Validation of SMAP Soil Moisture at Terrestrial National Ecological Observatory Network (NEON) Sites Show Potential for Soil Moisture Retrieval in Forested Areas

Ayres, Edward; Colliander, Andreas; Cosh, Michael H.; Roberti, Joshua A.; Simkin, Sam; Genazzio, Melissa A.

doi:10.1109/jstars.2021.3121206

Cited by 30 publications

(13 citation statements)

References 61 publications

(90 reference statements)

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“…Whereas, the variation in the estimation for CYGNSS (80% CI = 0.046, 0.075) and for the SMAP (80% CI = 0.036, 0.070) are close. It can be found that the performance of SMAP are perfectly consistent with previous sparse network validation report (Ayres et al 2021), median ubRMSD between CYGNSS and SMAP is 0.049 cm 3 cm − 3 , which is also in line with previous studies (Chew and Small 2018; Al-Khaldi et al 2019; Dong and Jin 2021), and veri es the correctness of our data preprocessing and metric calculation. In comparison to the relative metrics calculated from the raw time series, the R 2 and ubRMSD values of short-anomalies time series is smaller since the processing of sliding averaging screens the seasonal cycle, while the CYGNSS still shows large uncertainty.…”

Section: Relative Evaluation Of Ssm Against Ground Observationssupporting

confidence: 91%

Validation of CYGNSS soil moisture products using in-situ measurments: A case study of Southern China

Dong

Jin

et al. 2023

Preprint

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In terrestrial remote sensing applications, the spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has demonstrated its worth. The application to land surface soil moisture (SSM) detection is particularly intriguing since it has the ability to provide fine-scale results to supplement traditional satellite-based active and passive missions. To date, many retrieval algorithms for spaceborne GNSS-R have been developed in order to produce SSM products. However, detailed product reliability and robustness evaluations are still absent. In this study, the satellite-based microwave radiometry product, the model-base product, and in-situ measurements from the Chinese soil moisture monitoring network with over 1800 ground stations during the year 2018 were used to evaluate the CYclone Global Navigation Satellite System (CYGNSS) mission Level-3 SSM products released by the University Corporation for Atmospheric Research (UCAR) and the University of Colorado at Boulder (CU). Typical relative skill metrics and triple collocation-based metrics, along with corresponding confidence intervals, are given to analyze the performance. According to the pixel-by-pixel validation and overall statistical findings, the results reveal that the current CYGNSS-based SSM exhibits low performance in southern China when compared to the radiometry-based data with a low R2 (median R2=0.09) and the ubRMSD 0.055 cm3cm-3, which is poorer than the results from SMAP against in-situ measurements (median R2=0.25, ubRMSD=0.046 cm3cm-3). To acquire better results to support the related operational applications in the future, the new enhanced retrieval algorithms and high-accuracy calibration referenced data must be used.

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Section: Relative Evaluation Of Ssm Against Ground Observationssupporting

confidence: 91%

Validation of CYGNSS soil moisture products using in-situ measurments: A case study of Southern China

Dong

Jin

et al. 2023

Preprint

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“…Thereafter, the objective was broadened to include areas outside of the original validation domain, including forests. The mission is currently engaged in exploring the improvement and validation of SMAP SM products over forested areas through field experiments [52], [161], an added focus on forested candidate CVS (see Table III) and other networks, such as the National Ecological Observatory Network [53]. Furthermore, the effort to expand the validation domain includes accounting for the complex soil composition of the boreal and arctic regions.…”

Section: Discussionmentioning

confidence: 99%

“…Surfaces with permanent ice and snow, urban areas, wetlands, and areas with above-ground vegetation water content (VWC) greater than 5 kg/m 2 are excluded from the formal accuracy requirements and identified with a nonzero retrieval quality flag [1]. In recent years, the SMAP SM algorithm research has included improving the quality of SM retrievals in more densely vegetated regions, which has resulted in validation activities in forested areas [52], [53].…”

