## 3 Challenges  
Figure 4: Scatterplots between WUE and (**a**) VIs (WUE is indirectly estimated by flux observation. NDVI and EVI are calculated by MODIS reflectance products. GPP and ET data at 8-day time scale are averaged from daily observed data of FLUXNET ([[https://fluxnet.org/](https://fluxnet.org/)]([https://fluxnet.org/](https://fluxnet.org/)). Accessed on 15 January 2020)). Three temperate broadleaf forest sites, i.e., IT-Col, US-MMS and US-UMB are selected. The black and red lines represent the linear trend between WUE and EVI, and NDVI, respectively), and (**b**) VPD (The data are based on daily mean values of US-MMS site from 2000–2014. The pink curve indicates the resulted fitting equation, i.e., \(y=a*x^{b+x^{c}}+d\) using daily data for the whole year. Winter and summer respectively represent the non-growth and growth periods, whereas spring and autumn respectively represent the emergence and senescence periods. The phenological information is determined by the rate of change in slope for the fitted curve’s double logistic function to the WUE time series).  
role in methodological innovation in ecosystem ecology [104]. However, remote sensing as a basic data source and improvements in accuracy--including temporal and spatial resolution--are still important issues for remote sensing algorithms and products. An analytical WUE model developed by [PERSON] et al. (2017) also requires long-term continuous and spatially consistent observations on vegetation structural and physiological parameters measured by remote sensing techniques [8]. Although existing empirical and mechanistic models have contributed a lot to the WUE remote sensing, reducing the uncertainty from both methods, input data is still a big challenge.  
Uncertainties and Limitations of the Indirect Estimation of WUE by Remote-Sensed GPP and ET Products  
The MODIS GPP and ET products can indirectly estimate ecosystem WUE at an 8-day time scale with the advantage of upscaling carbon and water cycles, but uncertainties remain due to defects in algorithms [97]. Model parameters are mainly from a simple look-up table and uncertainty in model inputs is the source of uncertainty for MODIS GPP and ET products [105; 106]. A single set of parameters for MODIS-based GPP and ET algorithms cannot exhibit the spatial heterogeneity of physiological characteristics for each biome [75]. Moreover, soil types, atmospheric conditions and changes in vegetation also affect the variability of physiological parameters [107], which can lead to larger biases in GPP and ET [108]. A parameter optimization method is used to calibrate and reduce uncertainty for the carbon cycle models of MODIS to improve the performance [109]. Those uncertainties are indicated through comparative studies, which show a poor performance of ecosystem WUE estimated by MODIS products indirectly (Figure 5) [86; 110]. Therefore, an evaluation and validation of MODIS-based WUE are needed to improve the algorithm and to have new reliable remote sensing products across space and time. More precise structural and physiological parameters of vegetation through remotely sensed data are also needed to improve WUE estimation.