## 3 Materials and Methods
### Machine-Learning-Based Leaf Area Index Inversion Model  
#### 3.2.1 Preprocessing of Satellite Imagery  
Before inversing LAI from remote-sensing reflectance imagery, there are several steps need to be implemented. The first step is using the maximum value compositing to generate annual satellite imagery over the dry season since Landsat satellites have a 16-day revisit cycle, which means vegetation can only be observed every 16 days and the condition could be worse when cloud contaminates the Landsat imagery, such as thick clouds blocking  
\begin{table}
\begin{tabular}{l l l} \hline \hline
**Period Number** & **Imbounded Water Level** & **Included Years** \\ \hline
1 & - & -2002 \\ \hline  & 135.0–139.0 m & 2003–2005 \\
2 & 144.0–156.0 m & 2006–2007 \\  & 145.0–172.8 m & 2008 \\ \hline
3 & 145.0–171.4 m & 2009 \\  & 145.0–175.0 m & 2010–present \\ \hline \hline \end{tabular}
\end{table}
Table 2: Essential information on 3 operation periods of the Three Gorges Dam (TGD).  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Data Type** & **Spatial** & **Temporal** & **Period** & **Source** \\ \cline{3-5}  & **Resolution** & **Resolution** & **Period** & **Source** \\ \hline Imagery & 30 m & 16 days & 2003–2012 & Landsat-5, USGS \\ \cline{3-5}  & & 2014–2021 & Landsat-8, USGS \\ \hline Discharge & - & 1 day & 1991–2020 & CWRC \\ \hline Water level & - & 1 day & 1991–2020 & CWRC \\ \hline Cross-section profile & 2 km & 1 year & 2003–2020 & CWRC \\ \hline \hline \end{tabular}
\end{table}
Table 3: Sources and resolution of measurements.  
vegetation information [40]. Specifically, we computed the normalized difference vegetation index (_NDVI_):  
\[NDVI=\frac{NIR-R}{NIR+R} \tag{1}\]  
where _NIR_ and \(R\) are near-infrared and red reflectance bands, respectively. The _NDVI_ is a good indicator for monitoring vegetation dynamics [49; 50], and large _NDVI_ values reflect strong vegetation signals. We selected the pixel along with the maximum _NDVI_ value as the composited value from several candidate pixels from October to April every year. Note that images acquired from 2011-2013 were not used in this study because these images suffered many errors, including geo-registration errors and cloud contamination issues. Then, water masks were generated by Landsat water index (_WI\({}_{2015}\)_) [51]:  
\[WI_{2015}=1.7204+171G+3R-70 NIR-455 WIR1+71 SWIR2 \tag{2}\]  
where \(G\), _SWIR_1, and _SWIR_2 are the green band and shortwave infrared bands 1 and 2, respectively. Normally, a remote-sensing index is designed to divide remote-sensing imagery into two categories. In this study, we used the _NDVI_ and _WI\({}_{2015}\)_ to identify vegetation and non-vegetation and water and non-water, respectively. Large _WI\({}_{2015}\)_ values reflect strong water signals. Therefore, the threshold of _WI\({}_{2015}\)_ is set to 0.5 to mask out water for monitoring bar vegetation dynamics. Subsequently, the threshold of the _NDVI_ was set to 0.3 to identify vegetation. In detail, a pixel with a _WI\({}_{2015}\)_ value greater than 0.5 will be classified as water; otherwise, it is non-water. Note that a pixel with an _NDVI_ value greater than 0.3 will be classified as vegetation; otherwise, it is non-vegetation.