## 3 Results
### Scientometrics Analysis  
CiteSpace software v.6.2.R4 Basic has the ability to provide citation analytics of references based on keywords, subject, terms and abstracts. Although meta-analysis is specific to the criteria defined in this SLR considering PRISMA guidelines, scientometrics provides detailed information relevant to citations based on selected databases like Web of Science (WoS), Scopus and a few others. For example, the systematic review in this article covered the characteristics described in the included publications without any consideration of the citations of selected articles, but scientometrics is mainly concerned with citations.  
Among the screened 91 articles, WoS refined articles were 87 with citations of 2832. WoS did not include publications with no index. There were four Not Indexed [56; 63; 67; 73] articles among the selected articles that were not included in the scientometrics analysis. Web of Science accepted 87 articles for the Scientometrics analysis text list for use in CiteSpace Software. As per WoS analysis, year-wise publications and citation trends are given in the graph as Figure 14. The graph shows that citations have seen an increasing trend since 2015 (except for 2023, due to the short timeline ending in June 2023). This also indicates the dynamic interest of researchers in recent years to work on RTM applications  
Figure 13: Graph showing crops used for RTM-based crop traits retrieval.  
in CYP and crop traits retrieval. Thus, the selection of a timeline from 2015 to June 2023 was appropriate, as evident from citation growth trend, for comprehensive knowledge sharing during this SLR.  
Scientometrics analysis was conducted for document co-citations of sorted (87) articles with an unlimited look-back years condition. The rest of the default settings were followed for the analysis. The analysis resulted in merged network nodes of 292 for 1121 links, describing an average link amount of 3.94 per node. Document co-citation clustering analysis based on keywords using CiteSpace is shown in Figure 14. Among clusters, agronomic variables and RTM inversion models were most relevant, followed by Sentinel-2 and winter wheat keywords cited in the document co-citations. The document co-citation analysis also covered the key theme of RTMs described in this SLR. Moreover, this analysis found four references with the strongest citation bursts, with details given in Figure 15.  
Country analytics of the document co-citations showed that China was the largest node with 38 counts, followed by the USA with a count of 18. China document co-citations are linked to England, Italy and France, whereas the USA is mainly linked to the Netherlands and a few others, as shown in Figure 16.  
In our study, we employed CiteSpace v.6.2.R4 Basic to analyze the scholarly land-scape from 2015 to 2023. The analysis, conducted on 14 August 2023, revealed a network of 47 nodes and 163 edges, with a density of 0.1508. The 30 largest connected components covered 72% of the network, and 1.0% of nodes were labeled. The selection criteria included Gindex (k = 25) and LRF = \(-\)1.0, among others. The pruning method applied was Pathfinder, resulting in a modularity (Q) of 0.4127 and a weighted mean silhouette (S) of 0.9982. The harmonic mean (Q, S) was calculated as 0.584.  
Figure 14: Year-wise publications and citation trend (WoS).  
Figure 16: Country wise publication Cluster Nodes and Links.  
Figure 15: Co-citation analysis [42] based on keywords using CiteSpace.