12/26/2023 0 Comments Manyland dynamic![]() ![]() These passive T b observations are suitable for detecting surface soil moisture and FT cycles over long time series and from regional to global scales (Zhang et al. The thresholds of the 37 GHz T b and the spectral gradient between 18/19 GHz and 37 GHz T b to distinguish the FT states can be determined through statistical analysis of the samples. 2003a Zhang and Armstrong 2001 Zuerndorfer and England 1992). 2017 McFarland, Miller, and Neale 1990 Zhang et al. A large number of experimental studies have shown that frozen soil has a low T b of 37 GHz, and the gradient between low-frequency brightness temperature and high-frequency brightness temperature (spectral gradient) of frozen soil is negative because of the darkening of the bulk scattering, while the opposite is true for thawed soil (England, Galantowicz, and Zuerndorfer 1991 Jin, Li, and Che 2009 Kim et al. Leveraging the launches of the Scanning Multichannel Microwave Radiometer (SMMR operational from 1978 to 1987), the Special Sensor Microwave Imager (SSM/I operational from 1987 to the present), the Advanced Microwave Scanning Radiometer-EOS (AMSR-E operational from 2002 to 2011), and the Special Sensor Microwave Imager/Sounder (SSMIS operational from 2000 to the present) among other satellite missions, we have obtained continuous records of global passive microwave brightness temperatures (T b) for over 40 years. These traditional methods are time-consuming and usually display considerable uncertainty. Prior to the satellite era, traditional methods for determining the surface soil FT state relied on numerical simulations extrapolated from a point to a larger area using limited ground measurement sources. Monitoring the FT state is critical for studying the Earth’s hydrologic and surface temperature patterns and dynamics (Wang et al. Recently, changes in FT processes have caused increasing nitrogen emissions in permafrost regions (Schuur, Abbott, and Network 2011 Wang et al. 2012) and net ecosystem carbon exchange (Zhang, Armstrong, and Smith 2003b). The number of thawed days directly affects the interannual variability and spatial distribution of net primary productivity (Kim et al. ![]() Surface FT cycles also have a substantial impact on ecosystem function. Surface FT processes significantly affect land hydrology, crop growth, carbon dynamics, and the energy exchange between the Earth and the atmosphere (Zhang and Armstrong 2001). Changes in the timing, spatial extent, and duration of surface soil freeze/thaw (FT) are sensitive to climate change and can serve as an climate change indicator (Zhang, Armstrong, and Smith 2003b). This study demonstrates that the freeze/thaw dynamic ensemble selection algorithm can provide daily estimates of surface FT states across China, improve FT states’ retrieval accuracy, and provide a valuable multi-decadal record for daily FT states.Īpproximately 60% of near-surface soils worldwide experience seasonal phase transitions between water and ice every year (Kim et al. The evaluations further indicate that some of the existing algorithms do not reflect the temporal and spatial heterogeneity in selecting thresholds for FT classification. The mean classification accuracy for the PM and AM overpasses of FT-DESA is 89% and 84%, respectively. Our results show that FT-DESA has the highest retrieval accuracy and the lowest biases across China among the four algorithms. We then evaluated our FT-DESA results by comparing the observations of 2398 stations and three other existing FT algorithms, including the modified seasonal threshold algorithm (MSTA), decision tree algorithm (DTA), and dual-index algorithm (DIA) across China. We applied our developed freeze/thaw dynamic ensemble selection retrieval algorithm (FT-DESA) to retrieve China’s daily surface FT states from 2009 to 2020 based on multiband Special Sensor Microwave Imager/Sounder (SSMIS) brightness temperatures. This algorithm can optimally integrate three machine learning models on a grid cell scale, namely Random Forests, Extra-Trees, and Extreme Gradient Boosting. ![]() This study aims to improve FT retrieval accuracy by developing a new FT retrieval algorithm that applies the K-Nearest Oracle Union (KNORA-UNION) dynamic ensemble selection algorithm. The surface soil freeze/thaw (FT) cycle serves as a “switch” for land surface processes accurate retrieval of surface FT dynamics based on satellite passive microwave remote sensing is critical for studies on climate change and dynamics of the cryosphere.
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