## 3 Methodology
### Time Window Retrieval  
When to establish rice depends on genetic, environmental, and management factors [19]. Environment related factors (e.g., temperature [11]) can be used to retrieve rice phenology only if they are the primary limitation for rice planting, which is not the case in the HJH plain. Several researchers used the crop calendar provided by meteorological stations or agricultural observation stations [24; 25]. However, these approaches may fail in undeveloped countries where stations are not available and the local agricultural ministry cannot provide a reliable crop calendar. Remote sensing data provide reliable and robust solutions to rice phenology retrieval [26; 27; 28]. The PhenoRice algorithm monitors information of primarily paddy rice areas, dates for the establishment (Start Of Season, SOS) and flowering (Peak of Season, POS) periods, and cropping intensity by analyzing the time series of individual pixels [19]. For each MODIS pixel (except those excluded by the mask), PhenoRice determines if there is a high probability of belonging to a rice area, estimating the rice crop establishment dates. The EVI time series of each pixel is smoothed using the Savitzky-Golay two-iteration method to reduce noise from cloud contamination or various acquisition angles. The smoothing method assigns weights to the EVI values based on the estimated data quality [13] using information contained in the Pixel Reliability, Usefulness Index, and blue reflectance. The resulting smoothed time series is adapted to the upper envelope of the original curve because those noises have negative biases on original values generally. The smoothed EVI signal is analyzed to identify all local minima and maxima within the time series. Following [PERSON] et al. [10], these minima and maxima are analyzed based on agronomic criteria (e.g., occurrence of flood event in transplanting phase, the duration from SOS to POS) to determine the presence of a \"rice-cycle signal\" in the time series. Pixels that meet these criteria are labeled as rice, and their rice crop establishment and flowering dates are  
Figure 4: The workflow graph showcases the procedure of multi-temporal edge detection analysis. Multiple edge layers (**b**) are extracted from multiple corresponding Sentinel-2 10m bands (**a**), then superimposed to single edge layer (**c**). The continuous edge layer is thresholded to binary edge layers (**d**), where the black is non-edge region (agricultural fields) and the white is the edge region. The red ellipses in (**d**) show examples of over-segmentation where a single field is separated by false edges, and under-segmentation where multiple fields with different crops planted are not separated. (**e**) from left to right and top to bottom shows the zoom-in binary edge of that in the middle of (**d**), reference edge over VHR images, reference edge over detected edge, and VHR image, respectively. It should be noted that we use cadastral boundaries as reference edges, so some reference edges are redundant (e.g., division lines in river) and some are missing where multiple fields have the same ownership. estimated. A more detailed description of the PhenoRice algorithm and its performance can be found in [19]. The EVI [29] and normalized flood index (NDFI) used in PhenoRice [19] are calculated as:  
\[\text{EVI}=2.5\times\frac{\rho_{NIR}-\rho_{Red}}{\rho_{NIR}+6\times\rho_{Red}- 7.5\times\rho_{Blue}+1} \tag{1}\]  
\[\text{NDFI}=\frac{\rho_{Red}-\rho_{SWIR}}{\rho_{Red}+\rho_{SWIR}} \tag{2}\]  
For the exemplar plot (latitude: 30.644440\({}^{\circ}\) N, longitude: 120.804088\({}^{\circ}\) E), the SOS date determined with the PhenoRice algorithm is the 46 th observation in the required time series. Figure 6 shows the exemplar plots of the PhenoRice estimates for the rice phenological dates (SOS and POS are inflection points in the fitted EVI curve, shown as the orange dotted lines) based on the temporal patterns of the NDVI and the smoothed/raw EVI for different cropping systems for a case pixel.