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

Zhang, Xuefei; Zhang, Tingting; Ping, Zhou; Shao, Yun; Gao, Shan

doi:10.3390/rs9020104

Cited by 80 publications

(55 citation statements)

References 52 publications

(52 reference statements)

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“…AMSR2 has been reported [57] to show some overestimation in dry regions, and shows a poor performance in semiarid areas when compared with SMAP and SMOS. It also appears to largely disagree with SMAP in the soil moisture spatial patterns [37,57,59], which is a phenomenon that is also observed in both the dry and wet seasons in our study. SMAP and SMOS have been reported to be the most similar, exhibiting the lowest ubRMSD values and the highest R values overall [57,60].…”

Section: Discussioncontrasting

confidence: 41%

“…In this paper, SMAP and SMOS obtain ubRMSD values of 0.038 and 0.029 m 3 /m 3 , respectively, and R values of 0.79 and 0.77, respectively, which are the lowest ubRMSD values and the highest R values among all of the products. Combining the results of previous studies [37,[56][57][58][59][60], we can conclude that SMAP and SMOS are better indicators of in situ measurements for the further evaluation of S1A soil moisture in the spatial dimension.…”

Section: Discussionmentioning

confidence: 54%

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Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal

Liu

Yang

2017

Remote Sensing

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Abstract:The spatiotemporal pattern of soil moisture is of great significance for the understanding of the water exchange between the land surface and the atmosphere. The two-satellite constellation of the Sentinel-1 mission provides C-band synthetic aperture radar (SAR) observations with high spatial and temporal resolutions, which are suitable for soil moisture monitoring. In this paper, we aim to assess the capability of pattern analysis based on the soil moisture retrieved from Sentinel-1 time-series data of Dahra in Senegal. The look-up table (LUT) method is used in the retrieval with the backscattering coefficients that are simulated by the advanced integrated equation Model (AIEM) for the soil layer and the Michigan microwave canopy scattering (MIMICS) model for the vegetation layer. The temporal trend of Sentinel-1A soil moisture is evaluated by the ground measurements from the site at Dahra, with an unbiased root-mean-squared deviation (ubRMSD) of 0.053 m 3 /m 3 , a mean average deviation (MAD) of 0.034 m 3 /m 3 , and an R value of 0.62. The spatial variation is also compared with the existing microwave products at a coarse scale, which confirms the reliability of the Sentinel-1A soil moisture. The spatiotemporal patterns are analyzed by empirical orthogonal functions (EOF), and the geophysical factors that are affecting soil moisture are discussed. The first four EOFs of soil moisture explain 77.2% of the variance in total and the primary EOF explains 66.2%, which shows the dominant pattern at the study site. Soil texture and the normalized difference vegetation index are more closely correlated with the primary pattern than the topography and temperature in the study area. The investigation confirms the potential for soil moisture retrieval and spatiotemporal pattern analysis using Sentinel-1 images.

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Section: Discussioncontrasting

confidence: 41%

Section: Discussionmentioning

confidence: 54%

Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal

Liu

Yang

2017

Remote Sensing

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“…In response to these challenges confronted by the validation, the triple collocation (TC) technique has been developed and extensively introduced to validate satellite products, such as soil moisture products (Alemohammad et al, 2015;An et al, 2016;Crow et al, 2012;Draper et al, 2013;Scipal et al, 2008;Zhang et al, 2017), leaf area index products (Fang et al, 2012), fraction of absorbed photosynthetically active radiation products (D'Odorico et al, 2014), land water storage (van Dijk et al, 2014), ocean wind speed wave height (Janssen et al, 2007;Portabella & Stoffelen, 2009), and sea surface salinity (Ratheesh et al, 2013). This technique is believed to be able to provide root-mean-square error (RMSE) of satellite products without requiring the truth (Alemohammad et al, 2015;Scipal et al, 2013).…”

Section: Introductionmentioning

confidence: 99%

Direct Comparison and Triple Collocation: Which Is More Reliable in the Validation of Coarse‐Scale Satellite Surface Albedo Products

