A Systematic Review of Radiative Transfer Models for Crop Yield Prediction and Crop Traits Retrieval

Abstract

Radiative transfer models (RTMs) provide reliable information about crop yield and traits with high resource efficiency. In this study, we have conducted a systematic literature review (SLR) to fill the gaps in the overall insight of RTM-based crop yield prediction (CYP) and crop traits retrieval. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 76 articles were found to be relevant to crop traits retrieval and 15 for CYP. China had the highest number of RTM applications (33), followed by the USA (13). Crop-wise, cereals, and traits-wise, leaf area index (LAI) and chlorophyll, had a high number of research studies. Among RTMs, the PROSAIL model had the highest number of articles (62), followed by SCOPE (6) with PROSAIL accuracy for CYP (median R2 = 0.62) and crop traits (median R2 = 0.80). The same was true for crop traits retrieval with LAI (CYP median R2 = 0.62 and traits median R2 = 0.85), followed by chlorophyll (crop traits median R2 = 0.70). Document co-citation analysis also found the relevancy of selected articles within the theme of this SLR. This SLR not only focuses on information about the accuracy and reliability of RTMs but also provides comprehensive insight towards understanding RTM applications for crop yield and traits, further exploring possibilities of new endeavors in agriculture, particularly crop yield modeling.