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Soil Moisture Content Retrieval from Remote Sensing Data by Artificial Neural Network Based on Sample Optimization
2022
Sensors
Soil moisture content (SMC) plays an essential role in geoscience research. The SMC can be retrieved using an artificial neural network (ANN) based on remote sensing data. The quantity and quality of samples for ANN training and testing are two critical factors that affect the SMC retrieving results. This study focused on sample optimization in both quantity and quality. On the one hand, a sparse sample exploitation (SSE) method was developed to solve the problem of sample scarcity, resultant
doi:10.3390/s22041611
pmid:35214511
pmcid:PMC8879226
fatcat:cqwljvbn4rfcbirynjstw6vgpm