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Found 6 papers, 0 papers with code
In-Season Crop Progress in Unsurveyed Regions using Networks Trained on Synthetic Data
Net F1 scores across all crop progress stages increased by 8. 7% when trained on a combination of surveyed region and synthetic data, and overall performance was only 21% lower than when the NN was trained on surveyed data and applied in the US Midwest.
Domain-guided Machine Learning for Remotely Sensed In-Season Crop Growth Estimation
Results from this study exhibit both the viability of NNs in crop growth stage estimation (CGSE) and the benefits of using domain knowledge.
Spatial Scaling of Satellite Soil Moisture using Temporal Correlations and Ensemble Learning
A novel algorithm is developed to downscale soil moisture (SM), obtained at satellite scales of 10-40 km by utilizing its temporal correlations to historical auxiliary data at finer scales.
Disaggregation of SMAP L3 Brightness Temperatures to 9km using Kernel Machines
In this study, a machine learning algorithm is used for disaggregation of SMAP brightness temperatures (T$_{\textrm{B}}$) from 36km to 9km.
Disaggregation of Remotely Sensed Soil Moisture in Heterogeneous Landscapes using Holistic Structure based Models
The KLD of the disaggregated estimates generated by the SRRM is at least four orders of magnitude lower than those for the PRI disaggregated estimates, while the computational time needed was reduced by three times.
Downscaling Microwave Brightness Temperatures Using Self Regularized Regressive Models
A novel algorithm is proposed to downscale microwave brightness temperatures ($\mathrm{T_B}$), at scales of 10-40 km such as those from the Soil Moisture Active Passive mission to a resolution meaningful for hydrological and agricultural applications.
