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Annual monitoring of the spatial distribution of cultivated land is important for maintaining the ecological environment, achieving a status quo of land resource management, and guaranteeing agricultural production. With the gradual development of remote sensing technology, it has become a common practice to obtain cultivated land boundary information on a large scale with the help of satellite Earth observation images. Traditional land use classification methods are affected by multiple types of land cover, which leads to a decrease in the accuracy of cultivated land mapping. In contrast, although the current advanced methods (such as deep learning) can obtain more accurate cultivated land mapping results than traditional methods, such methods often require the use of a massive amount of training samples, large computing power, and highly complex model tuning processes, increasing the cost of mapping and requiring the involvement of more professionals. This has hindered the promotion of related methods in mapping institutions. This paper proposes a method based on time series vector features (MTVF), which uses vector thinking to establish the features. The advantage of this method is that the introduction of vector features enlarges the differences between the different land cover types, which overcomes the loss of mapping accuracy caused by the influences of the spectra of different ground objects and ensures the calculation efficiency. Moreover, the MTVF uses a traditional method (random forest) as the classification core, which makes the MTVF less demanding than advanced methods in terms of the number of training samples. Sentinel-2 satellite images were used to carry out cultivated land mapping for 2020 in northern Henan Province, China. The results show that the MTVF has the potential to accurately identify cultivated land. Furthermore, the overall accuracy, producer accuracy, and user accuracy of the overall study area and four sub-study areas were all greater than 90%. In addition, the cultivated land mapping accuracy of the MTVF is significantly better than that of the maximum likelihood, support vector machine, and artificial neural network methods. [ABSTRACT FROM AUTHOR]

• The model optimization scheme is presented from parameter selection. • This study is also applicable to built-up, water, grassland and forest information. • U-Net is not limited by the number of bands and indicators. • Patch size is an important parameter affecting deep learning classification. Accurate acquisition of cultivated land area and location information is of great significance to agricultural management, agro-ecological environment monitoring, and national food security. The rapid development of deep learning technology provides a new way to extract cultivated land information. However, there are many parameters involved in deep learning, so it is time-consuming to find the optimal parameters. In order to simplify the complex parameter tuning process and explore the main parameters that affect the classification accuracy of deep learning, this study uses Gaofen-2 images as data support and based on U-Net semantic segmentation model to carry out an experiment of cultivated land extraction in Henan Province, China. This study designs model training in different scenarios based on four bands(near-infrared (Nir), red, green, and blue), three backbone models, and six patch sizes (pixel resolution), and analyzes the training model and classification results from multiple perspectives such as model training time, sample size, epoch, and classification categories and quantity. After analysis and evaluation, we conducted cultivated land extraction experiments in three years and different phenological periods, and finally tested the performance of U-Net, PSPNet, Deeplab, and Random Forest in cultivated land extraction. The results show that: (1) the patch size in the model has the greatest influence on the classification accuracy, which is affected by image resolution, geographic objects and batch size. When the patch size was smaller than 112 × 112 pixels, the model recall and F1 decreased substantially, with a decrease of 23.91% and 22.64%, respectively. (2) For U-Net model, after setting reasonable band combination, backbone model, patch size, epoch and sample category, the segmentation and classification accuracy of predicted results increased by 3.27% (Intersection over Union, IoU), 2.62%(overall accuracy, OA), 3.89%(F1) and 7.89%(Kappa) on average. (3) We also determined the optimal band combination (Nir, Red, and Green), patch size (224 × 224 pixels), and backbone (Resnet34) for cultivated land extraction from Gaofen-2 images. The appropriate patch size of cultivated land extraction in the study is between 224 × 224 and 256 × 256 pixels. (4) For cultivated land extraction, relying only on binary classification is not as effective as multi-classification. [ABSTRACT FROM AUTHOR]

The aim of this study was to explore the differences in the accuracy of winter wheat identification using remote sensing data at different growth stages using the same methods. Part of northern Henan Province, China was taken as the study area, and the winter wheat growth cycle was divided into five periods (seeding-tillering, overwintering, reviving, jointing-heading, and flowering-maturing) based on monitoring data obtained from agrometeorological stations. With the help of the Google Earth Engine (GEE) platform, the separability between winter wheat and other land cover types was analyzed and compared using the Jeffries-Matusita (J-M) distance method. Spectral features, vegetation index, water index, building index, texture features, and terrain features were generated from Sentinel-2 remote sensing images at different growth periods, and then were used to establish a random forest classification and extraction model. A deep U-Net semantic segmentation model based on the red, green, blue, and near-infrared bands of Sentinel-2 imagery was also established. By combining models with field data, the identification of winter wheat was carried out and the difference between the accuracy of the identification in the five growth periods was analyzed. The experimental results show that, using the random forest classification method, the best separability between winter wheat and the other land cover types was achieved during the jointing-heading period: the overall identification accuracy for the winter wheat was then highest at 96.90% and the kappa coefficient was 0.96. Using the deep-learning classification method, it was also found that the semantic segmentation accuracy of winter wheat and the model performance were best during the jointing-heading period: a precision, recall, F1 score, accuracy, and IoU of 0.94, 0.93, 0.93, and 0.88, respectively, were achieved for this period. Based on municipal statistical data for winter wheat, the accuracy of the extraction of the winter wheat area using the two methods was 96.72% and 88.44%, respectively. Both methods show that the jointing-heading period is the best period for identifying winter wheat using remote sensing and that the identification made during this period is reliable. The results of this study provide a scientific basis for accurately obtaining the area planted with winter wheat and for further studies into winter wheat growth monitoring and yield estimation. [ABSTRACT FROM AUTHOR]