Abstract:

To overcome the challenge posed by the mutual restriction between the limited coverage capacity of high-resolution images, which hinders their promotion and application on large spatial scales, and the relatively low extraction accuracy of medium-resolution images in quantitatively assessing winter wheat planting abundance and measuring planting areas over extensive regions, this study conducted experiments in the Huang-Huai-Hai Plain. Six evenly distributed Sentinel-2 tiles covering a vast spatial area served as the experimental region. The winter wheat planting area, derived from Sentinel-2 data, was converted into planting abundance with a spatial resolution of 250 meters. Subsequently, planting abundance data and time-series MODIS NDVI were combined to train a random forest regression model, aiming to achieve quantitative assessments of winter wheat planting abundance and measurements of planting areas on a large scale. In contrast to traditional methods relying on MODIS NDVI time-series data for extracting winter wheat planting areas, which necessitate additional mixed image decomposition to ascertain planting abundance, this study utilized the random forest regression approach to directly ascertain the planting abundance of each pixel, thereby eliminating the mixed image decomposition step. The trained random forest classification model exhibited an F₁ score exceeding 0.9983 across different tiles. The random forest regression model tended to stabilize when the training set sample size comprised over 2% of the total sample size. The model proved most suitable for use at a 50% sample size, yielding an R² value of 0.8140. At a 90% sample size, the regression model achieved a maximum R² of 0.8162. The random forest regression models predicted winter wheat planting abundance and area with accuracies exceeding 91% and 99% of Sentinel-2’s accuracy, respectively. This meets the operational accuracy requirements for large-scale quantitative evaluations of winter wheat planting abundance and area measurements.

Key words: winter wheat, combination of medium and high resolution, planting abundance, random forest, classification, regression