Using Artificial Neural Network Ensembles With Crogging Resampling Technique to Retrieve Sea Surface Temperature From HY-2A Scanning Microwave Radiometer Data

The brightness temperature data acquired during 2012–2015 from the scanning microwave radiometer (SMR), onboard the first Chinese ocean dynamic environment satellite—Haiyang-2A, were matched up with the WindSat Polarimetric Radiometer (WindSat) $0.25^{\circ }\,\,\times0.25^{\circ }$ gridded daily sea surface temperature (SST) data. Then, the artificial neural network (ANN) ensemble (ANNE) method implementing the Crogging technique was used to build the SMR SST retrieval algorithm. Different from a regular ANN, an ANNE combines the outputs of its ANN members to generate an algorithm. The developed ANNE algorithm for SMR SST was validated based on the SMR/WindSat data pairs that were not used in the tuning of the algorithm. The SST comparison shows the root mean square (rms) of 1.16 °C for the ANNE algorithm. We further validate the SMR SST products using the in situ measurements from the National Oceanic and Atmospheric Administration iQuam System. The rms of the ANNE algorithm in comparison with the global iQuam SSTs is 1.46 °C. All validations showed that ANNEs were more accurate than the other statistically based SST retrieval algorithms for SMR, and generally had much smaller uncertainties than regular ANNs.