized by a proper accuracy, completeness, and a high temporal resolution. Soil water sensors usually measure a physical property that is related to soil water content or matric potential via a specific calibration function. Hence, measurement accuracy depends not only on this calibration but also on basic physical principles and material properties. Knowing the performance of a device is, therefore, essential for the selection of an adequate sensor arrangement and truthful data interpretation. Advanced soil water monitoring sites combine different sensor types that are integrated into a wireless network to enable real-time data availability and provide a basis for large-scale monitoring. Keywords

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Evaluation of remotely sensed and modelled soil moisture products using global ground-based in situ observations

Cited by 441 publications

References 40 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

JASMIN: A prototype high resolution soil moisture analysis system for Australia

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