A review of Remotely Sensed Data Assimilation into Crop Simulation Models

A significant course of action to planning agricultural operations and further maintaining and developing performance on a regional scale involves the accurate and timely estimation of crop yield prior to harvesting using crop growth models. Modeling dynamic changes during crop growth can assist researchers in planning crop management strategies aimed at increasing crop yield. Such models include several parameters that can be calibrated according to the characteristics of the study area. However, insufficent information on location/spatial-wise components or the lack of  thereof in these models along with uncertainties in parameter values may lead to errors in the estimated outputs. In this light, remote sensing data assimilation can be useful for resolving such complications and evaluating the spatial variability of lands, particularly at the regional scale. Remote sensing can estimate values of input parameters for crop growth models such as Leaf Area Index (LAI), fCover, biomass, and soil characteristics. This review paper seeks to introduce and compare different methods of remote sensing data assimilation in crop growth models and examine their advantages and disadvantages. In addition, a literature review conducted in this field can guide the readers in slecting the appropriate crop growth model, relevant remote sensing data assimilation method, and pertinent state/control variables. The literature review indicates that with new sensors and methods in the estimation of remote sensing state/control variables such as LAI and the development and improvement of crop growth models, it is possible to improve the accuracy of crop yield estimation.