Crop-type mapping is the foundation of grain security and digital agricultural management. Accuracy, efficiency and large-scale scene consistency are required to perform crop classification from remote sensing images. Many current remote-sensing crop extraction methods based on deep learning cannot account for adaptation effects in large-scale, complex scenes. Therefore, this study proposes a novel adaptive feature-fusion network for crop classification using single-temporal Sentinel-2 images. The selective patch module implemented in the network can adaptively integrate the features of different patch sizes to assess complex scenes better. TabNet was used simultaneously to extract spectral information from the center pixels of the patches. Multitask learning was used to supervise the extraction process to improve the weight of the spectral characteristics while mitigating the negative impact of a small sample size. In the network, superpixel optimization was applied to post-process the classification results to improve the crop edges. By conducting the crop classification of peanut, rice, and corn based on Sentinel-2 images in 2022 in Henan Province, China, the novel method proposed in this paper was more accurate, indicated by an F1 score of 96.53%, than other mainstream methods. This indicates our model’s potential for application in crop classification in large scenes.

Accurate crop mapping can represent the fundamental data for digital agriculture and ecological security. However, current crop classification methods perform poorly in mountainous areas with small cropland field parcel areas and multiple crops under cultivation. This study proposed a new object-oriented classification method to address this issue, using multi-source data and object features to achieve multi-crop classification in mountainous areas. Firstly, a deep learning method was employed to extract cropland field parcels in mountainous areas. Subsequently, the fusion of multi-source data was carried out based on cropland field parcels, while object features tailored for mountainous crops were designed for crop classification. Comparative analysis indicates that the proposed classification method demonstrates exceptional performance, enabling accurate mapping of various crops in mountainous regions. The F1 score and overall accuracy (OA) of the proposed method are 0.8449 and 0.8502, representing a 10% improvement over the pixel-based random forest classification results. Furthermore, qualitative analysis reveals that the proposed method exhibits higher classification accuracy for smaller plots and more precise delineation of crop boundaries. Finally, meticulous crop mapping of corn, sorghum, rice, and other crops in Xishui County, Guizhou Province, demonstrates the significant potential of the proposed method in crop classification within mountainous scenarios.

Change detection from heterogeneous satellite and aerial images plays a progressively important role in many fields, including disaster assessment, urban construction, and land use monitoring. Currently, researchers have mainly devoted their attention to change detection using homologous image pairs and achieved many remarkable results. It is sometimes necessary to use heterogeneous images for change detection in practical scenarios due to missing images, emergency situations, and cloud and fog occlusion. However, heterogeneous change detection still faces great challenges, especially using satellite and aerial images. The main challenges in satellite and aerial image change detection are related to the resolution gap and blurred edge. Previous studies used interpolation or shallow feature alignment before traditional homologous change detection methods, which ignored the high-level feature interaction and edge information. Therefore, we propose a new heterogeneous change detection model based on multimodal transformers combined with edge guidance. In order to alleviate the resolution gap between satellite and aerial images, we design an improved spatially aligned transformer (SP-T) with a sub-pixel module to align the satellite features to the same size of the aerial ones supervised by a token loss. Moreover, we introduce an edge detection branch to guide change features using the object edge with an auxiliary edge-change loss. Finally, we conduct considerable experiments to verify the effectiveness and superiority of our proposed model (EGMT-CD) on a new satellite–aerial heterogeneous change dataset, named SACD. The experiments show that our method (EGMT-CD) outperforms many previously superior change detection methods and fully demonstrates its potential in heterogeneous change detection from satellite–aerial images.

Northern Slopes of Tianshan Mountain (NSTM) in Xinjiang hold significance as a principal agricultural hub within the region’s arid zone. Accurate crop mapping across vast agricultural expanses is fundamental for intelligent crop monitoring and devising sustainable agricultural strategies. Previous studies on multi-temporal crop classification have predominantly focused on single-point pixel temporal features, often neglecting spatial data. In large-scale crop classification tasks, by using spatial information around the pixel, the contextual relationships of the crop can be obtained to reduce possible noise interference. This research introduces a multi-scale, multi-temporal classification framework centered on ConvGRU (convolutional gated recurrent unit). By leveraging the attention mechanism of the Strip Pooling Module (SPM), a multi-scale spatial feature extraction module has been designed. This module accentuates vital spatial and spectral features, enhancing the clarity of crop edges and reducing misclassifications. The temporal information fusion module integration features from various periods to bolster classification precision. Using Sentinel-2 imagery spanning May to October 2022, datasets for cotton, corn, and winter wheat of the NSTM were generated for the framework’s training and validation. The results demonstrate an impressive 93.03% accuracy for 10 m resolution crop mapping using 15-day interval, 12-band Sentinel-2 data for the three crops. This method outperforms other mainstream methods like Random Forest (RF), Long Short-Term Memory (LSTM), Transformer, and Temporal Convolutional Neural Network (TempCNN), showcasing a kappa coefficient of 0.9062, 7.52% and 2.42% improvement in Overall Accuracy compared to RF and LSTM, respectively, which demonstrate the potential of our model for large-scale crop classification tasks to enable high-resolution crop mapping on the NSTM.

Cotton (Gossypium hirsutum L.) is one of the main crops in Uzbekistan, which makes a major contribution to the country’s economy. The cotton industry has played a pivotal role in the economic landscape of Uzbekistan for decades, generating employment opportunities and supporting the livelihoods of countless individuals across the country. Therefore, having precise and up-to-date data on cotton cultivation areas is crucial for overseeing and effectively managing cotton fields. Nonetheless, there is currently no extensive, high-resolution approach that is appropriate for mapping cotton fields on a large scale, and it is necessary to address the issues related to the absence of ground-truth data, inadequate resolution, and timeliness. In this study, we introduced an effective approach for automatically mapping cotton fields on a large scale. A crop-type mapping method based on phenology was conducted to map cotton fields across the country. This research affirms the significance of phenological metrics in enhancing the mapping of cotton fields during the growing season in Uzbekistan. We used an adaptive feature-fusion network for crop classification using single-temporal Sentinel-2 images and automatically generated samples. The map achieved an overall accuracy (OA) of 0.947 and a kappa coefficient (KC) of 0.795. This model can be integrated with additional datasets to predict yield based on the identified crop type, thereby enhancing decision-making processes related to supply chain logistics and seasonal production forecasts. The early boll opening stage, occurring approximately a little more than a month before harvest, yielded the most precise identification of cotton fields.

Addressing the demands of agricultural resource digitization and facility crop monitoring, precise extraction of plastic greenhouses using high-resolution remote sensing imagery demonstrates pivotal significance for implementing refined farmland management. However, the complex spatial topological relationships among densely arranged greenhouses and the spectral confusion of ground objects within agricultural backgrounds limit the effectiveness of conventional methods in the large-scale, precise extraction of plastic greenhouses. This study constructs an Individual Plastic Greenhouse Extraction Network (IPGENet) by integrating a multi-scale feature fusion decoder with the Swin-UNet architecture to improve the accuracy of large-scale individual plastic greenhouse extraction. To ensure sample accuracy while reducing manual labor costs, an iterative sampling approach is proposed to rapidly expand a small sample set into a large-scale dataset. Using GF-2 satellite imagery data in Shandong Province, China, the model realized large-scale mapping of individual plastic greenhouse extraction results. In addition to large-scale sub-meter extraction and mapping, the study conducted quantitative and spatial statistical analyses of extraction results across cities in Shandong Province, revealing regional disparities in plastic greenhouse development and providing a novel technical approach for large-scale plastic greenhouse mapping.