Inverse modeling (IM) is a valuable tool in agriculture for estimating model parameters that aid in decision-making. It is particularly useful when parameters cannot be directly measured or easily estimated due to logistical constraints in agricultural settings. Unlike other estimation methods, IM combines a mechanistic model with observations of its outputs to derive the parameters of interest, allowing for the integration of various sources of knowledge. The availability of numerous data sources, such as remote sensing and crowdsourcing, with high spatial and temporal resolution, has expanded the potential of IM in agriculture. Practitioners can now incorporate the spatial and temporal footprint of observational data into parameter estimation. However, common IM techniques currently applied in agriculture often struggle to account for effectively spatial and temporal variability. Relevant IM methods that address these challenges are usually isolated within specific developer and user communities and are not well known within the agricultural community. There is a lack of comprehensive reviews focusing on IM methods suitable for handling spatial and temporal data in agriculture. In parallel, the process of conducting IM in agriculture remains under-formalized. Typically, specific IM methods are chosen for specific combinations of models and types of observational data, but the rationale behind their selection is rarely explained in publications. The relationship between IM methods, models, and observational data is unclear, making it overwhelming for new practitioners to choose an appropriate method. This complex problem, along with the diversity of IM methods, has yet to be adequately addressed while taking into account the specificities of agricultural applications. To address these challenges, this review aims to provide a structured classification of IM methods based on the practical needs of new practitioners in agriculture. It examines a wide range of inversion methods applied in agriculture-related domains and covers four key topics: i) the essential elements and general process of IM, ii) the main families of IM methods in agriculture and their characteristics, iii) the circumstances in which practitioners prefer using IM over other approaches, and their motivations, and iv) practical guidance on choosing a method family based on operational criteria. The review aims to help readers develop a clear understanding of the practice of inverse modeling, gain insights into the diversity of IM methods, and make informed choices when selecting a method family for their agricultural applications.