  In the realm of Earth science, effective cloud property retrieval,
encompassing cloud masking, cloud phase classification, and cloud optical
thickness (COT) prediction, remains pivotal. Traditional methodologies
necessitate distinct models for each sensor instrument due to their unique
spectral characteristics. Recent strides in Earth Science research have
embraced machine learning and deep learning techniques to extract features from
satellite datasets' spectral observations. However, prevailing approaches lack
novel architectures accounting for hierarchical relationships among retrieval
tasks. Moreover, considering the spectral diversity among existing sensors, the
development of models with robust generalization capabilities over different
sensor datasets is imperative. Surprisingly, there is a dearth of methodologies
addressing the selection of an optimal model for diverse datasets. In response,
this paper introduces MT-HCCAR, an end-to-end deep learning model employing
multi-task learning to simultaneously tackle cloud masking, cloud phase
retrieval (classification tasks), and COT prediction (a regression task). The
MT-HCCAR integrates a hierarchical classification network (HC) and a
classification-assisted attention-based regression network (CAR), enhancing
precision and robustness in cloud labeling and COT prediction. Additionally, a
comprehensive model selection method rooted in K-fold cross-validation, one
standard error rule, and two introduced performance scores is proposed to
select the optimal model over three simulated satellite datasets OCI, VIIRS,
and ABI. The experiments comparing MT-HCCAR with baseline methods, the ablation
studies, and the model selection affirm the superiority and the generalization
capabilities of MT-HCCAR.
