## 3 Materials and Methods
### Retrieval of Vegetation Properties Time Series Using HLS and Airborne Data and the SCOPE Model
To tune the parameters, we used nonlinear least squares optimization, implemented in the MATLAB (the MathWorks Inc., Natick, MA, USA) built-in function \"lsqnonlin\", to minimize a cost function that calculated the sum of squared differences between the simulated and measured mean TOC reflectance in the selected six HLS optical bands. In this study, we chose to retrieve five biochemical parameters (\(C_{\text{ab}}\), \(C_{\text{w}}\), \(C_{\text{dm}}\), \(C_{\text{s}}\), and \(C_{\text{ca}}\); Table 2) and three structural parameters (\(LA1\), leaf inclination distribution function (\(LIDF\)) and leaf structural parameter (\(N\)); Table 2). To run the optimization method, prior information, i.e., range, mean, and standard deviation of each vegetation property to be retrieved (Table 2) was needed. We used non-informative prior information following [PERSON] et al. [21] and [PERSON] et al. [22] to ensure the significant impact of the measured HLS reflectance on the retrieved parameters. We used the middle of the total range (i.e., (lower bound + upper bound)/2) as a prior mean of each vegetation property. Assuming uniform prior distribution over the range between the lower and upper bound produced values of standard deviation equal to \(1/\sqrt{12}\approx 0.3\) times the range of each vegetation property. We fixed the SWC based on the measurements of mean SWC within the target area measured at the observation time of HLS data (Section 3.1.2 and Table 3). We used the BSM submodel to describe soil background reflectance. The BSM submodel is based on the library of global soil vectors [56], which is extended via a brightness-shape component and SWC effect. This has two main advantages: (1) the model can simulate soil reflectance, providing an effective alternative to soil spectrum measurements; and (2) the model can scale the dry soil spectrum for any given SWC. Further, the BSM submodel can be inverted to retrieve BSM input parameters (soil brightness (\(B\)), \(Lat\), and \(Lon\)) from the given dry soil spectrum. We took advantage of this possibility and retrieved these three input parameters by inverting the BSM submodel against a dry soil reflectance extracted from the airborne data. We also calculated a mean TOC reflectance within the target area from the airborne hyperspectral data acquired on 31 August 2016. It was used to retrieve the vegetation properties using the RTMo inversion, where we used the same non-informative prior information of the vegetation properties and the BSM input parameters as we used for HLS data. We further used the ground measurements (Table 2) to compare the retrieved vegetation properties obtained from HLS and airborne data. \begin{table}
\begin{tabular}{l l l l l l l} \hline \hline
**Date of HLS Data** & **Sensing Satellite** & **Julian Day** & **Time of Observation in CET** & **\% of High Quality Pixels** & **Mean NDVI** & **Mean SWC (\%) at the Time of Observation** \\ \hline
3 April 2016 & S30 & 94 & 11:54:06 & 87.73 & 0.73 & 28.45 \\
16 April 2016 & L30 & 107 & 11:38:12 & 96.47 & 0.82 & 31.54 \\
23 May 2016 & S30 & 144 & 11:54:04 & 100 & 0.81 & 23.3 \\
27 May 2016 & L30 & 148 & 11:32:29 & 100 & 0.84 & 24.94 \\
2 July 2016 & S30 & 184 & 11:52:19 & 100 & 0.87 & 18.41 \\
5 July 2016 & L30 & 187 & 11:38:30 & 78.81 & 0.87 & 22.67 \\
22 July 2016 & S30 & 204 & 11:53:51 & 100 & 0.87 & 25.21 \\
31 August 2016 & L30 & 244 & 11:33:01 & 100 & 0.88 & 23.37 \\
7 September 2016 & L30 & 251 & 11:38:50 & 66.91 & 0.81 & 27.23 \\
10 September 2016 & S30 & 254 & 11:50:27 & 70.45 & 0.87 & 23.52 \\
16 September 2016 & L30 & 260 & 11:33:04 & 100 & 0.88 & 17.28 \\
20 September 2016 & S30 & 264 & 11:52:23 & 99.07 & 0.9 & 19.31 \\
23 September 2016 & L30 & 267 & 11:38:51 & 100 & 0.85 & 18.95 \\
30 September 2016 & S30 & 274 & 11:50:25 & 51.86 & 0.86 & 15.61 \\ \hline \hline \end{tabular}
\end{table}