A Triple Collocation-Based Comparison of Three L-Band Soil Moisture Datasets, SMAP, SMOS-IC, and SMOS, Over Varied Climates and Land Covers

Kim, Seokhyeon; Dong, Jianzhi; Sharma, Ashish

doi:10.3389/frwa.2021.693172

Cited by 18 publications

(9 citation statements)

References 72 publications

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“…An investigation whether the fast changing surface signals can also be detected in the GRACE data will be pursued in Section 4.3 for high-pass filtered time series. In a comparison of the three SSM products, SMAP L3 and the multi-satellite combination product ESA CCI time series appear less noisy than the SMOS L3 time series, which is in good agreement with findings of, e.g., Montzka et al (2017), Cui et al (2017), Xu and Frey (2021) and Kim et al (2021).…”

Section: Time Series Comparison For An Example Locationsupporting

confidence: 84%

A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry

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Eicker

Jensen

et al. 2022

Preprint

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Abstract. Water storage changes in the soil can be observed on a global scale with different types of satellite remote sensing. While active or passive microwave sensors are limited to the upper few centimeters of the soil, satellite gravimetry can detect changes of terrestrial water storage (TWS) in an integrative way but it cannot distinguish between storage variations in different compartments or soil depths. Jointly analyzing both data types promises novel insights into the dynamics of subsurface water storage and of related hydrological processes. In this study, we investigate the global relationship of (1) several satellite soil moisture products and (2) non-standard daily TWS data from the GRACE and GRACE-FO satellite gravimetry missions on different time scales. The six soil moisture products analyzed in this study differ in post-processing and the considered soil depth. Level-3 surface soil moisture data sets of SMAP and SMOS are compared to post-processed Level-4 data products (surface and root zone soil moisture) and the ESA CCI multi-satellite product. On a common global 1 degree grid, we decompose all TWS and soil moisture data into seasonal to sub-monthly signal components and compare their spatial patterns and temporal variability. We find larger correlations between TWS and soil moisture for soil moisture products with deeper integration depths (root zone vs. surface layer) and for Level-4 data products. Even for high-pass filtered sub-monthly variations, significant correlations of up to 0.6 can be found in regions with large high-frequency storage variability. A time-shift analysis of TWS versus soil moisture data reveals the differences in water storage dynamics with integration depth.

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Section: Time Series Comparison For An Example Locationsupporting

confidence: 84%

A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry

Blank

Eicker

Jensen

et al. 2022

Preprint

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“…However, all the data exhibit relatively low SD TC and R TC values over northern Africa and the Arabian Peninsula with barren land covers and arid/semi-arid climates. The reasons given for this discrepancy can be concluded: i) The radiometer/radar has a challenge in receiving the relevant signals over extremely dry environments [30,52]. ii) The SM retrievals are derived from deeper soil layers due to the lower frequency microwave bands penetrating deeper in dry soil layers [53,54].…”

Section: ) Tc-based Accuracy Assessmentmentioning

confidence: 99%

“…Relative to the other land cover types, despite all the products with lower SD TC values, the performance of R TC degraded over the barren lands. Previous validation works [52,56] also indicated that satellite-based products have difficulty in capturing the temporal SM variations over barren areas. This discrepancy also appears in the subsequent in-situ validations in barren lands.…”

Section: ) Comparisons Over Different Land Cover Classesmentioning

confidence: 99%

Triple Collocation Analysis and In Situ Validation of the CYGNSS Soil Moisture Product

Deng

Zhu

Wang

et al. 2023

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

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along with a low median R of 0.414. Both inter-product comparisons of TC analysis and in-situ validations revealed that the CYGNSS product was characterized by small SDTC and ubRMSE but performed poorly in capturing SM temporal variability. Additionally, the performance degradation for CYGNSS capturing the SM temporal variability over the barren areas including in Northern Africa, the Arabian Peninsula, and Central Australia with arid/semi-arid climates, and forested regions including in eastern South America, the Indo-China Peninsula, and Southeastern China with temperate/tropical climates. This suggests that capturing SM temporal variations over barren and forests regions is a key priority to improve CYGNSS SM algorithms.

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“…The Moderate-Resolution Imaging Spectroradiometer (MODIS) data can provide the spectral information of the soil surface related to the SM and have a finer spatial resolution than CYGNSS and passive remote sensing SM products (Babaeian et al, 2018 ). Some recent studies have proposed different multi-source remote sensing fusion methods for SM spatial reconstruction, e.g., Kalman filtering, triple collocation, random forest, and back-propagation (BP) neural network (Xu et al, 2018 ; Fu et al, 2019 ; Long et al, 2019 ; Kim et al, 2021 ; Wu et al, 2021 ). Nevertheless, it should be noted that the existing algorithms mostly ignore the missing data caused by the influence of the cloud on the optical remote sensing data, which lead to the discontinuity of the fusion result.…”

Section: Introductionmentioning

confidence: 99%

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data

Yang¹,

Sun²,

Jundong³

et al. 2022

Front. Big Data

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Daily spatial complete soil moisture (SM) mapping is important for climatic, hydrological, and agricultural applications. The Cyclone Global Navigation Satellite System (CYGNSS) is the first constellation that utilizes the L band signal transmitted by the Global Navigation Satellite System (GNSS) satellites to measure SM. Since the CYGNSS points are discontinuously distributed with a relativity low density, limiting it to map continuous SM distributions with high accuracy. The Moderate-Resolution Imaging Spectroradiometer (MODIS) product (i.e., vegetation index [VI] and land surface temperature [LST]) provides more surface SM information than other optical remote sensing data with a relatively high spatial resolution. This study proposes a point-surface fusion method to fuse the CYGNSS and MODIS data for daily spatial complete SM retrieval. First, for CYGNSS data, the surface reflectivity (SR) is proposed as a proxy to evaluate its ability to estimate daily SM. Second, the LST output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625° × 0.0625°) and MODIS LST (1 × 1 km) are fused to generate spatial complete and temporally continuous LST maps. An Enhanced Normalized Vegetation Supply Water Index (E-NVSWI) model is proposed to estimate SM derived from MODIS data at high spatial resolution. Finally, the final SM estimation model is constructed from the back-propagation artificial neural network (BP-ANN) fusing the CYGNSS point, E-NWSVI data, and ancillary data, and applied to get the daily continuous SM result over southeast China. The results show that the estimation SM are comparable and promising (R = 0.723, root mean squared error [RMSE] = 0.062 m3 m−3, and MAE = 0.040 m3 m−3 vs. in situ, R = 0.714, RMSE = 0.057 m3 m−3, and MAE = 0.039 m3 m−3 vs. CLDAS). The proposed algorithm contributes from two aspects: (1) validates the CYGNSS derived SM by taking advantage of the dense in situ networks over Southeast China; (2) provides a point-surface fusion model to combine the usage of CYGNSS and MODIS to generate the temporal and spatial complete SM. The proposed approach reveals significant potential to map daily spatial complete SM using CYGNSS and MODIS data at a regional scale.

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A Triple Collocation-Based Comparison of Three L-Band Soil Moisture Datasets, SMAP, SMOS-IC, and SMOS, Over Varied Climates and Land Covers

Cited by 18 publications

References 72 publications

A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry

A global analysis of water storage variations from remotely sensed soil moisture and daily satellite gravimetry

Triple Collocation Analysis and In Situ Validation of the CYGNSS Soil Moisture Product

Daily Spatial Complete Soil Moisture Mapping Over Southeast China Using CYGNSS and MODIS Data

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