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Frequent agricultural activities in farmland ecosystems bring challenges to crop information extraction from remote sensing (RS) imagery. The accurate spatiotemporal information of crops serves for regional decision support and ecological assessment, such as disaster monitoring and carbon sequestration. Most traditional machine learning algorithms are not appropriate for prediction classification due to the lack of historical ground samples and poor model transfer capabilities. Therefore, a transferable learning model including spatiotemporal capability was developed based on the UNet++ model by integrating feature fusion and upsampling of small samples for Sentinel-2A imagery. Classification experiments were conducted for 10 categories from 2019 to 2021 in Xinxiang City, Henan Province. The feature fusion and upsampling methods improved the performance of the UNet++ model, showing lower joint loss and higher mean intersection over union (mIoU) values. Compared with the UNet, DeepLab V3+, and the pyramid scene parsing network (PSPNet), the improved UNet++ model exhibits the best performance, with a joint loss of 0.432 and a mIoU of 0.871. Moreover, the overall accuracy and macro F1 values of prediction classification results based on the UNet++ model are higher than 83% and 58%, respectively. Based on the reclassification rules, about 3.48% of the farmland was damaged in 2021 due to continuous precipitation. The carbon sequestration of five crops (including corn, peanuts, soybean, rice, and other crops) is estimated, with a total carbon sequestration of 2460.56, 2549.16, and 1814.07 thousand tons in 2019, 2020, and 2021, respectively. The classification accuracy indicates that the improved model exhibits a better feature extraction and transferable learning capability in complex agricultural areas. This study provides a strategy for RS semantic segmentation and carbon sequestration estimation of crops based on a deep learning network. [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]

Numerical simulation was performed to predict the flow field parameters in an agitating tank. The relationship between characteristic parameters, impeller size, and agitating speed was studied. The conditioning and flotation tests were conducted by using fine coal from Henan province in China. Based on the simulation results, the effects of velocity, shear rate, and power consumption on conditioning and flotation processes were discussed. Maximum shear rate had more notable influence on fine coal flotation indices than fluid velocity and average shear rate. The agitating mode of smaller diameter with higher speed had better performance and lower power consumption than larger diameter with lower speed for fine coal conditioning. Particle size analysis indicated that the combustible recovery and flotation efficiency of the − 0.074 mm fraction significantly increased with the increase of maximum shear rate. [ABSTRACT FROM AUTHOR]