## 2 Materials and Methods
### Data Collection
#### 2.2.3 Crop Rotation  
We conducted multiple field investigations in Zengcheng and learned that the crop rotations are extremely complex due to fragmented cropland, multi-cropping, and diverse crops. There are many kinds of vegetables that could be harvested three or more times per year, and rice could be harvested twice a year (double paddy rice). Usually, vegetables are rotated with rice in a year to balance economic output and grain production, e.g., early rice-late rice-winter vegetable and spring vegetable-late rice.  
To represent the complex crop rotations, we first provided a classification scheme based on the field investigation, farmer interviews, expert knowledge, and agricultural statistics. The classification included three main systems, i.e., paddy, vegetable, and orchard systems, which were divided into nine subsystems with distinct seasonal dynamics. The paddy system had single and double rice, which were further distinguished by the presence of vegetables. The vegetable system was classified into low- and high-intensity (includes low- and high-diversity) vegetables. Finally, the orchard system was classified into short- (e.g., banana, papaya) and long-term (e.g., litchi, longan) orchards. Based on the analyses of their spectral and temporal signatures, we then proposed a hierarchical rule-based algorithm to identify the crop rotation systems in 2020 and 2021 (Figure 2). We initially obtained four key indicators: flooding frequency, cropping intensity, cropping diversity, and coefficient of variation. Then, we mapped the three main crop rotation systems according to Sentinel-2 images and the random forest (RF) algorithm. Finally, the nine crop rotation subsystems were identified by the four indicators. For example, to identify the low-diversity and high-diversity vegetables, we designed and calculated an indicator called \"cropping diversity\".  
\begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Environmental Variables** & & **Year** & **Resolution** & **Source** \\ \hline Organisms, vegetation, fauna, or human activities & Vertical–horizontal (VH) and vertical–vertical (VV) polarization & May 2015–December & 10 m & \begin{tabular}{l} Bimonthly median \\ composites of Sentinel-1 \\ \end{tabular} \\ Organisms, vegetation, fauna, or human activities & & & & \\ \hline \multirow{4}{*}{\begin{tabular}{l} Originals, vegetation, \\ fauna, or human activities \\ \end{tabular} } & Spectral bands (2-8, 8A, 11, and 12), normalized difference vegetation index (NDVI), soil-adjusted vegetation index (SAVI), land surface water index (LSWI), and three soil salinity indices (SSII, SSI2, and SSI3) & July 2017–March 2022 & 10 m & \
\begin{tabular}{l} Seasonal median \\ composites of Sentinel-2 \\ \end{tabular} \\ \cline{2-5}  & Cropland type & 1985–2021 & 30 m & [39] \\ \cline{2-5}  & Crop rotation systems & 2020–2021 & 10 m & [27] \\ \hline Soil & Soil organic carbon (SOC), pH, texture (sand, silt, clay), bulk density, and thickness & — & 1 km & [40] \\ \hline \multirow{4}{*}{Climate} & Mean annual temperature (MAT) and mean annual precipitation (MAP) & \multirow{2}{*}{2021} & \multirow{2}{*}{1 km} & [41] \\ \cline{2-5}  & Mean annual daytime and nighttime land surface temperature (LST) & & & \\ \cline{2-5}  & Minimum, mean and maximum temperature, precipitation & & & \\ \hline \multirow{4}{*}{
\begin{tabular}{l} Relief \\ \end{tabular} } & Elevation, aspect, slope, topographic & & & \\ \cline{2-5}  & wetness index (TWI), plan curvature (PLC), profile curvature (PRC), slope-length factor (LS), valley depth & & & \\ \cline{1-1} \cline{2-5}  & (VD), convergence index (CI), channel network base level (CNBL), catchment network distance (CND), relative slope position (RSP), and vertical distance to channel network (VDCN) & - & 30 m & DEM from SRTM \\ \hline \hline \end{tabular}
\end{table}
Table 1: The collected environmental variables used to predict the soil pH.