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.

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.

Winter cereals, including wheat, rye, barley, and triticale, are important food crops, and it is crucial to identify the distribution of winter cereals for monitoring crop growth and predicting yield. The production and plating area of winter cereals in Europe both contribute 12.57% to the total global cereal production and plating area in 2020. However, the distribution maps of winter cereals with high spatial resolution are scarce in Europe. Here, we first used synthetic aperture radar (SAR) data from Sentinel-1 A/B, in the Interferometric Wide (IW) swath mode, to distinguish rapeseed and winter cereals; we then used a time-weighted dynamic time warping (TWDTW) method to discriminate winter cereals from other crops by comparing the similarity of seasonal changes in the Normalized Difference Vegetation Index (NDVI) from Landsat and Sentinel-2 images. We generated winter cereal maps for 2016–2020 that cover 32 European countries with 30 m spatial resolution. Validation using field samples obtained from the Google Earth Engine (GEE) platform show that the producer’s and user’s accuracies are 91% ± 7.8% and 89% ± 10.3%, respectively, averaged over 32 countries in Europe. The winter cereal map agrees well with agricultural census data for planted winter cereal areas at municipal and country levels, with the averaged coefficient of determination R² as 0.77 ± 0.15 for 2016–2019. In addition, our method can identify the distribution of winter cereals two months before harvest, with an overall accuracy of 88.4%, indicating that TWDTW is an effective method for timely crop growth monitoring and identification at the continent level. The winter cereal maps in Europe are available via an open-data repository.