NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

Yin, Jifu; Zhan, Xiwu; Liu, Jicheng

doi:10.3390/rs12172861

Cited by 19 publications

(9 citation statements)

References 43 publications

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“…To address those discrepancies, the soil moisture operational product system (SMOPS) is operationally produced by National Oceanic and Atmospheric Administration (NOAA) to offer the real-time blended satellite SM observations [9][10][11][12][13][14]. As NOAA requires highquality satellite SM observations with short latency, the SMOPS combines all available individual SM observations within the numerical weather prediction (NWP) 6 h cut-off Remote Sens.…”

Section: Introductionmentioning

confidence: 99%

“…2022, 14, 1700 2 of 10 time and 24 h windows to generate the 6-hourly and daily blended products. Considering the operational users' feedback, the SMOPS was updated twice by removing old sensors, adding new satellite platforms and improving retrieval algorithms [13,14]. The current SMOPS version 3.0 combines the SM retrievals from ASCATA, ASCATB, AMSR-2, SMOS and SMAP [14].…”

Section: Introductionmentioning

confidence: 99%

“…Considering the operational users' feedback, the SMOPS was updated twice by removing old sensors, adding new satellite platforms and improving retrieval algorithms [13,14]. The current SMOPS version 3.0 combines the SM retrievals from ASCATA, ASCATB, AMSR-2, SMOS and SMAP [14]. Due to the different spatial resolution, polarization, frequency, revisit time and spatiotemporal coverage, the SM retrievals from different sensors must be processed toward a consistent mergeable dataset with the same climatology [15].…”

Section: Introductionmentioning

confidence: 99%

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A New Method for Generating the SMOPS Blended Satellite Soil Moisture Data Product without Relying on a Model Climatology

et al. 2022

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Soil moisture operational product system (SMOPS) is developed by National Oceanic and Atmospheric Administration (NOAA) to provide the real-time blended soil moisture (SM) for numeric weather prediction and national water model applications. However, all individual satellite SM data ingested into the current operational SMOPS are scaled to global land data assimilation system (GLDAS) 0–10 cm SM climatology before the combination. As a result, the useful information from the original microwave SM retrievals could be lost, and the GLDAS model errors could be brought into the final SMOPS blended product. In this paper, we propose to scale the individual SM retrievals to the soil moisture active passive (SMAP) data through building regression models. The rescaled individual SM data and the SMAP observations then have similar climatology and dynamics, which allows producing the SMOPScdr (distinguishing with the current operational SMOPSopr) data using an equal-weight averaging approach. With respect to the in situ SM measurements, the developed SMOPScdr is more successful tracking the surface SM status than the individual satellite SM products with significantly decreased errors. The proposed method also preserves the climatology of the reference SMAP data for the period when SMAP is not available, allowing us to produce a long-term SMOPScdr data product.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A New Method for Generating the SMOPS Blended Satellite Soil Moisture Data Product without Relying on a Model Climatology

et al. 2022

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“…In many ways the H SAF ASCAT SSM data are similar to SSM data provided by the L-band Soil Moisture Active Passive (SMAP) and Soil Moisture Ocean Salinity (SMOS) missions, and higher-frequency radiometers such as the Advanced Microwave Scanning Radiometer 2 (AMSR2) [8]- [11]. Therefore it is possible to fuse them with passive SSM data to create consistent climate data records that are more accurate than single satellite data records [12], [13]. The prerequisite to achieve such an improvement is a detailed understanding of the accuracy of each input data set and a fusion technique capable of optimally merging the individual satellite data records [14].…”

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confidence: 99%

Global Scale Mapping of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals

Wagner,

Lindorfer,

Hahn

et al. 2023

Preprint

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<p>Microwave pulses can penetrate several centimeters to decimeters into dry soils. As a result, active microwave sensors are sensitive to discontinuities in the soil profile caused by the presence of stones, rocks or distinct soil layers. Such subsurface scattering effects can disturb the retrieval of soil moisture, vegetation and other land surface properties from active microwave measurements. In this study we mapped subsurface scatterers impacting C-band backscatter measurements acquired by the Advanced Scatterometer (ASCAT) on a global scale. Users of ASCAT soil moisture data distributed by the EUMETSAT Satellite Application Facility on Support to Operational Hydrology and Water Management are recommended to use the subsurface scattering masks generated within this study.</p>

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“…We use the proposed metrics to investigate the surface soil moisture memory captured by three spaceborne microwave sensors, namely, the ESA's Soil Moisture and Ocean Salinity (SMOS) L-band radiometer, the JAXA's C-band Advanced Microwave Scanning Radiometer-2 (AMSR2), and the Eumetsat C-band Advanced Scatterometer (ASCAT). The three sensors are integrated in the Copernicus Climate Change Service (C3S) long-term SM product [24], [25], as well as in the NOAA Satellite Soil Moisture Operational Product System (SMOPS) [26] and other recent initiatives to merge multi-satellite satellite soil moisture products [27]. Within the electromagnetic spectrum, low-frequency microwaves in the range of 1 to 10 GHz are most useful for sensing soil moisture.…”

mentioning

confidence: 99%

Autocorrelation Metrics to Estimate Soil Moisture Persistence From Satellite Time Series: Application to Semiarid Regions

Piles

Muñoz-Marí

Guerrero-Curieses

et al. 2022

IEEE Trans. Geosci. Remote Sensing

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Satellite-derived soil moisture (SM) products have become an important information source for the study of land surface processes in hydrology and land monitoring. Characterizing and estimating soil memory and persistence from satellite observations is of paramount relevance, and has deep implications in ecology, water management, and climate modeling. In this work, we address the problem of SM persistence estimation from microwave sensors using several autocorrelation metrics that, unlike traditional approaches, build on accurate estimates of the autocorrelation function from non-uniformly sampled time series. We show how the choice of the autocorrelation estimator can have a dramatic impact in the SM persistence metrics derived thereof, particularly given the non-uniform nature of satellite observations, yet this fact has been overlooked at a large extent in literature. We give empirical evidence of performance using ground-based SM measurements, as well as L-band (SMOS) and C-band (AMSR2, ASCAT) remotely sensed SM data. Experiments along transects allow to scrutinize the inter-method consistency and the spatial-temporal characteristics of autocorrelation estimators. This motivates the introduction of novel measures of spatial-temporal autocorrelation and allows us to retrieve improved persistence estimates. Results over the Iberian Peninsula indicate the SM persistence patterns captured by L and C-band microwave sensors over semi-arid regions exhibit spatially concurrent patterns of persistence and support their combination in long-term data records. We conclude that accounting for the non-uniform nature of the satellite time series using robust autocorrelation estimations allows providing improved measures and spatial descriptions of soil moisture persistence.

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NOAA Satellite Soil Moisture Operational Product System (SMOPS) Version 3.0 Generates Higher Accuracy Blended Satellite Soil Moisture

Cited by 19 publications

References 43 publications

A New Method for Generating the SMOPS Blended Satellite Soil Moisture Data Product without Relying on a Model Climatology

A New Method for Generating the SMOPS Blended Satellite Soil Moisture Data Product without Relying on a Model Climatology

Global Scale Mapping of Subsurface Scattering Signals Impacting ASCAT Soil Moisture Retrievals

Autocorrelation Metrics to Estimate Soil Moisture Persistence From Satellite Time Series: Application to Semiarid Regions

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