scispace - formally typeset
Journal ArticleDOI

The Soil Moisture Active Passive Experiments: Validation of the SMAP Products in Australia

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
More filters
Journal ArticleDOI

Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

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.

Development and Initial Assessment of the SMAP Passive Soil Moisture Product

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

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
More filters
Journal ArticleDOI

The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle

TL;DR: The SMOS satellite was launched successfully on November 2, 2009, and will achieve an unprecedented maximum spatial resolution of 50 km at L-band over land (43 km on average over the field of view), providing multiangular dual polarized (or fully polarized) brightness temperatures over the globe.
Journal ArticleDOI

Modeling the Exchanges of Energy, Water, and Carbon Between Continents and the Atmosphere

TL;DR: Modern schemes incorporate biogeochemical and ecological knowledge and, when coupled with advanced climate and ocean models, will be capable of modeling the biological and physical responses of the Earth system to global change, for example, increasing atmospheric carbon dioxide.
Journal ArticleDOI

High-quality spatial climate data-sets for Australia

TL;DR: In this article, high-quality national climate information is provided to place these climatedriven events in a proper historical perspective and to provide a context for understanding the associated impacts on humans and the environment.
Journal ArticleDOI

Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals

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.
Related Papers (5)