Xiao

Wen

et al. 2019

JGR Atmospheres

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A rigorous and reliable validation is the prerequisite and basis to properly understand the accuracy of satellite surface albedo products. However, the representativeness error of point scale in situ measurements, which mainly results from the spatial mismatch between in situ and satellite measurements as well as the surface heterogeneities, generally hinders an effective validation of satellite surface albedo products based on the conventional direct comparison method. Triple collocation technique has been recognized as a powerful tool to estimate the errors of satellite products without requiring the truth. Here, we evaluated the effectiveness of both the direct comparison and extended triple collocation (ETC) methods in tackling with the representativeness error of in situ measurements and characterizing the performances of satellite surface albedo products (MCD43A3 [V006], GLASS, and NPP VIIRS albedos) given whether the in situ‐based pixel‐scale albedo was available or not. The ETC method is shown to be advantageous over the direct comparison based on single “point” in situ measurements, because it is less affected by representativeness errors. However, it shall not substitute for the direct comparison method based on in situ‐based pixel‐scale albedo measurements, because its effectiveness in solving representativeness errors and estimating product errors is very limited and dependent on the choice of the data in the triplets. Furthermore, the ETC technique cannot be used to estimate the true physical errors of satellite surface albedo products.

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“…Several algorithms are available for soil moisture retrievals using passive microwave instruments, including the single‐channel algorithm [ Jackson , ; Jackson et al ., , ], the multifrequency‐polarization iterative algorithm [ Njoku and Li , ; Njoku et al ., ; Koike et al ., ], the look‐up table algorithm [ Fujii et al ., ], the polarization index algorithm [ Paloscia et al ., ], and the Dual‐Channel Algorithm based on the Qp model (QDCA) [ Shi et al ., , ]. Recently, with these algorithms considerable satellite soil moisture validation work has been conducted in the United States [ Collow et al ., ; Leroux et al ., ; Ford et al ., ; Kornelsen et al ., ; Kim et al ., ; Chan et al ., ; Zhang et al ., ], Europe [ Mittelbach et al ., ; Pierdicca et al ., ; Kim et al ., ; Chan et al ., ; Gruber et al ., ], Australia [ Panciera et al ., ; Draper et al ., ; Su et al ., ; Kim et al ., ; O'Neill et al ., ; Yee et al ., ; Cho et al ., ], and the Tibetan Plateau, China[ Su et al ., , ; Liu et al ., ; Chen et al ., ; Bi et al ., ]. However, limited comparative work has been performed in northeast China.…”

Section: Introductionmentioning

confidence: 99%

Evaluation and analysis of AMSR‐2, SMOS, and SMAP soil moisture products in the Genhe area of China

Cui

Jiang

et al. 2017

JGR Atmospheres

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High‐precision soil moisture products play an important role in estimating forest carbon storage and carbon emissions in Genhe, China. In this paper, we evaluated the Soil Moisture and Ocean Salinity (SMOS) L3 product, the Soil Moisture Active Passive (SMAP) L3 product, and four soil moisture products derived from the Advanced Microwave Scanning Radiometer 2 (AMSR‐2), i.e., the Dual Channel Algorithm based on the Qp model (QDCA) product, the Japan Aerospace Exploration Agency (JAXA) L3 product, and the Land Parameter Retrieval Model (LPRM) C band and X band products in the Genhe area of China. The results indicated that the root‐mean‐square error (RMSE) and bias of the QDCA product were lower than those of the other AMSR‐2 products, although the QDCA still fell outside of the acceptable range with a volumetric error of no greater than 6%. The JAXA product underestimated the soil moisture and had a constant bias of 0.089–0.099 m3 m−3. The LPRM C‐band and X‐band products had a constant variable season bias of 0.261–0.576 m3 m−3. The quality of the SMOS was better than that of the AMSR‐2 products; however, the results were noisy and unstable. The SMAP was closest to the ground measurements and presented a low RMSE (0.039–0.063 m3 m−3) and bias (0.022–0.050 m3 m−3). Finally, an assessment was performed on the parameters in these soil moisture algorithms.

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

Cited by 80 publications

References 52 publications

Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal

Soil Moisture Retrieval and Spatiotemporal Pattern Analysis Using Sentinel-1 Data of Dahra, Senegal

Direct Comparison and Triple Collocation: Which Is More Reliable in the Validation of Coarse‐Scale Satellite Surface Albedo Products

Evaluation and analysis of AMSR‐2, SMOS, and SMAP soil moisture products in the Genhe area of China

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