A global comparison of alternate AMSR2 soil moisture products: Why do they differ?

Soil moisture products acquired from passive satellite missions have been widely applied in environmental processes. A primary challenge for the use of soil moisture products from passive sensors is their reliability. It is crucial to evaluate the reliability of those products before they can be routinely used at a global scale. In this paper, we evaluated the Soil Moisture Active/Passive (SMAP) and the Advanced Microwave Scanning Radiometer (AMSR2) radiometer soil moisture products against in situ measurements collected from American networks with four statistics, including the mean difference (MD), the root mean squared difference (RMSD), the unbiased root mean square error (ubRMSE) and the correlation coefficient (R). The evaluation results of SMAP and AMSR2 soil moisture products were compared. Moreover, the triple collocation (TC) error model was used to assess the error among AMSR2, SMAP and in situ data. The monthly average and daily AMSR2 and SMAP soil moisture data were analyzed. Different spatial series, temporal series and combined spatial-temporal analysis were carried out. The results reveal that SMAP soil moisture retrievals are generally better than AMSR2 soil moisture data. The remotely sensed retrievals show the best agreement with in situ measurements over the central Great Plains and cultivated crops throughout the year. In particular, SMAP soil moisture data shows a stable pattern for capturing the spatial distribution of surface soil moisture. Further studies are required for better understanding the SMAP soil moisture product.

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“…This result was also obtained by previous studies [24]. The weak results from this evaluation may be explained by several reasons.…”

Section: Discussionsupporting

confidence: 87%

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

Zhang

Ping

et al. 2017

Remote Sensing

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“…At the time of writing (January 2017), four quasi-operational, i.e., available either in near real time (NRT) or few days after sensing, coarse resolution satellite surface soil moisture products are available: (1) the Soil Moisture Active and Passive (SMAP) mission (L-band radiometer) starting from April 2015 with~36 km/2-day spatial/temporal resolution [49]; (2) the Advanced Microwave Scanning Radiometer 2 (AMSR2) onboard the Global Change Observation Mission for Water, GCOM-W, satellite (C-and X-band radiometers) starting from July 2012 with~25 km/1-day spatial/temporal resolution [50]; (3) the Soil Moisture and Ocean Salinity (SMOS) mission product (L-band radiometer) starting from January 2010 with~50 km/2-day spatial/temporal resolution [51]; and (4) the Advanced SCATterometer (ASCAT) onboard Metop-A and Metop-B satellites (C-band scatterometer) starting from January 2007 with~25 km/1-day spatial/temporal resolution [45]. Recently, higher resolution (~1 km) soil moisture products are becoming available based on the disaggregation of coarse resolution products [52][53][54] and, in the near future, from Sentinel-1 satellites (e.g., [55]).…”

Section: Remote Sensingmentioning

confidence: 99%

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

Brocca

Ciabatta

Massari

et al. 2017

Water

259

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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“…For more details on soil moisture remote sensing, it is recommended to refer to these review articles. There are also studies on the evaluation and comparison of advanced remote sensing soil moisture products [130][131][132][133][134][135], and these studies provide a better knowledge of quality, reliability and suitability of current remote sensing soil moisture products. The main focus of this section is the application of RS-SM products on flood/streamflow forecasting.…”

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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A global comparison of alternate AMSR2 soil moisture products: Why do they differ?

Cited by 159 publications

References 55 publications

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

Validation Analysis of SMAP and AMSR2 Soil Moisture Products over the United States Using Ground-Based Measurements

Soil Moisture for Hydrological Applications: Open Questions and New Opportunities

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

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