A Global Sensitivity Analysis of Commonly Used Satellite-Derived Vegetation Indices for Homogeneous Canopies Based on Model Simulation and Random Forest Learning  
[PERSON]  
1 Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China; [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (D.Y. ); [EMAIL_ADDRESS] (Z.L.) [PERSON]  
1 Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China; [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (D.Y. ); [EMAIL_ADDRESS] (Z.L.) [PERSON]  
1 Beijing Institute of Spacecraft System Engineering, China Academy of Space Technology, Beijing 100094, China; [EMAIL_ADDRESS] (S.W. ); [EMAIL_ADDRESS] (D.Y. ); [EMAIL_ADDRESS] (Z.L.) [PERSON]  
2 Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (L.L. ); [EMAIL_ADDRESS] (C.H.) [PERSON]  
2 Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China; [EMAIL_ADDRESS] (L.L. ); [EMAIL_ADDRESS] (C.H.) [PERSON]  
4 Key Laboratory of Oasis-Eco Agriculture, Xinjiang Production and Construction Group, Shihezi University, Shihezi 832003, China 42 Management of Computing and Information Systems,  
reflectivity, and sun-sensor geometry [3; 4]. Therefore, \(\rho\) and consequently VIs are almost never related to a single variable of interest. Over the last decades, many VIs have been proposed with different original purposes. As a double-edged sword, the potential diverse sensitivity of VIs makes VIs useful in a variety of contexts but results in uncertainties in different situations as well. The applications of VIs can be roughly categorized into two classes: i) estimating bio-physical parameters and ii) being integrated into ecological models as proxies for vegetation traits. For the first class of application, i.e., retrieving certain bio-physical parameters (e.g., leaf chlorophyll a+b content, Cab, leaf water content, Cw, and leaf area index, LAI), the additional sensitivity of the used VI to confounding factors beyond the parameter of interest can induce uncertainties to the empirically established regression model. For example, the sensitivity of Cab-sensitive VIs to LAI should be suppressed when they are used to estimate Cab [5]. Recently, a comprehensive global sensitivity analysis (GSA) for Cab-, LAI- and Cw-sensitive VIs has been made on the basis of radiative transfer model (RTM) simulations to evaluate the robustness of VIs with respect to corresponding sensitive parameters [6]. While results in [6] provide a good guidance for applying VIs in parameter retrieval, for the second class of applications, i.e., using VIs to represent vegetation traits at large scales, the sensitivity of VIs to a single parameter is not always concerned, but rather VIs are sometimes expected to be sensitive to combined parameters. For example, for agricultural drought monitoring, an ideal VI could be related to both vegetation greenness and canopy water content (CWC) [7]. For representing photosynthetic capacity, a VI that is more closely related to canopy chlorophyll content (CCC) might be a better choice [8; 9]. Therefore, a GSA of VIs considering combined parameters such as CWC and CCC that have clear physical meaning is of primary importance for understanding the potential and uncertainty of applying VIs at large scales. Typically, GSA can be based on RTM simulations and a variance-based method [3; 6; 10; 11], but some limitations exist for a variance-based GSA for VIs. A variance-based GSA uses certain mathematical techniques to decompose the variance of the output to different inputs [11; 12]. Therefore, it has a relatively complex, computational cost and cannot provide the sensitivity of VIs to combined parameters with clear ecological meaning (e.g., CCC and CWC), which is often concerned in VI applications. In this study, we perform a GSA for several satellite-derived VIs that have been used at large scales on the basis of the PROSAIL RTM and random forest (RF) learning.