## 3 Methodology
### Phenology-GPP Method for Crop Classification
#### 3.1.1 Remote Sensing Mapping of Cultivated Land
We identified cultivated land using Sentinel-2 images from 2019 based on the above analysis. [PERSON] [52] used the method of decision tree classification to classify the land use types of the Yuecheng Reservoir catchment. To identify cultivated land, we divided the decision tree into five levels (Figure 7a) according to this method. Each level removed a class of typical features, ultimately yielding a distribution map of the cultivated land. The normalized difference water index (NDWI) was used for water identification to separate water from land. Compared with other water indices, NDWI can enhance water signals by effectively suppressing vegetation signals. Our aim was to identify cultivated land, so more attention was paid to the index's ability to distinguish between other features and vegetation. Based on the land map, NDVI was used to separate vegetation and non-vegetation areas. Compared with other vegetation indices, NDVI is more sensitive to vegetation with low and medium FVC, so it is effective in distinguishing vegetation from non-vegetation. August is the most productive month with respect to vegetation growth in the Yuecheng Reservoir catchment area, and the NDVI value approaches its maximum for the whole year during this month, thereby enabling vegetation to be distinguished from  
Figure 6: Comparison of the sensitivity of each vegetation index to different FVC areas. non-vegetation. For some winter single crops, the cultivated land is in a fallow state in August and the NDVI value is small. Thus, winter single crops were missed when mapping only using the images from August. The identification of vegetation is not accurate enough, and some planting areas of winter single crops cannot be identified as vegetation. To aid in avoiding this problem, we used the maximum synthesis method [53] for the annual NDVI time series images to obtain NDVI\({}_{\text{max}}\) and set the appropriate threshold to get the vegetation distribution map. From the vegetation map, we need to identify the shrubland, grassland and forest land to get the map of cultivated land. The FVC of grassland and shrubland is lower than that of cultivated land and forest. After comparing the values of the universal pattern decomposition (VIUPD) index in a large number of vegetation samples, we found that the VIUPD could differentiate shrubland and grassland from other vegetation. To identify cultivated land from forest, the ratio vegetation index (RVI) was used. Forest and cultivated land have high FVC, and RVI is just sensitive to vegetation with high FVC. Based on large vegetation samples, RVI has the ability to separate cultivated land from forest. The thresholds of these indices are general empirical values. Finally, after the removal of other features at each level, we obtained the remote sensing mapping results of cultivated land. Table 2 lists the spectral indices used in this study. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Model** & **Abbreviation** & **Formula** \\ \hline Normalized Difference Water Index [54] & NDWI & NDWI = \(\frac{\text{G-NIR}}{\text{C+NIR}}\) \\ \hline Normalized Difference Vegetation Index [55] & NDVI & NDVI = \(\frac{\text{NIR-R}}{\text{NIR-R}}\) \\ \hline Vegetation Index based on the Universal Pattern & & \\ Decomposition Method [56] & VIUPD & VIUPD = \(\frac{\text{C}y-01\text{C}z-\text{C}z}{\text{C}y+\text{C}y+\text{C}z}\) \\ \hline Ratio Vegetation Index [57] & RVI & RVI = \(\frac{\text{NIR}}{\text{R}}\) \\ \hline \hline \end{tabular} Abbreviations: R, red; G, green; NIR, near-infrared; MIR, mid-infrared; C\({}_{W}\), UPDM coefficient of standard water; C\({}_{V}\), UPDM coefficient of standard vegetation; C\({}_{S}\), UPDM coefficient of standard soil; C\({}_{L}\), UPDM coefficient of standard winder and yellow vegetation;  
\end{table} 
Table 2: Spectral indices used in this study. Figure 7: (**a**) Extraction of cultivated land using a classification decision tree method. (**b**) Flow chart depicting crop information extraction.