## 4 Discussion  
### Uncertainty of RS-Based GPP, ET, and eWUE Estimations  
Undoubtedly, the accuracy of RS-based eWUE is tightly related to the performance of individual GPP and ET products. Overall, the PMLv2 product could provide higher-accuracy GPP estimates than the MOD17 product for maize and soybean. From the perspective of model structure, the MOD17 and PMLv2 products adopt the LUE model and the light response curve (i.e., the rectangular hyperbola function), respectively, which are established on different theoretical bases. The LUE model, which assumes the GPP is equal to the product of APAR and LUE, has the advantage of ease of use and few model inputs/parameters [3]. However, some limitations in the LUE model still exist. First, the LUE scheme lacks a solid mechanism explanation in comparison to the process-based model. Second, no consensus has been reached on which formulas of FPAR and environmental scalar are the most effective in the LUE scheme. Third, different environmental stress factors may have interactions among themselves [27]. The abovementioned issues can reduce the accuracy of the LUE model. In addition, the light response curve, which reveals a rectangular hyperbola function between light saturation and photosynthesis [28], has been recognized as one of the reliable methods to partition GPP from net ecosystem exchange (NEE) and simulate GPP [25; 29; 30; 31]. Despite the better performance of the PMLv2 product than the MOD17 product, neither model exhibited sufficiently satisfying accuracy in GPP estimation over cropland, which could further introduce large errors in eWUE estimation. One common weakness of the PMLv2 and MOD17 products is that both two products cannot accurately reflect the distinct photosynthesis capacity between C4 and C3 crop species (see Figure 2), primarily because both two products fail to differentiate C4 and C3 crop species in their model calibration and validation processes [5; 13]. In reality, the carbon assimilation capacity of different crop species (C4 vs. C3) varies largely. A previous manuscript found that the calibrated maximum LUE of C4 maize was significantly larger than that of C3 soybean in the MOD17 model [4]. Moreover, another study revealed that the best-fitted maximum LUE of C4 maize was 1.69-2.63 times higher than that of six other C3 crop species (namely, potato, winter wheat, rape, paddy rice, soybean, and winter barley) in the MOD17 model [32]. However, the current MOD17 model simply assumes that C4 and C3 crop species have the same maximum LUE [5] and thus cannot well monitor the different magnitudes of GPP between C4 maize and C3 soybean. A similar issue can be also found in the PMLv2 product. Moreover, the MOD17 product severely underestimated the observed GPP during the peak of growing seasons for both maize and soybean (see Figure 4), which is consistent with the previous findings [4; 33; 34]. These biases in the MOD17 product predominantly stem from the underestimation of the maximum LUE, which is obtained from a simple Biome-specified Parameters Look-Up Table (BPLUT). A recent study applied the Bayesian inference in conjunction with the Markov chain Monte Carlo approach to re-calibrate the free parameter (including but not limited to the maximum LUE) in the MOD17 model at globally distributed flux sites and further substantiated that the updated BPLUT could benefit the performance of the MOD17 model [33]. In addition, one possible explanation for the better performance of the PMLv2 product for C3 soybean than C4 maize is that the most of the in-situ data at 11 cropland sites used for the model calibration were collected for C3 species, and therefore the PMLv2 product may achieve an overall high retrieval accuracy as much as possible. In comparison to the result of GPP estimation, the PMLv2 and MOD16 products showed more consistent accuracy of ET estimates, whereas the PMLv2 only slightly outperformed the MOD16 product (see Figure 3).