Comparison of high-resolution airborne soil moisture retrievals to SMAP soil moisture during the SMAP validation experiment 2016 (SMAPVEX16)

Colliander, Andreas; Cosh, Michael H.; Misra, Sidharth; Jackson, Thomas J.; Crow, Wade T.; Powers, Jarrett; McNairn, Heather; Bullock, Paul; Berg, Aaron; Magagi, Ramata; Gao, Ying; Bindlish, Rajat; Williamson, R.; Ramos, Isaac; Latham, Barron; O’Neill, Peggy; Yueh, Simon

doi:10.1016/j.rse.2019.04.004

Cited by 49 publications

(35 citation statements)

References 42 publications

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“…Field campaigns were performed during preliminary test flights of the NASA Passive–Active L‐ and S‐band (PALS) microwave sensor (Wilson et al, 2001) prior to both Soil Moisture Active–Passive Validation Experiments in 2015 (SMAPVEX15) (Colliander et al, 2017a) and 2016 (SMAPVEX16) (Colliander et al, 2019). In all, 14 campaigns occurred primarily during the spring of 2015 and 2016 (Fig.…”

Section: Resultsmentioning

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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Section: Resultsmentioning

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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“…Each temporary station implemented one horizontallyoriented Stevens HydraProbe at 5 cm depth and one verticallyoriented Stevens HydraProbe at 0-6 cm depth, with measurements taken at hourly intervals. Temporary station data were collected throughout the SMAPVEX16-MB field campaign in June-July 2016 [47] [48].…”

Section: A Smapvex16-mb Site In Carman Manitobamentioning

confidence: 99%

“…Each temporary station had one Stevens HydraProbe installed horizontally at a depth of 5 cm, collecting soil moisture at half hour intervals. Temporary station data were collected throughout the SMAPVEX16-IA field campaign in May-August 2016 [48].…”

Section: B Smapvex16-ia Site In South Fork Iowamentioning

confidence: 99%

Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests

Whitcomb

Clewley

Colliander

et al. 2020

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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In order to validate its soil moisture products, the NASA Soil Moisture Active Passive (SMAP) mission utilizes sites with permanent networks of in situ soil moisture sensors maintained by independent calibration and validation partners in a variety of ecosystems around the world. Measurements from each core validation site (CVS) are combined in a weighted average to produce an estimate of soil moisture at a 33-km scale that represents the SMAP's radiometer-based retrievals. Since upscaled estimates produced in this manner are dependent on the weighting scheme applied, an independent method of quantifying their biases is needed. Here we present one such method that uses soil moisture measurements taken from a dense, but temporary, network of soil moisture sensors deployed at each CVS to train a Random Forests regression expressing soil moisture in terms of a set of spatial variables. The regression then serves as an independent source of upscaled estimates against which permanent network upscaled estimates can be compared in order to calculate bias statistics. This method, which offers a systematic and unified approach to estimate bias across a variety of validation sites, was applied to estimate biases at four CVSs. The results showed that the magnitude of the uncertainty in the permanent network upscaling bias can sometimes exceed 80% of the upper limit on SMAP's entire allowable unbiased root-meansquare error (ubRMSE). Such large CVS bias uncertainties could make it more difficult to assess biases in soil moisture estimates from SMAP.

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“…The first experiment, called SMAP Validation Experiment 2019–2021 (SMAPVEX19‐21), started in 2019 in two locations in the northeastern United States and will continue through 2021. The experiment follows a series of SMAP postlaunch validation experiments conducted in biomes with predominantly lower vegetation water content (e.g., Colliander, Jackson, et al, 2017; Colliander et al, 2019). The objective of the experiment is to test the SMAP retrieval in both fully forested and partially forested areas because a substantial portion of the United States and the globe have a nonuniform forest cover at the SMAP resolution scale.…”

Section: Introductionmentioning

confidence: 99%

SMAP Detects Soil Moisture Under Temperate Forest Canopies

Colliander

Cosh

Kelly

et al. 2020

Geophysical Research Letters

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Soil moisture dynamics in the presence of dense vegetation canopies are determinants of ecosystem function and biogeochemical cycles, but the capability of existing spaceborne sensors to support reliable and useful estimates is not known. New results from a recently initiated field experiment in the northeast United States show that the National Aeronautics and Space Administration (NASA) SMAP (Soil Moisture Active Passive) satellite is capable of retrieving soil moisture under temperate forest canopies. We present an analysis demonstrating that a parameterized emission model with the SMAP morning overpass brightness temperature resulted in a RMSD (root-mean-square difference) range of 0.047-0.057 m 3 /m 3 and a Pearson correlation range of 0.75-0.85 depending on the experiment location and the SMAP polarization. The inversion approach included a minimal amount of ancillary data. This result demonstrates unequivocally that spaceborne L-band radiometry is sensitive to soil moisture under temperate forest canopies, which has been uncertain because of lack of representative reference data.

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Comparison of high-resolution airborne soil moisture retrievals to SMAP soil moisture during the SMAP validation experiment 2016 (SMAPVEX16)

Cited by 49 publications

References 42 publications

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

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

Evaluation of SMAP Core Validation Site Representativeness Errors Using Dense Networks of In Situ Sensors and Random Forests

SMAP Detects Soil Moisture Under Temperate Forest Canopies

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