## 4 Discussion
### Findings, Challenges, and Research Scope  
Following future research intentions, along with knowledge gaps, are suggested for researchers based on the findings of this SLR. This will provide a way forward to improve the scope and accuracy of RTM-based CYP and crop traits retrieval.  
* During this SLR, it was identified that there is a lack of consistent research for RTM-based crop yield modeling. Repeated research will improve RTMs and pave the way for more reliability and consistency for decision making.
* Most CGMs, like WOFOST, DSSAT, etc., have only LAI as a crop trait for integration with RTMs for CYP due to coupling problems. The addition of more crop traits along with efficient and accurate coupling techniques could provide scope for reliable CYP and further applications of CGM and RTM integration to assist in smart, precision agriculture.
* These limited parameters, mostly LAI and ground measurements, hinder model validation, particularly of canopy structure, leaf, and soil properties. Robust and efficient technique development will improve model validation.
* Among the available popular CGMs, APSIM is the only model that can provide a high number of crop traits, such as LAI, chlorophyll ab, leaf dry matter (Cm) and leaf water content (Cw). These crop traits can be integrated with RTMs for CYP. Currently, within the criteria of this SLR, two research studies [51; 52] have used APSIM for CYP. These research studies used RFD instead of crop traits for CYP. A crop trait simulation through APSIM used as an input for an RTM can provide a straightforward specific solution to the problem of optimal solution search from the inversion techniques like LUT. No research is currently available for APSIM and RTM integration-based CYP, an area with the potential to provide viable solutions.
* Most RTM-based applications in agriculture are specific to cereals (wheat, rice and maize). Many crops are still not investigated for RTM-based estimation for crop traits and CYP. Cotton and sugarcane are cash crops, but still have no RTM-based CYP studies. This may be due to the limitations of models. Developing crop-specific or crop group-specific RTM can be very useful, robust and efficient for reliable CYP.
* Research studies are mostly confined to a few countries. In Asia, China is the major country working on RTM applications for agriculture. Other countries like Russia, India, Pakistan and Bangladesh should also be included for RTM applications in agriculture.
* The outlier of R\({}^{2}\) = 0.11 in the study [47] for yield estimation was due to calibration against a few pixels for optimal parameter identification. Parcel-based calibration of RTM-based CYP is another area for further investigation.
* Spatial heterogeneity among fields due to crop density variations is another area not addressed by RTMs. RTM applications for parcel-based calibrations to account for crop density variations can improve RTM efficiency for CYP.
* RTMs have the limitation of not catering to the narrow variations in datasets, as in the case of [73], for retrieval of Cw (R\({}^{2}\) = 0.30). RTMs should respond to narrow variations, as crops with narrow growth variations have different yields.
* Crop phenology development resulted in variations of crop traits dynamics. Capturing these crop traits dynamics over time is challenging but has a key role in enhancing model accuracy for CYP.
* New computational techniques like machine and deep learning have the potential to improve CYP. This, however, needs training datasets and ground information for model training. RTM integration with CGM outputs with state-of-the-art computational techniques [128] is another research focus area. The same approach could be effective in CYP subjected to research investigations in this field.