## 3 Methods
### Daily 100-m Reflectance Dataset Generation  
The STARFM and ESTARFM models were employed to blend the PROBA-V 100-m and 300-m S1 data because both of them have been widely and successfully used due to their ease of implementation and reasonable algorithm complexity [34; 35; 36; 37]. We chose ESTARFM as the main fusion model because it was developed based on STARFM but overcomes STARFM's shortcoming of making inaccurate predictions in heterogeneous landscapes such as that of our study area, which has various field sizes comparable to that of a 300-m pixel. Because the ESTARFM algorithm requires at least two pairs of 100-m/300-m images, STARFM was used when only one pair of images was available, for example, at the start and end of the growing season (i.e., in October 2014 and June 2015; Table 2). To minimize the uncertainty caused by human activity or environmental changes, the temporally closest available data were selected as two pairs in ESTARFM, and the images marked in bold in Table 2 were used as references to assess the predicted images [38]. The implementation of the two data fusion methods required resampling of the 300-m data to the spatial resolution of the 100-m imagery using a nearest neighbor algorithm. No further processing was applied because the provided PROBA-V data were pre-processed for atmospheric and geometric corrections. The main technical basis of the STARFM and the ESTARFM algorithms are briefly described as follows. Figure 2: Flowchart of the aboveground biomass and yield mapping process. The STARFM can generate synthetic images based on a pair image of coarse-spatial-resolution and high-spatial-resolution (base images) and the coarse-spatial-resolution data on the prediction date through establishing a linear relationship between the base images of coarse-spatial-resolution and high-spatial-resolution. Assuming that land cover types keep stable over time, the data at high-spatial-resolution for date (\(t_{k}\)) can be predicted through using coarse-spatial-resolution observations for date (\(t_{k}\)) and one or several pairs of high-spatial-resolution and coarse-spatial-resolution images acquired at the same date (\(t_{0}\)). \[H\left(x_{i},y_{j},t_{k}\right)=\ C\left(x_{i},y_{j},t_{k}\right)+\ H\left(x_{i },y_{j},t_{0}\right)-\ C\left(x_{i},y_{j},t_{0}\right) \tag{1}\]  
where \(H\) and \(C\) represent high-spatial-resolution and coarse-spatial-resolution image reflectance, \(\left(x,y\right)\) is the location of the predicted pixel reflectance, \(t_{0}\) is the date of one input pair data, and \(t_{k}\) is the date of predicted data. During the implementation of ESTARFM algorithm, four major steps come about. Firstly, two high-spatial-resolution images are used to search for pixels similar to the central pixel in a local window. Secondly, the weight of each similar pixel \(w_{i}\) was calculated by the spectral and spatial distance between similar pixel and its corresponding coarse-spatial-resolution pixel. Thirdly, the conversion coefficient \(v_{i}\) can be computed from the linear regression of the high-spatial-resolution reflectance in the two observed pairs (\(t_{1}\) and \(t_{2}\)) against the coarse-spatial-resolution reflectance of the similar pixels. Finally, \(w_{i}\) and \(v_{i}\) are used to calculate the high-spatial-resolution reflectance from the coarse-spatial-resolution image at the desired prediction date \(t_{p}\), predicted as:  
\[H\left(x,y,t_{p}\right)=H\left(x,y,t_{0}\right)+\ \sum_{i=1}^{N}w_{i}\times v_{i} \times\left(C\left(x_{i},y_{i},t_{p}\right)-C\left(x_{i},y_{i},t_{0}\right)\right) \tag{2}\]  
where \(H\) and \(C\) represent high-spatial-resolution image and coarse-spatial-resolution image reflectance, \(\left(x,y\right)\) is the location of the predicted pixel value while \(x_{i}\) and \(y_{i}\) is the location of \(i\)th similar pixel, and \(t_{0}\) is the date of one input pair (\(t_{1}\) or \(t_{2}\)). \(N\) is the total number of similar pixels of the predicted pixel within a local window. The two algorithms are explained in detail in [PERSON] et al.