A Review of Satellite-Derived Soil Moisture and Its Usage for Flood Estimation

Kim, Seokhyeon; Zhang, Runze; Pham, Hung; Sharma, Ashish

doi:10.1007/s41976-019-00025-7

Cited by 40 publications

(17 citation statements)

References 175 publications

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“…Whereas, active microwave sensors transmit pulses of microwave energy to the land surface and measure the backscatter coefficient over vegetated regions (Mo et al, 1984;Wagner et al, 1999), the passive sensors observe microwave emission at various frequencies from the Earth's surface (Njoku and Kong, 1977;Jackson et al, 1982;Mo et al, 1982). The latter (i.e., passive) largely occupies the history of SM remote sensing compared to the active sensors (Kim et al, 2019). The first passive sensor for retrieving SM was SMMR (note that all undefined acronyms and abbreviations used in this study are listed in the Appendix) onboard the Nimbus-7 satellite launched in the late 1970s, which could observe brightness temperatures through multiple channels including C-band (6.6 GHz).…”

Section: Introductionmentioning

confidence: 99%

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

2021

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Soil moisture plays an important role in the hydrologic water cycle. Relative to in-situ soil moisture measurements, remote sensing has been the only means of monitoring global scale soil moisture in near real-time over the past 40 years. Among these, soil moisture products from radiometry sensors operating at L-band, e.g., SMAP, SMOS, and SMOS-IC, are theoretically established to be more advantageous than previous C/X-band products. However, little effort has been made to investigate the inter-product differences of L-band soil moisture retrievals and provide insights into the optimal use of these products. In this regard, this study aims to identify the relative strengths and weaknesses of three L-band soil moisture products across diverse climate zones and land covers at the global scale using triple collocation analysis. Results show that SMOS-IC exhibits significantly improved soil moisture estimation skills, relative to the original SMOS product. This demonstrates the paramount importance of retrieval algorithm development in improving global soil moisture estimates—given both SMOS-IC and SMOS are using the same L-band brightness temperature information. Relative to SMOS-IC, SMAP is superior across 69% of global land surface in terms of error variances. However, SMOS-IC tends to outperform SMAP over temperate/arid regions including in the east of North America, South America, western Africa, northern China, and central Australia. Additionally, considerable performance degradation of all the L-band data products is observed over unvegetated areas. This may suggest that improving soil moisture retrieval accuracy over arid and semi-arid regions should be a key priority for future L-band soil moisture development, and model-based (e.g., GLDAS) soil moisture products appear to provide more accurate soil moisture estimates over these regions.

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Section: Introductionmentioning

confidence: 99%

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

2021

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“…Recent advances in satellite remote sensing technologies have provided an avenue to monitor soil moisture over regional and global scales more consistently, filling the gap within the observational database [8]. Different imagers onboard satellites measure radiation and reflected backscatter from the earth's surface that can be linked to surface soil moisture conditions.…”

Section: Introductionmentioning

confidence: 99%

Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging

et al. 2020

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In this study, an existing combination approach that maximizes temporal correlations is used to combine six passive microwave satellite soil moisture products from 1998 to 2015 to assess its added value in long-term applications. Five of the products used are included in existing merging schemes such as the European Space Agency’s essential climate variable soil moisture (ECV) program. These include the Special Sensor Microwave Imagers (SSM/I), the Tropical Rainfall Measuring Mission (TRMM/TMI), the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) sensor on the National Aeronautics and Space Administration’s (NASA) Aqua satellite, the WindSAT radiometer, onboard the Coriolis satellite and the soil moisture retrievals from the Advanced Microwave Scanning Radiometer 2 (AMSR2) sensor onboard the Global Change Observation Mission on Water (GCOM-W). The sixth, the microwave radiometer imager (MWRI) onboard China’s Fengyun-3B (FY3B) satellite, is absent in the ECV scheme. Here, the normalized soil moisture products are merged based on their availability within the study period. Evaluation of the merged product demonstrated that the correlations and unbiased root mean square differences were improved over the whole period. Compared to ECV, the merged product from this scheme performed better over dense and sparsely vegetated regions. Additionally, the trends in the parent inputs are preserved in the merged data. Further analysis of FY3B’s contribution to the merging scheme showed that it is as dependable as the widely used AMSR2, as it contributed significantly to the improvements in the merged product.

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“…Accurate characterization of SM is beneficial for applications such as weather and climate modeling, agricultural and water resources management over larger spatial extents [6]. Accurate and timely SM estimation at a relevant spatiotemporal scale is a sound strategy to forecast droughts/floods [7,8] and schedule irrigation [9,10] for the sustainability and productivity of agriculture, particularly in arid and semi-arid regions like the Chinese Loess Plateau (CLP).…”

Section: Introductionmentioning

confidence: 99%

Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau

Yuan

Zhang

et al. 2021

IJGI

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This study aims to integrate multisource data to model the relative soil moisture (RSM) over the Chinese Loess Plateau in 2017 by stepwise multilinear regression (SMLR) in order to improve the spatial coverage of our previously published RSM. First, 34 candidate variables (12 quantitative and 22 dummy variables) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and topographic, soil properties, and meteorological data were preprocessed. Then, SMLR was applied to variables without multicollinearity to select statistically significant (p-value < 0.05) variables. After the accuracy assessment, monthly, seasonal, and annual spatial patterns of RSM were mapped at 500 m resolution and evaluated. The results indicate that there was a high potential of SMLR to model RSM with the desired accuracy (best fit of the model with Pearson’s r = 0.969, root mean square error = 0.761%, and mean absolute error = 0.576%) over the Chinese Loess Plateau. The variables of elevation (0–500 m and 2000–2500 m), precipitation, soil texture of loam, and nighttime land surface temperature can continuously be used in the regression models for all seasons. Including dummy variables improved the model fit both in calibration and validation. Moreover, the SMLR-modeled RSM achieved better spatial coverage than that of the reference RSM for almost all periods. This is a significant finding as the SMLR method supports the use of multisource data to complement and/or replace coarse resolution satellite imagery in the estimation of RSM.

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A Review of Satellite-Derived Soil Moisture and Its Usage for Flood Estimation

Cited by 40 publications

References 175 publications

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

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

Maximizing Temporal Correlations in Long-Term Global Satellite Soil Moisture Data-Merging

Modeling Soil Moisture from Multisource Data by Stepwise Multilinear Regression: An Application to the Chinese Loess Plateau

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