Assimilation of remote sensing data and crop simulation models for agricultural study: Recent advances and future directions

Abstract

Crop simulation models (CSMs) estimate crop production, water and nitrogen balances, and carbon dynamics through a deterministic scheme with input data such as field and climate conditions, crop characteristics, and management practice. Many parameters and input variables, required by crop models but usually poorly known, can be supplied through the remote sensing technique. In the past decades, integration of remote sensing data and CSMs has gained increasing attention in both scientific communities and agricultural practice. This chapter starts with a general introduction of the procedure to assimilate remote sensing data into CSMs, followed by a brief description of the Decision Support System for Agrotechnology Transfer (DSSAT) model (Hoogenboom et al., 1999, 2004). Several assimilation methods of various degrees of complexity and integration are described, including the direct input, sequential, and variational assimilation techniques. Remote sensing data in solar, microwave, and thermal domains are presented for their capabilities in assimilating with crop growth models. Regional application examples are presented to illustrate the procedure of sensitivity study, cost function construction, crop yield estimation, and the hydrological simulation process. Challenges facing both remote sensing data and CSMs are discussed and some recommendations about the future research priorities are proposed at the end.