Assimilation of remote sensing into crop growth models: Current status and perspectives

TLDR

Timely monitoring of crop lands is essential for sustainable agriculture and food security, and while Earth Observation data offers multi‑scale monitoring, its limitations are offset by crop growth models that simulate physiological development, with data assimilation combining the strengths of both approaches. This review critiques the strengths and weaknesses of Earth Observation data and crop growth models and outlines future research directions that position data assimilation as a key enabler for integrating diverse observations and advancing crop modeling. The authors present a robust data‑assimilation framework based on Bayes’ rule, deriving various methods from differing assumptions about the posterior probability density function, following a critical assessment of EO data and crop growth models. The framework yields recommendations for selecting appropriate data‑assimilation methods, highlights computational challenges in scaling to large spatial domains, and is illustrated with numerous literature examples.

Abstract

Timely monitoring of crop lands is important in order to make agricultural activities more sustainable, as well as ensuring food security. The use of Earth Observation (EO) data allows crop monitoring at a range of spatial scales, but can be hampered by limitations in the data. Crop growth modelling, on the other hand, can be used to simulate the physiological processes that result in crop development. Data assimilation (DA) provides a way of blending the monitoring properties of EO data with the predictive and explanatory abilities of crop growth models. In this paper, we first provide a critique of both the advantages and disadvantages of both EO data and crop growth models. We use this to introduce a solid and robust framework for DA, where different DA methods are shown to be derived from taking different assumptions in solving for the a posteriori probability density function (pdf) using Bayes' rule. This treatment allows us to provide some recommendation on the choice of DA method for particular applications. We comment on current computational challenges in scaling DA applications to large spatial scales. Future areas of research are sketched, with an emphasis on DA as an enabler for blending different observations, as well as facilitating different approaches to crop growth models. We have illustrated this review with a large number of examples from the literature.