## 3 Results
### Crop Traits Retrieval  
Crop traits are very important parameters, as not only does crop productivity depend on it but increased crop yield is also linked to the selection of the best crop traits for breeding technologies and crop management measures that target sustainable increases in total productive potential [55]. As such, reliable estimation of crop traits will not only aid in reliable yield prediction, but also contribute towards technology-oriented decision-making for smart and precision agriculture. It was, however, observed that despite significant research articles (76), there is little utilization of these traits towards decision-making to benefit crop field operations. Increasingly, research studies towards reliable estimation have the potential to be utilized in crop field operations not only for CYM but also to promote smart/precision agriculture [56].  
During this SLR, a total of 76 papers were found that described RTMs used for crop traits retrieval. Different models used for crop traits retrieval were classified into five categories of PROSAIL (53) including all types of PROSAIL PRO, D or different inversion methods [17; 20; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81; 82; 83; 84; 85; 86; 87; 88; 89; 90; 91; 92; 93; 94; 95; 96; 97; 98; 99; 100; 101; 102; 103; 104; 105; 106; 107; 108; 109; 110; 111; 112; 113], PROCOSINE (4) [114; 115; 116; 117; 118; 119; 120; 121], SCOPE (5) [117; 118; 119; 120; 121] and Others (7) [40; 122; 123; 124; 125; 126; 127]. The Community Microwave Emission Model (CMEM), Polynomial Chaos Expansion (PCE) MetaModel, SunScan (SS1), Fluorescence Correction Vegetation Index (FCVI), REGularized canopy reFLECTance (REGFLEC) and Fluspect-CX + SAIL (Flusail) were a few of the relevant RTMs grouped under Others.  
PROSAIL was the most prevalent RTM, featuring in more than 70 percent of crop traits studies, followed by PROSPECT alone and SCOPE. The most frequent use of the PROSAIL model can be attributed to its ease of use, robustness and consistent validation through several experiments in lab, field and space [10]. Crop traits retrieved through the use of various RTMs were classified into five categories of LAI, chlorophyll, biomass, water content, and others. All traits relevant to chlorophyll like chlorophyll a, b (Cab), leaf chlorophyll content (LCC), canopy chlorophyll content (CCC), etc., were grouped under chlorophyll, while biomass-relevant traits like above ground biomass (AGB) (wet or dry), leaf mass per area (LMA), gross primary productivity (GPP), net primary productivity (NPP), etc., were grouped under biomass. Similarly, equivalent water thickness (EWT), water content (Cw), crop canopy water content (CCWC), etc., were grouped under water content. Remaining traits like carotenoid content (Car), brown pigment (Cb), soil moisture, diseases, average leaf inclination angle (ALIA), etc., were grouped under others. LAI (71% of total studies) and chlorophyll (76% of total studies) were the most-investigated crop traits. These investigations were motivated by the crucial importance of these traits in describing crop health, directly linked to crop yield.  
Figure 6: RTM-based crop trait characteristics. (**a**) RTMs used; (**b**) crop trait retrieval; (**c**) crops investigated; (**d**) countries of study.  
Figure 7: Summary flow chart of RTM-based retrieved crop traits characteristics.