Although China is the largest producer of rice, accounting for about 25% of global production, there are no high-resolution maps of paddy rice covering the entire country. Using time-weighted dynamic time warping (TWDTW), this study developed a pixel- and phenology-based method to identify planting areas of double-season paddy rice in China, by comparing temporal variations of synthetic aperture radar (SAR) signals of unknown pixels to those of known double-season paddy rice fields. We conducted a comprehensive evaluation of the method’s performance at pixel and regional scales. Based on 145,210 field surveyed samples from 2018 to 2020, the producer’s and user’s accuracy are 88.49% and 87.02%, respectively. Compared to county-level statistical data from 2016 to 2019, the relative mean absolute errors are 34.11%. This study produced distribution maps of double-season rice at 10 m spatial resolution from 2016 to 2020 over nine provinces in South China, which account for more than 99% of the planting areas of double-season paddy rice of China. The maps are expected to contribute to timely monitoring and evaluating rice growth and yield.

Winter wheat is a major staple food grown worldwide. China contributes about 19% of the winter wheat production in the world. Therefore, quickly and accurately acquiring winter wheat distribution in China over long time periods is critical to achieve grain safety and understand the spatiotemporal patterns of winter wheat. However, there is a lack of widely available, high spatial resolution, long-term, and large-scale remotely sensed maps on winter wheat cultivation in China. In this study, we utilized a phenology-based algorithm to identify winter wheat location. The algorithm integrated three key growing features of winter wheat into one variable to calculate the planting probability of winter wheat. Here, we produced a long-term winter wheat map dataset at 30-m resolution for China from 2001 to 2020 using the fusion dataset. The resultant maps achieved high accuracy based on field survey samples, with user’s, producer’s, and overall accuracies of 91.17%, 90.92%, and 91.6%, respectively. Furthermore, the mapped winter wheat area aggregated to municipal- and county-levels exhibited good correlations with agricultural statistical data.

Sugarcane is an important sugar and biofuel crop with high socio-economic importance, and its planted area has increased rapidly in recent years. China is the world’s third or fourth sugarcane producer. However, to our knowledge, no study has investigated the mapping of sugarcane cultivation areas across entire China. In this study, we developed a phenology-based method to identify sugarcane plantations in China at 30-m spatial resolution from 2016–2020 using the time-series of Landsat and Sentinel-1/2 images derived from Google Earth Engine (GEE) platform. The method worked by comparing the phenological similarity in normalized difference vegetation index (NDVI) series between unknown pixels and sugarcane samples. The phenological similarity was assessed using the time-weighted dynamic time warping method (TWDTW), which has less sensitivity to training samples than machine learning methods and therefore can be easily applied to large areas with limited samples. More importantly, our method introduced multiple and moving time standard phenological curves of sugarcane to the TWDTW by fully considering the variable crop life-cycle of sugarcane, particularly its long harvest season spanning from December to March of the following year. Validations showed the method performed well in 2019, with overall accuracies of 93.47% and 92.74% for surface reflectance (SR) and top of atmosphere reflectance (TOA) data, respectively. The sugarcane maps agreed well with the agricultural statistical areas from 2016–2020. The mapping accuracies using TOA data were comparable to SR data in 2019–2020, but outperformed SR data in 2016–2018 when SR data had lower availability on GEE. The sugarcane maps produced in this study can be used to monitor growing conditions and production of sugarcane and, therefore, can benefit sugarcane management, sustainable sugarcane production, and national food security.

Accurate information about the location and extent of irrigation is fundamental to many aspects of food security and water resource management. This study develops a new method for identifying irrigation in northeastern China by comparing canopy moisture between the cropland and adjacent natural ecosystems (i.e., forests). This method is based on two basic assumptions, which we validated using field survey data. First, the canopy moisture of irrigated cropland, indicated by a satellite-based land surface water index (LSWI), is higher than that of the adjacent forest. Second, the difference in LSWI between irrigation cropland and forest is larger in arid regions than in humid regions. Based on the field survey and statistical dataset, our method performed well in indicating spatial variations of irrigated areas. Results from this study suggest that our method is a promising tool for mapping irrigated areas, as it is a general and repeatable method that does not rely on training samples and can be applied to other regions.