Timely and accurate crop classification is of enormous significance for agriculture management. The Shiyang River Basin, an inland river basin, is one of the most prominent water resource shortage regions with intensive agriculture activities in northwestern China. However, a free crop map with high spatial resolution is not available in the Shiyang River Basin. The European Space Agency (ESA) satellite Sentinel-2 has multi-spectral bands ranging in the visible-red edge-near infrared-shortwave infrared (VIS-RE-NIR-SWIR) spectrum. Understanding the impact of spectral-temporal information on crop classification is helpful for users to select optimized spectral bands combinations and temporal window in crop mapping when using Sentinel-2 data. In this study, multi-temporal Sentinel-2 data acquired in the growing season in 2019 were applied to the random forest algorithm to generate the crop classification map at 10 m spatial resolution for the Shiyang River Basin. Four experiments with different combinations of feature sets were carried out to explore which Sentinel-2 information was more effective for higher crop classification accuracy. The results showed that the augment of multi-spectral and multi-temporal information of Sentinel-2 improved the accuracy of crop classification remarkably, and the improvement was firmly related to strategies of feature selections. Compared with other bands, red-edge band 1 (RE-1) and shortwave-infrared band 1 (SWIR-1) of Sentinel-2 showed a higher competence in crop classification. The combined application of images in the early, middle and late crop growth stage is significant for achieving optimal performance. A relatively accurate classification (overall accuracy = 0.94) was obtained by utilizing the pivotal spectral bands and dates of image. In addition, a crop map with a satisfied accuracy (overall accuracy > 0.9) could be generated as early as late July. This study gave an inspiration in selecting targeted spectral bands and period of images for acquiring more accurate and timelier crop map. The proposed method could be transferred to other arid areas with similar agriculture structure and crop phenology.

     Accurate and timely spatial distribution of crops is of great significance to agricultural security issues. They are not only important parameters for regional crop growth monitoring, yield prediction, model simulation, but also important basis for government decision-making to assess food security and predict comprehensive productivity of agricultural resources. The Shiyang River Basin is the inland river basin with the highest development intensity and the most prominent water resource conflicts in arid regions of Northwest China. The timely and accurate acquisition of crop distribution information in the watershed can provide data support for alleviating the water resource conflicts in the Shiyang River Basin. However, there is no publically available in-season crop distribution information with high spatial resolution in the watershed. To solve this problem and generate accurate ,in-season crop-type map with high spatial resolution, this study uses multi-temporal sentinel2 data to generate spectrum, texture, vegetation index, red edge multi-feature sets while exploiting random forest classification algorithm(RF).the study designed Experiments to evaluate what kind of feature is most effctitve and further dig which spectral information is most important for the training RF model to map crop types in Shiyang River Basin. The experimental results show that the spectral information outperforms other features in the highest classification accuracy, followed by vegetation index information and red edge index information, and texture information has no effect on crop classification in the watershed. Temporal information has a significant impact on crop classification. the classification accuracy increases with the progression of time, and the accuracy reaches a plateau (94%)around DOY 218. The highest accuracy for single-date classification is 87%, and for multi-temporal is 94.8%. The classification performance of SWIR1 band of Sentinel2 is better than the visible and near-infrared bands commonly used in crop classification at present, and crop classification based on the Sentinel2 Visible-NIR-SWIR1 three-band combination mode can achieve the effect of full-band classification. The Visible -NIR-SWIR1 data of sentinel2 before DOY218 was used to generate the 2019 crop distribution map of Shiyang River Basin, with an overall accuracy of 93.5%.

Timely and accurate crop identification and mapping are of great significance for crop yield estimation, disaster warning, and food security. Early-season crop identification places higher demands on the quality and mining of time-series information than post-season mapping. In recent years, great strides have been made in the development of deep-learning algorithms, and the emergence of Sentinel-2 data with a higher temporal resolution has provided new opportunities for early-season crop identification. In this study, we aimed to fully exploit the potential of deep-learning algorithms and time-series Sentinel-2 data for early-season crop identification and early-season crop mapping. In this study, four classifiers, i.e., two deep-learning algorithms (one-dimensional convolutional networks and long and short-term memory networks) and two shallow machine-learning algorithms (a random forest algorithm and a support vector machine), were trained using early-season Sentinel-2 images and field samples collected in 2019. Then, these algorithms were applied to images and field samples for 2020 in the Shiyang River Basin. Twelve scenarios with different classifiers and time intervals were compared to determine the optimal combination for the earliest crop identification. The results show that: (1) the two deep-learning algorithms outperformed the two shallow machine-learning algorithms in early-season crop identification; (2) the combination of a one-dimensional convolutional network and 5-day interval time-series Sentinel-2 data outperformed the other schemes in obtaining the early-season crop identification time and achieving early mapping; and (3) the early-season crop identification mapping time in the Shiyang River Basin was identified as the end of July, and the overall classification accuracy reached 0.83. In addition, the early identification time for each crop was as follows: the wheat was in the flowering stage (mid-late June); the alfalfa was in the first harvest (mid-late June); the corn was in the early tassel stage (mid-July); the fennel and sunflower were in the flowering stage (late July); and the melons were in the fruiting stage (around late July). This study demonstrates the potential of using Sentinel-2 time-series data and deep-learning algorithms to achieve early-season crop identification, and this method is expected to provide new solutions and ideas for addressing early-season crop identification monitoring.