China is one of the countries with the largest citrus cultivation areas, and its citrus industry has received significant attention due to its substantial economic benefits. Traditional manual forestry surveys and remote sensing image classification tasks are labor-intensive and time-consuming, resulting in low efficiency. Remote sensing technology holds great potential for obtaining spatial information on citrus orchards on a large scale. This study proposes a lightweight model for citrus plantation extraction that combines the DeepLabV3+ model with the convolutional block attention module (CBAM) attention mechanism, with a focus on the phenological growth characteristics of citrus in the Guangxi region. The objective is to address issues such as inaccurate extraction of citrus edges in high-resolution images, misclassification and omissions caused by intra-class differences, as well as the large number of network parameters and long training time found in classical semantic segmentation models. To reduce parameter count and improve training speed, the MobileNetV2 lightweight network is used as a replacement for the Xception backbone network in DeepLabV3+. Additionally, the CBAM is introduced to extract citrus features more accurately and efficiently. Moreover, in consideration of the growth characteristics of citrus, this study augments the feature input with additional channels to better capture and utilize key phenological features of citrus, thereby enhancing the accuracy of citrus recognition. The results demonstrate that the improved DeepLabV3+ model exhibits high reliability in citrus recognition and extraction, achieving an overall accuracy (OA) of 96.23%, a mean pixel accuracy (mPA) of 83.79%, and a mean intersection over union (mIoU) of 85.40%. These metrics represent an improvement of 11.16%, 14.88%, and 14.98%, respectively, compared to the original DeepLabV3+ model. Furthermore, when compared to classical semantic segmentation models, such as UNet and PSPNet, the proposed model achieves higher recognition accuracy. Additionally, the improved DeepLabV3+ model demonstrates a significant reduction in both parameters and training time. Generalization experiments conducted in Nanning, Guangxi Province, further validate the model’s strong generalization capabilities. Overall, this study emphasizes extraction accuracy, reduction in parameter count, adherence to timeliness requirements, and facilitation of rapid and accurate extraction of citrus plantation areas, presenting promising application prospects.

Focusing on the trend of continuously seeking high-precision tree species classification results in small areas from the perspectives of sensors and classification algorithms. This study aimed to explore the effects of data sources, classifiers, and seasons on classification accuracy in regions with significant environmental variation, examining patterns of tree species classification to enhance the transferability of classification. Considering two typical forest distribution regions in the north and south of China, this study utilized the revisitation cycle and open-source advantages of Sentinel-2 and Landsat-8. Leveraging the Google Earth Engine (GEE) platform, this study captured spectral features, vegetation indices, and texture features for single seasonal and seasonal combination images. With the assistance of Sentinel-1A and SRTM (Shuttle Radar Topography Mission) DEM (Digital Elevation Model), backscattering coefficient features and topographical features were extracted and input with features captured from Sentinel-2 and Landsat-8 into three types of classifiers: random forest (RF), support vector machine (SVM), and gradient tree boosting (GTB) for major tree species classification. In this research, we discovered that the best classification for single season in the northern study area was spring, whereas, for the southern study area, it was winter. Seasonal combination images effectively improved the classification accuracy of single seasonal images, with Sentinel-2 imagery displaying better classification performance compared to Landsat-8, and the optimal classifier differing between the north and the south. The inclusion of topographical or backscattering coefficient features in the four-season combination imagery contributed to improvements in classification accuracy, with topographical features significantly enhancing the classification performance in the topographically varied southern study area. The evaluation of feature importance indicated that elevation was the most critical feature for classification, while spectral features and vegetation indices were also significant. In the southern study area with large topographical discrepancies, subdividing into different terrain units led to improved tree species classification accuracy in medium-altitude, gentle slope areas. These findings provide insights into the regularity of enhancing tree species classification accuracy in environmentally diverse areas through the use of multi-source remote sensing data and multi-seasonal imagery. Consequently, the results offer a reference for the identification of tree species across large areas and the creation of spatial distribution maps.