Journal ArticleDOI
The Soil Moisture Active Passive Experiments: Validation of the SMAP Products in Australia
Nan Ye,Jeffrey P. Walker,Xiaoling Wu,Richard de Jeu,Ying Gao,Thomas J. Jackson,François Jonard,Edward J. Kim,Olivier Merlin,Valentijn R. N. Pauwels,Luigi J. Renzullo,Christoph Rudiger,Sabah Sabaghy,Christian von Hebel,Simon Yueh,Liujun Zhu +15 more
TLDR
The SMAP radar, radiometer, and derived SM showed a high agreement with the SMAPEx-4 and -5 data set, with a root-mean-squared error (RMSE) of ~3 K for radiometer brightness temperature, and an RMSE of $\sim 0.05~\text m3/m3/\text{m}^{3}/£3$ for the radiometer-only SM product.Abstract:
The fourth and fifth Soil Moisture Active Passive Experiments (SMAPEx-4 and -5) were conducted at the beginning of the SMAP operational phase, May and September 2015, to: 1) evaluate the SMAP microwave observations and derived soil moisture (SM) products and 2) intercompare with the Soil Moisture and Ocean Salinity (SMOS) and Aquarius missions over the Murrumbidgee River Catchment in the southeast of Australia. Airborne radar and radiometer observations at the same microwave frequencies as SMAP were collected over SMAP footprints/grids concurrent with its overpass. In addition, intensive ground sampling of SM, vegetation water content, and surface roughness was carried out, primarily for validation of airborne SM retrieval over six $\sim 3\,\,\text {km} \times 3$ km focus areas. In this study, the SMAPEx-4 and -5 data sets were used as independent reference for extensively evaluating the brightness temperature and SM products of SMAP, and intercompared with SMOS and Aquarius under a wide range of SM and vegetation conditions. Importantly, this is the only extensive airborne field campaign that collected data while the SMAP radar was still operational. The SMAP radar, radiometer, and derived SM showed a high agreement with the SMAPEx-4 and -5 data set, with a root-mean-squared error (RMSE) of ~3 K for radiometer brightness temperature, and an RMSE of $\sim 0.05~\text{m}^{3}/\text{m}^{3}$ for the radiometer-only SM product. The SMAP radar backscatter had an RMSE of 3.4 dB, while the retrieved SM had an RMSE of 0.11 m3/m3 when compared with the SMAPEx-4 data set.read more
Citations
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Journal ArticleDOI
Ground, Proximal, and Satellite Remote Sensing of Soil Moisture
Ebrahim Babaeian,Morteza Sadeghi,Scott B. Jones,Carsten Montzka,Harry Vereecken,Markus Tuller +5 more
TL;DR: Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society.
Journal ArticleDOI
Version 4 of the SMAP Level-4 Soil Moisture algorithm and data product
Rolf H. Reichle,Qing Liu,Randal D. Koster,Wade T. Crow,Gabrielle De Lannoy,John S. Kimball,Joseph V. Ardizzone,David D. Bosch,Andreas Colliander,Michael H. Cosh,Jana Kolassa,Jana Kolassa,Sarith Mahanama,John H. Prueger,Patrick J. Starks,Jeffrey P. Walker +15 more
TL;DR: For example, Reichle et al. as mentioned in this paper presented an overview of recent updates in the Level-4 Soil Moisture Active Passive (L4_SM) algorithm and an assessment of the quality of the resulting Version-4 soil moisture estimates.
Development and Initial Assessment of the SMAP Passive Soil Moisture Product
Rajat Bindlish,Steven Chan,P. E. O'Neill,T. J. Jackson,E. G. Njoku,A. Colliander,M. H. Cosh,Fan Chen,W. T. Crow +8 more
TL;DR: Initial in situ comparisons conducted at a limited number of core validation sites (CVSs) and several hundred sparse network points indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics.
Journal ArticleDOI
Assessment of 24 soil moisture datasets using a new in situ network in the Shandian River Basin of China
Tianjie Zhao,Haishen Lü,Jiancheng Shi,Michael H. Cosh,Dabin Ji,Lingmei Jiang,Qian Cui,Huiguo Lu,Kun Yang,Jean-Pierre Wigneron,Xiaojun Li,Yonghua Zhu,Lu Hu,Zhiqing Peng,Ye-long Zeng,Xiaoyi Wang,Chuen Siang Kang +16 more
TL;DR: In this article , triple collocation analysis (TCA) was applied to all possible triplets to verify the reliability and robustness of the results, including local acquisition time, physical surface temperature, and vegetation optical depth (VOD).
Journal ArticleDOI
Estimating catchment scale soil moisture at a high spatial resolution: Integrating remote sensing and machine learning
TL;DR: In this paper, a regression tree (RT), an Artificial Neural Network (ANN), and a Gaussian Process Regression (GPR) model based on the soil thermal inertia theory over a semi-arid agricultural landscape in Australia was used to estimate near-surface soil moisture at a high spatial resolution.
References
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Journal ArticleDOI
The Soil Moisture Active Passive (SMAP) Mission
Dara Entekhabi,Eni G. Njoku,Peggy O'Neill,Kent Kellogg,Wade T. Crow,W. Edelstein,Jared Entin,Shawn D Goodman,Thomas J. Jackson,Joel T. Johnson,John S. Kimball,Jeffrey R. Piepmeier,Randal D. Koster,Neil R.W. Martin,Kyle C. McDonald,Mahta Moghaddam,Susan Moran,Rolf H. Reichle,Jiancheng Shi,Michael W. Spencer,Samuel W Thurman,Leung Tsang,Jakob van Zyl +22 more
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Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals
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TL;DR: In this article, the retrieval characteristics of passive (AMSR-E) and active (ASCAT) microwave satellite estimates are combined to produce an improved soil moisture product. But the results of the satellite-based passive and active microwave sensors have the potential to offer improved estimates of surface soil moisture at global scale.
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