###### Abstract
However, fixed \(\text{LUE}_{\text{max}}\) measured at the field or plant levels may be much greater than \(\text{LUE}_{\text{max}}\) applicable to remote sensing scales. Calculating in situ \(\text{LUE}_{\text{max}}\) from flux tower GPP measurements has revealed overestimation of \(\text{LUE}_{\text{max}}\) for croplands and therefore overestimation of remotely sensed GPP. For example, [PERSON] et al. [9] calculated \(\text{LUE}_{\text{max}}\) values ranging from approximately 2.3 to 3.7 gC/MJ/day for maize and 1.4 gC/MJ/day for wheat from flux tower sites. Similarly, [PERSON] et al. [24] calculated mean values of 2.78 and 1.64 gC/MJ/day for maize and soybean, respectively. These values are generally greater than estimates of \(\text{LUE}_{\text{max}}\) for global or continental products such as the Moderate-Resolution Imaging Spectroradiometer (MODIS) GPP, but less than values generated in field experiments. This has led to an underestimation of cropland GPP in continental products and overestimation of GPP when fixed \(\text{LUE}_{\text{max}}\) values are used in cropland-specific applications [7; 24; 25]. In addition to overestimation of \(\text{LUE}_{\text{max}}\), there are two main reasons why CASA with fixed \(\text{LUE}_{\text{max}}\) may not adequately quantify LUE for cropland. The first is that neither a fixed coefficient for \(\text{LUE}_{\text{max}}\) nor the stress scalars (Equation (2)) account for within-season variation in LUE, which has been demonstrated for a range of field crops. For example, [PERSON] and [PERSON] [15] show that LUE varied within maize- and soybean-growing seasons, both before (\(\text{LUE}_{\text{inc}}\)) and after (\(\text{LUE}_{\text{total}}\), referred to as LUE in this paper) fPAR was accounted for, and [PERSON] and [PERSON] [26] show a similar temporal pattern for field peas. [PERSON] et al. [16] recognised the two to three-fold variation in LUE within growing seasons for maize and soybean meant this source of variation \"should be accounted for in LUE models\". The second reason that CASA may not apply well to croplands is that stress scalars may be less relevant in managed environments, especially irrigated croplands, than in other vegetated land types. [PERSON] et al. [8] found that omitting stress scalars from CASA (Equation (2), adapted for NPP) improved NPP estimation accuracy for irrigated and temperature-resistant crops. Therefore, an alternative approach to setting the LUE variable for cropland productivity estimations based primarily on within-season variation in crop growth may be superior to the CASA method. In this paper, we investigate whether a non-parametric, machine learning, predictive model for LUE based on satellite-derived vegetation indices and agroclimatic variables can give more accurate GPP predictions for cropland than CASA. This approach differs from previous studies in its non-parametric approach, and specificity and scalability to croplands. Relationships between vegetation indices and both LUE and GPP for cropland have been investigated by [PERSON] et al. [27], and [PERSON] and [PERSON] [28], though these have generally focused on parametric modelling approaches relating GPP or LUE to individual indices. Non-parametric random forest approaches have been applied to prediction of LUE at the global scale by [PERSON] et al. [29] based predominantly on meteorological and land use/land cover predictors. Our approach is unique in its use of both agroclimatic and vegetation indices as predictor variables that have previously been investigated separately, its specificity to cropland and crop type, and its scalability due to reliance on globally-available Landsat and climate data. To this end, flux tower data were used to derive \(\mathrm{GPP_{in\ situ}}\) and \(\mathrm{LUE_{in\ situ}}\), and demonstrate how these varied within a crop season and between crop type. The in situ values were compared with satellite-based estimates of \(\mathrm{LUE_{CASA}}\), and \(\mathrm{GPP_{CASA}}\). \(\mathrm{LUE_{in