Mapping winter fallow arable lands in Southern China by using a multi-temporal overlapped area minimization threshold method - Xiangyi Wang; Yingbin He; Yan Zha; Huicong Chen; Yongye Wang; Xiuying Wu; Jiong Ning; Anran Feng; Shengnan Han; Shanjun Luo (GIScience & Remote Sensing)
In China, a nation facing farmland scarcity, accurate mapping of winter fallow arable lands is crucial for enhancing crop rotation and land use efficiency. The Dynamic Threshold (DT) method commonly used in phenology and winter fallow land studies often employs empirically set subjective thresholds. This approach tends to uniformly apply a single threshold across diverse vegetation types and geographical regions, neglecting the variations in physiological traits and spatial heterogeneity. This study developed a multitemporal overlapping area minimization threshold method (MOAMT). This approach constructed Normalized difference vegetation index (NDVI) probability density functions for both winter fallow and non-winter fallow arable land across a time series to attain a statistically significant threshold. We use MOAMT and DT methods for winter fallow arable land extraction in southern China, respectively. And then, comparison of identification accuracy between MOAMT and DT has been implemented by using confusion matrix method. Compared to the DT method, MOAMT exhibits better performance. The mapping of winter fallow arable lands highlighted their predominant distribution in the middle and lower reaches of the Yangtze River (MLR-YR) Basin, covering approximately 20.8 million hectares, with potential for development. This study will provide information support for the optimization of planting layouts in China, offering new opportunities for further increasing grain production. While in the sense of research approach, this study indicates the superiority of threshold obtained through statistical probability calibration over empirical universal thresholds for classification accuracy. We advocate for prioritizing threshold calibration in future applications of threshold classification methods.