Assessing SMAP Soil Moisture Scaling and Retrieval in the Carman (Canada) Study Site

Bhuiyan, Hassan A. K. M.; McNairn, Heather; Powers, Jarrett; Friesen, Matthew; Pacheco, Anna; Jackson, Thomas J.; Cosh, Michael H.; Colliander, Andreas; Berg, Aaron; Rowlandson, Tracy; Bullock, Paul; Magagi, Ramata

doi:10.2136/vzj2018.07.0132

Cited by 63 publications

(40 citation statements)

References 42 publications

(57 reference statements)

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“…10 These domains were established to coincide with the SMAP Core Validation Sites operated by the USDA Agricultural Research Service near Ames, Iowa (42.03 N, 93.62 W; South Fork), and Agriculture and Agri-food Canada (AAFC) near Winnipeg, Manitoba (49.90 N, 97.14 W). 11 The South Fork experimental watershed covers an area of ∼1300 km 2 and has a subhumid climate with an average annual precipitation of 800 mm. 12 The crop land portion is dominated by two row crops, corn, and soybeans.…”

Section: Study Sitesmentioning

confidence: 99%

Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment 2016

Cosh

White

Colliander

et al. 2019

J. Appl. Rem. Sens.

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Section: Study Sitesmentioning

confidence: 99%

Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment 2016

Cosh

White

Colliander

et al. 2019

J. Appl. Rem. Sens.

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“…The arithmetic average (Method 1) was calculated for unflagged data from each in situ location within the given EASE2 pixel (Table 2) at 36, 9, and 3 km at each depth and the SD calculated for each hour. This approach was evaluated by others and found to be robust, particularly when replication was sufficient within the spatial extent (Adams et al, 2015; Clewley et al, 2017; Bhuiyan et al, 2018).…”

Section: Network Implementationmentioning

confidence: 99%

The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation

Caldwell

Bongiovanni

Cosh

et al. 2019

Vadose Zone Journal

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Core Ideas TxSON is a Core Cal/Val Site for the NASA SMAP, SMOS, and Sentinel programs. TxSON consists of 40 in situ locations nested within a 36‐km EASE2 grid cell. Locations monitor soil moisture, temperature, and precipitation at 5, 10, 20, and 50 cm. Hourly, quality‐controlled data from 2015 to 2018 are available. The spatiotemporal variability of soil water content (SWC) at the remote sensing scale requires dense monitoring for calibration and validation. Here, we present an overview of the Texas Soil Observation Network (TxSON), an intensively monitored area in the semiarid rangelands of the central Texas Hill Country. TxSON is a dense network consisting of 40 in situ locations nested at 36, 9, and 3 km within the Equal‐Area Scalable Earth Grid and serves as a Core Calibration and Validation Site for NASA's Soil Moisture Active Passive mission. The 4‐yr dataset consists of hourly SWC measured at 5, 10, 20, and 50 cm. The SWC data are upscaled using arithmetic, Voronoi, and inverse distance weighting for 36‐, (2) 9‐, and (5) 3‐km grid cells. We present the site selection, environmental characteristics, network design, quality assurance (QA), upscaling algorithms, and data structure. Ancillary data include bulk density, particle size, and carbon content for each site and depth. We also provide automated QA for each location and scripts to use our binary flagging in upscaling methods. To summarize, the 36‐km grid cell has a mean bulk density of 1.34 ± 0.18 g cm−3 and a loam textural class. The in situ SWC has a root mean square error of 0.029 m3 m−3 against gravimetric data from 14 field campaigns. TxSON continues to add new locations with additional dense networks planned in other ecotones of the Edwards Plateau. The time series data along with scripts to import, plot, and upscaled SWC are available at https://doi.org/10.18738/T8/JJ16CF.

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“…4a) affect the distributions by increasing the maximum allowed sampling distances because the surface soil moisture becomes more homogeneously distributed in space due to the typically quite widespread precipitation in that region. The opposite occurs during dry periods because evaporation, draining, and runoff over various soil and land cover types tend to create spatially heterogeneous soil moisture distributions, which typically reaches its maximum at intermediate soil moisture levels (Brocca et al, 2010).…”

Section: Soil Moisturementioning

confidence: 99%

“…Qin et al (2013) suggested the use of MODIS-derived apparent thermal inertia to interpolate between in situ soil moisture measurements. So far, the required sampling density is discussed only concerning in situ measurements, which heavily depend on sensor quality and network location (Vereecken et al, 2008;Brocca et al, 2010;Bhuiyan et al, 2018). Higher station numbers are necessary, as well as the establishment of general rules for their selection .…”

Section: Introductionmentioning

confidence: 99%

Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

Schalge

Garfias

et al. 2020

Hydrol. Earth Syst. Sci.

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Abstract. Microwave remote sensing is the most promising tool for monitoring near-surface soil moisture distributions globally. With the Soil Moisture and Ocean Salinity (SMOS) and Soil Moisture Active Passive (SMAP) missions in orbit, considerable efforts are being made to evaluate derived soil moisture products via ground observations, microwave transfer simulation, and independent remote sensing retrievals. Due to the large footprint of the satellite radiometers of about 40 km in diameter and the spatial heterogeneity of soil moisture, minimum sampling densities for soil moisture are required to challenge the targeted precision. Here we use 400 m resolution simulations with the regional Terrestrial System Modeling Platform (TerrSysMP) and its coupling with the Community Microwave Emission Modelling platform (CMEM) to quantify the maximum sampling distance allowed for soil moisture and brightness temperature validation. Our analysis suggests that an overall sampling distance of finer than 6 km is required to validate the targeted accuracy of 0.04 cm3 cm−3 with a 70 % confidence level in SMOS and SMAP estimates over typical mid-latitude European regions. The maximum allowed sampling distance depends on the land-surface heterogeneity and the meteorological situation, which influences the soil moisture patterns, and ranges from about 6 to 17 km for a 70 % confidence level for a typical year. At the maximum allowed sampling distance on a 70 % confidence level, the accuracy of footprint-averaged soil moisture is equal to or better than brightness temperature estimates over the same area. Estimates strongly deteriorate with larger sampling distances. For the evaluation of the smaller footprints of the active and active–passive products of SMAP the required sampling densities increase; e.g., when a grid resolution of 3 km diameter is sampled by three sites of footprints of 9 km sampled by five sites required, only 50 %–60 % of the pixels have a sampling error below the nominal values. The required minimum sampling densities for ground-based radiometer networks to estimate footprint-averaged brightness temperature are higher than for soil moisture due to the non-linearities of radiative transfer, and only weakly correlated in space and time. This study provides a basis for a better understanding of the sometimes strong mismatches between derived satellite soil moisture products and ground-based measurements.

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Assessing SMAP Soil Moisture Scaling and Retrieval in the Carman (Canada) Study Site

Abstract: Core Ideas• Upscaling methods compared in situ measures with soil moisture from the SMAP satellite. • The accuracy of SMAP soil moisture products in annual cropland was assessed. • The spatial representativeness of sparse in situ networks was determined.

Cited by 63 publications

References 42 publications

Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment 2016

Estimating vegetation water content during the Soil Moisture Active Passive Validation Experiment 2016

The Texas Soil Observation Network:A Comprehensive Soil Moisture Dataset for Remote Sensing and Land Surface Model Validation

Required sampling density of ground-based soil moisture and brightness temperature observations for calibration and validation of L-band satellite observations based on a virtual reality

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