State of the Art in Large-Scale Soil Moisture Monitoring

Ochsner, Tyson; Cosh, Michael H.; Cuenca, Richard H.; Dorigo, Wouter; Draper, C.; Hagimoto, Yutaka; Kerr, Y. H.; Larson, Kristine M.; Njoku, E. G.; Small, E. E.; Zréda, Marek

doi:10.2136/sssaj2013.03.0093

Cited by 417 publications

(314 citation statements)

References 220 publications

(211 reference statements)

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“…Modern soil moisture monitoring technologies can be divided into in-situ techniques and remote sensing techniques. The early generation of in-situ monitoring techniques starts from the development of the neutron probe in the 1950s [113,114], which has been quickly commercialized and widely implemented [115]. In the 1980s, the application of in-situ soil moisture monitoring techniques was boosted by development of the time domain reflectometry (TDR) method, which uses the difference in dielectric properties of dry soil and water [116].…”

Section: Overview Of Productsmentioning

confidence: 99%

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Grimaldi

Walker

et al. 2016

Remote Sensing

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Abstract:Fluvial flooding is one of the most catastrophic natural disasters threatening people's lives and possessions. Flood forecasting systems, which simulate runoff generation and propagation processes, provide information to support flood warning delivery and emergency response. The forecasting models need to be driven by input data and further constrained by historical and real-time observations using batch calibration and/or data assimilation techniques so as to produce relatively accurate and reliable flow forecasts. Traditionally, flood forecasting models are forced, calibrated and updated using in-situ measurements, e.g., gauged precipitation and discharge. The rapid development of hydrologic remote sensing offers a potential to provide additional/alternative forcing and constraint to facilitate timely and reliable forecasts. This has brought increasing interest to exploring the use of remote sensing data for flood forecasting. This paper reviews the recent advances on integration of remotely sensed precipitation and soil moisture with rainfall-runoff models for rainfall-driven flood forecasting. Scientific and operational challenges on the effective and optimal integration of remote sensing data into forecasting models are discussed.

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Section: Overview Of Productsmentioning

confidence: 99%

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Grimaldi

Walker

et al. 2016

Remote Sensing

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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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“…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%

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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State of the Art in Large-Scale Soil Moisture Monitoring

Cited by 417 publications

References 220 publications

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

Application of Remote Sensing Data to Constrain Operational Rainfall-Driven Flood Forecasting: A Review

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

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