Abstract: Quantitative remote sensing inversion aims to estimate surface parameters using satellite observations, which play a crucial role in environmental monitoring and resource assessment. This process involves pixel-level estimation of continuous variables that describe surface structure, function, and state, offering high spatial precision essential for accurate enviro nmental analysis. However, traditional quantitative inversion algorithms often assume “consistent distribution” between the training and application domains and are heavily reliant on large, representative datasets. As a result, model generalization performance can degrade when faced with spatiotemporal distribution shifts or data scarcity. Transfer learning, an approach that involves transferring knowledge from a source domain to a target domain, offers a promising solution to this problem. This method has gained increasing popularity in the field of quantitative remote sensing as it enables models to adapt to new domains with li mited data by leveraging knowledge from similar domains. First, a systematic review of transfer learning applications in quantitative remote sensing inversion is provided, starting with an explanation of the basic concepts of transfer learning. The review cla ssifies and summarizes three main types of distribution shifts: covariate shift, prior shift, and concept shift, and discusses how th ese shifts manifest in remote sensing. This offers a comprehensive understanding of the challenges and solutions associated with applying transfer learning in this field. Second, a detailed classification and summary of transfer learning solution strateg ies are provided from a methodological perspective. Third, a literature analysis evaluates the use of transfer learning across various remote sensing domains, including vegetation, aquatic environments, soils, and the atmosphere. The review emphasizes the effectiveness of transfer learning in addressing issues such as data scarcity and domain adaptation, providing valuable insig hts into the practical challenges and successes encountered. Finally, future trends in transfer learning technology are explored, with suggestions for ongoing research and development to further enhance its applicability in remote sensing tasks.