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Quickly and accurately obtaining information on rice planting areas is crucial for maintaining food security and social stability. Based on the time series of Sentinel-1A images, this study proposes an automatic rice mapping method called the Dynamic Threshold Time-Weighted Dynamic Time Warping method (DT-TWDTW). This method consists of two parts. First, rice samples are extracted automatically by a dynamic threshold method based on the unique flooding signal before and after rice transplanting. Then, the Time-Weighted Dynamic Time Warping (TWDTW) method is used for rice mapping based on the cumulative cost matrix between unknown pixels and rice samples. The results show that the DT-TWDTW method can achieve satisfactory mapping accuracy with an overall accuracy of 94.6%, higher than random forest (89.9%), and support vector machine (91.7%). This study provides a new approach and perspective for remote sensing monitoring of rice, especially for some regions without prior phenological knowledge and collection of the rice samples. [ABSTRACT FROM AUTHOR]

Efficiently obtaining tobacco planting area is significant for rationally allocating tobacco resources and realizing the balance between supply and demand. However, tobacco fields are characterized by fragmentation in hilly areas, which brings various challenges to extracting the tobacco planting area. A 16-square-kilometre tobacco planting region in Xuancheng City, Anhui Province, China, was selected as the study area in this paper. First, the single-temporal full-feature sets (STFFS), a time-series full-feature set (TFFS) and a time-series optimal-feature set (TOFS) were constructed from multi-period Sentinel-2 images, respectively. Then, we applied Random Forest (RF), Support Vector Machine (SVM), Neural Network Classification (NNC) and Maximum Likelihood Classification (MLC) to classify the feature sets and compare the classification accuracy. The experimental results demonstrate that: (1) The best growth stage of tobacco remote sensing recognition is the mulching film phase of the spherical plant stage (ST1 period). (2) The classification accuracy indicates that TOFS outperforms the STFFS. (3) TOFS can still maintain the same classification accuracy as TFFS with fewer feature dimensions. (4) The RF classifier has great stability and robustness, which achieve an overall accuracy (OA) of 90.65%, 93.50%, and 93.09% based on the feature sets of ST1 period, TFFS, and TOFS, respectively. [ABSTRACT FROM AUTHOR]