The application of soft computing (SC) models for predicting environmental variables is widely gaining popularity, because of their capability to describe complex non-linear processes. The sea surface temperature (SST) is a key quantity in the analysis of sea and ocean systems, due to its relation with water quality, organisms, and hydrological events such as droughts and floods. This paper provides a comprehensive review of the SC model applications for estimating SST over the last two decades. Types of model (based on artificial neural networks, fuzzy logic, or other SC techniques), input variables, data sources, and performance indices are discussed. Existing trends of research in this field are identified, and possible directions for future investigation are suggested.

由于软计算（SC）模型具有描述复杂的非线性过程的能力，因此它们在预测环境变量方面的应用已广受欢迎。由于海面温度与水质，生物和干旱和洪水等水文事件的关系，海面温度（SST）是海洋系统分析中的关键指标。本文对SC模型应用程序进行了全面的回顾，以评估过去20年的SST。讨论了模型类型（基于人工神经网络，模糊逻辑或其他SC技术），输入变量，数据源和性能指标。确定了该领域的现有研究趋势，并提出了未来研究的可能方向。