# Linking Remotely Sensed Carbon and Water Use Efficiencies with In Situ Soil Properties
## 5 Conclusions  
CUE and WUE are key physiological parameters linking carbon and water cycles. Understanding changes in these efficiencies is critical to quantify future terrestrial ecosystem responses to climate change. Here, we presented a different approach to understand the stand-level soil-vegetation interactions by using remotely sensed data. Data from the 19 sites representative of the major land cover types occurring in a warm-temperate and humid climate were used to investigate the capability of satellite data to derive meaningful information about the soil-vegetation interactions. The results revealed significant relationships between satellite-derived CUE and WUE and measured soil chemical properties. The CART model identified soil variables that were important to predict CUE and WUE, implying that satellite data have the potential for elucidating soil-vegetation interactions.  
We have learned from this study that satellite spatial resolution was not important for more accurate detection of relationships between satellite vegetation products and soil chemical properties. Interestingly, coarser-resolution MODIS data produce the best estimates for CUE than high-resolution remote sensing products (e.g., Landsat CUE). Our study is limited by the number of sites used and the reported relationships may differ with more data or additional biomes or soil types. Nevertheless, our study represents a new approach that can be used to better understand the observed spatial variability in satellite-derived CUE and WUE data and can be used to inform land surface models about missing processes that are essential for more accurate predictions of vegetation CUE and WUE [86].  
The following are available online at [[https://www.mdpi.com/article/10.3390/rs13132593/s1](https://www.mdpi.com/article/10.3390/rs13132593/s1)]([https://www.mdpi.com/article/10.3390/rs13132593/s1](https://www.mdpi.com/article/10.3390/rs13132593/s1)). Figures S1-S5: Variable importance ranking based on the total reduction in mean square error (MSE) for calibrated CART CUE and WUE models, Figure S6: variability of soil carbon isotope and organic matter with soil depth, Figure S7: regression analysis for the relationship between MODIS GPP and GOSIF GPP for our study sites. Figure S8: Spatial image of WUE from ECOSTRESS. and supplementary data showing the [PERSON]'s HSD test for our study sites.  
[PERSON] and [PERSON] conceived and designed the research; [PERSON], [PERSON] and [PERSON] performed the research and analyzed and interpreted the data; [PERSON] collected the data and performed lab analysis; [PERSON] was responsible for the research analysis; [PERSON] produced the ECOSTRESS data; [PERSON] contributed the GOSIF GPP data; [PERSON], [PERSON] and [PERSON] contributed to the writing of the manuscript; [PERSON], [PERSON] and [PERSON] reviewed and revised the manuscript. All authors have read and agreed to the published version of the manuscript.  
**Funding:** The material is based upon work supported by NASA Kentucky under NASA award No: NNX15 AK28A. J.X. was supported by NASA's Climate Indicators and Data Products for Future National Climate Assessments (award No: NNX16 AG61G).  
**Data Availability Statement:** The data presented in this study is available upon request from the corresponding author.  
**Acknowledgments:** We thank [PERSON], [PERSON], [PERSON], [PERSON], and [PERSON] for the field, lab, and data assistance. [PERSON] contributed to this research at the Jet Propulsion Laboratory, California Institute of Technology, under a contract with the National Aeronautics and Space Administration. Government sponsorship acknowledged. J.B.F. was supported in part by NASA programs: IDS and ECOSTRESS. Copyright 2021. All rights reserved.  
**Conflicts of Interest:** The authors declare no conflict of interest.