Section: Smap Sm Data Productsmentioning

confidence: 99%

Validation of Soil Moisture Data Products From the NASA SMAP Mission

Colliander

Reichle

Crow

et al. 2022

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

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The National Aeronautics and Space Administration Soil Moisture Active Passive (SMAP) mission has been validating its soil moisture (SM) products since the start of data production on March 31, 2015. Prior to launch, the mission defined a set of criteria for core validation sites (CVS) that enable the testing of the key mission SM accuracy requirement (unbiased root-meansquare error <0.04 m 3 /m 3 ). The validation approach also includes other ("sparse network") in situ SM measurements, satellite SM products, model-based SM products, and field experiments. Over the past six years, the SMAP SM products have been analyzed with respect to these reference data, and the analysis approaches themselves have been scrutinized in an effort to best understand the products' performance. Validation of the most recent SMAP Level 2 and 3 SM retrieval products (R17000) shows that the L-band (1.4 GHz) radiometer-based SM record continues to meet mission requirements. The products are generally consistent with SM retrievals from the European Space Agency Soil Moisture Ocean Salinity mission, although there are differences in some regions. The high-resolution (3-km) SM retrieval product, generated by combining Copernicus Sentinel-1 data with SMAP observations, performs within expectations. Currently, however, there is limited availability of 3-km CVS data to support extensive validation at this spatial scale. The most recent (version 5) SMAP Level 4 SM data assimilation product providing surface and root-zone SM with complete spatio-temporal coverage at 9-km resolution also meets performance requirements. The SMAP SM validation program Manuscript

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“…However, this study intends to examine the accuracy of SMAP in forested regions. Accordingly, the SMAP quality flags were not applied in this study (similar to the study detailed in [37]).…”

Section: Smap Soil Moisture Data Productmentioning

confidence: 99%

Assessing the Spatiotemporal Variability of SMAP Soil Moisture Accuracy in a Deciduous Forest Region

et al. 2022

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The goal of this study is to assess the temporal variability of the performance of the Soil Moisture Active Passive, SMAP, soil moisture retrievals throughout the seasons as surface conditions change. In-situ soil moisture observations from a network deployed in Millbrook, New York, between 2019 and 2021 are used. The network comprises 25 stations distributed across a 33-km SMAP pixel with a predominantly forest land cover. The in-situ soil moisture observations were collected between 6 and 7 a.m., local time. This article covers the assessment of the temporal accuracy of SMAP soil moisture by incorporating various upscaling methods. Four upscaling methods are used in this study: arithmetic average, Voronoi diagram, topographic wetness index, and land cover weighted average. The agreement between SMAP soil moisture and the upscaled in-situ measurements was gauged using the root-mean-squared difference, the mean difference, and the unbiased root-mean-squared difference. The consistency of the temporal variability of SMAP soil moisture data resulting from the four upscaling methods was analyzed. The results revealed that SMAP retrievals (soil moisture data) are systematically higher than in situ observations during the different seasons. The results indicate that the highest performance of SMAP soil moisture retrievals is in September with an ubRMSD value of 0.03 m3.m−3 for the morning and evening overpasses, which can be attributed to a lower vegetation density during the seasonal transition. The agreement with in-situ observations degrades during March–April with ubRMSD values above 0.04 m3.m−3, reaching ~0.06 m3.m−3 in April, which can be attributed to the non-reliability of in-situ measurements due to freeze\thaw transition and the challenging determination of the soil effective temperature. The ubRMSD is also higher than 0.04 m3.m−3 in the months of May–June, which could be due to the introduced vegetation effect during the growth season. These findings are consistent across all the upscaling methods. The average ubRMSD over the study period is 0.055 m3.m−3, which falls short of meeting the mission’s performance target. This study proves the need to enhance SMAP retrieval over forest sites.

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Validation of SMAP Soil Moisture at Terrestrial National Ecological Observatory Network (NEON) Sites Show Potential for Soil Moisture Retrieval in Forested Areas

Cited by 30 publications

References 61 publications

Validation of CYGNSS soil moisture products using in-situ measurments: A case study of Southern China

Validation of CYGNSS soil moisture products using in-situ measurments: A case study of Southern China

Validation of Soil Moisture Data Products From the NASA SMAP Mission

Assessing the Spatiotemporal Variability of SMAP Soil Moisture Accuracy in a Deciduous Forest Region

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