## 4 Discussion
[PERSON] and [PERSON] [70] argued that the [PERSON]'s Razor philosophy of preferring hypotheses with fewer assumptions should be applied to determining crop LUE in their criticism of relating vapor pressure deficit to LUE. They cited minimal statistical support for a meaningful relationship between these variables. This is supported by our finding that relative humidity was a relatively unimportant predictor of LUE (Figure 5). Varying LUE within season is specific to remote sensing for croplands because crop maturity and type are generally uniform in space and time within fields, contrasting other land types with heterogeneous vegetation type and age [8]. We have demonstrated that approaches that vary crop LUE primarily on environmental stresses may lack relevance to croplands, and productivity modelling approaches should place more importance on within-season variation. Environmental stress-based approaches may remain applicable to ecological applications where a fixed \(\mathrm{LUE_{max}}\) reflects a central value for heterogeneous vegetation and plant growth is more greatly influenced by temperature and soil moisture stress [14; 17]. However, such approaches may also be enhanced by consideration of diffuse radiation [14; 70]. The importance of crop type and predictors relating to within-season variation in the random forest model (Figure 5) show that these variables best reflect the photosynthetic properties of a crop at a given satellite overpass date. The better prediction accuracy, reduced uncertainty, and importance of crop type for Model 1 compared with Model 2 (Table 4 and Figure 5) demonstrate that crop-specific estimates of LUE are greatly preferred. Temporal patterns in GPP and LUE shown in Figure 3 match [PERSON] and [PERSON]'s [26]description of the sigmoidal pattern between LUE and thermal time where LUE reaches a maximum following great variability after emergence, before declining towards zero at the end of the plant growth cycle. The partial dependence plots for LAI, NDVI, and SAVI also show that LUE increases with canopy greenness, corresponding to [PERSON] and [PERSON]'s [15] demonstration of a positive relationship between LUE and green LAI found using the US-Ne flux sites (Table 1). Given their relatively important predictive properties (Figure 5), further investigation into the relationships between NDVI, LAI and LUE across crop growth stages would inform how these indices might be used to better predict LUE. GCVI was the least important of the vegetation indice predictors (Figure 5), despite its hypothetical potential to capture nutrient stress. Figure 6 shows that GCVI values around 0 reduced \(L\tilde{U}E\), which may be due to these values relating to low leaf nitrogen (N) concentration. The relationship between leaf N concentration and LUE has been well described by [PERSON] et al. [71; 72], [PERSON] et al. [73], and [PERSON] [74]. Therefore, it has been hypothesised [21] and demonstrated [27] that the GCVI could be an important predictor of LUE given its ability to capture nutrient deficiencies and reflect the photosynthetic capacity of a canopy. [PERSON] and [PERSON] [21] attributed this ability to their finding that GCVI outperformed NDVI and the Enhanced Vegetation Index (EVI) in prediction of yields at smallholder farms in Kenya. However, [PERSON] and [PERSON] [21] noted that crop nutrient stress is common in those farming systems. Relatively lower variation in nutrient stress among flux tower sites may explain why the indices related to crop maturity and growth were more important predictors of LUE than the GCVI. The fixed values for C3 crops produced relatively accurate estimates of GPP compared with the fixed value for C4 maize. This was especially the case for rice. Maize, as a C4 crop, had higher in situ LUE values for these sites, but not to the degree recommended in the setting of LUE\({}_{\text{max}}\) for PySEBAL and the GYMEE [1; 11; 12]. The median LUE value of approximately 2.0 gC/MJ for maize from in situ sites (Figure 2) is consistent with [PERSON] et al.