Rapeseed distribution mapping is a crucial issue for food and oil security, entertainment, and tourism development. Previous studies have used various remote sensing approaches to map rapeseed. However, the time-consuming and labor-intensive sample data used in these supervised classification methods greatly limit the development of large-scale mapping in rapeseed studies. Regarding threshold methods, some empirical thresholding methods still need sample data to select the optimal threshold value, and their accuracies decrease when a fixed threshold is applied in complex and diverse environments. This study first developed the Normalized Difference Rapeseed Index (NDRI), defined as the difference in green and short-wave infrared bands divided by their sum, to find a suitable feature to distinguish rapeseed from other types of crops. Next, a two-stepwise hierarchical adaptive thresholding (THAT) algorithm requiring no training data was used to automatically extract rapeseed in Xinghua. Finally, two adaptive thresholding methods of the standalone Otsu and Otsu with Canny Edge Detection (OCED) were used to extract rapeseed across Jiangsu province. The results show that (1) NDRI can separate rapeseed from other vegetation well; (2) the OCED-THAT method can accurately map rapeseed in Jiangsu with an overall accuracy (OA) of 0.9559 and a Kappa coefficient of 0.8569, and it performed better than the Otsu-THAT method; (3) the OCED-THAT method had a lower but acceptable accuracy than the Random Forest method (OA = 0.9806 and Kappa = 0.9391). This study indicates that the THAT model is a promising automatic method for mapping rapeseed.

In low-relief agricultural areas, crop cover makes it challenging to obtain remotely sensed bare soil spectral data for predicting soil texture. Therefore, this study proposed a method for predicting soil texture using crop growth information with spatiotemporal stability. Spatiotemporal Stable Peak (SSP) maps were generated using the Ratio Vegetation Index (RVI) time-series data of rice and wheat, and they were used to represent crop growth information with spatiotemporal stability. Eighty-three soil sampling sites were arranged on the SSP maps with a regular grid. Ridge Regression, Ordinary Kriging, and Co-Kriging were adopted to map soil texture. The results showed that the SSP was closely related to clay and sand contents, with Pearson’s |r| ranging from 0.57 to 0.67. SSP-based Ridge Regression yielded better prediction accuracy (MAE = 3.95 and RMSE = 4.57) than Ordinary Kriging (MAE = 4.45 and RMSE = 5.19) in predicting clay content. The comparison between Ordinary Kriging and SSP-based Co-Kriging further demonstrated the effectiveness of SSP in improving clay content prediction accuracy, with an increase in R² of 70% and a reduction in RMSE of 3.85%. Similar results were obtained for sand content prediction. These results suggest that SSP can serve as an effective environmental variable for predicting soil texture spatial variation in low-relief agricultural areas.