Highlights What are the main findings? Nine deep learning models were compared, with four demonstrating excellent performance in sparse forests identification: U-Net, U-Net++, MA-Net, and DeepLabV3+ all achieved IoU values exceeding 75%, making them suitable for sparse target extraction. Populus euphraticaA Sparse forests Segmentation Network (SPS-Net) is proposed, which integrates multi-scale feature extraction and residual connections, achieving an IoU of 80%. What is the implication of the main finding? Populus euphraticaThis study provides a fine identification solution for sparse forests by integrating GF-2 satellite imagery and deep learning models.Highlights What are the main findings? Nine deep learning models were compared, with four demonstrating excellent performance in sparse forests identification: U-Net, U-Net++, MA-Net, and DeepLabV3+ all achieved IoU values exceeding 75%, making them suitable for sparse target extraction. Populus euphraticaA Sparse forests Segmentation Network (SPS-Net) is proposed, which integrates multi-scale feature extraction and residual connections, achieving an IoU of 80%. What is the implication of the main finding? Populus euphraticaThis study provides a fine identification solution for sparse forests by integrating GF-2 satellite imagery and deep learning models.Abstract is a critical constructive species in arid desert regions, serving as a "natural barrier" for oasis protection. The sustainable management of forests is directly related to regional ecological security, and the fine identification of sparse forests is essential for the conservation of natural forests. Currently, most mapping studies on distribution focus on the extraction of dense, contiguous forests, with insufficient attention paid to the identification of sparse forests. This study utilizes Gaofen-2 (GF-2) satellite imagery as the data source and takes a typical sparse forests distribution area in the Tarim River Basin as the study site. It systematically evaluates the performance of nine mainstream deep learning models, including U-Net, DeepLabV3+, and SegFormer, in the task of sparse forests identification. The results indicate that: (1) The false-color sample set, synthesized from near-infrared, red, and green bands, contributes to improved model accuracy. Compared to the true-color (red, green, blue bands) dataset, the average Intersection over Union (IoU) of the nine models shows a relative improvement of approximately 20%. (2) For the sparse forests identification task based on the false-color dataset, four models-U-Net, U-Net++, MA-Net, and DeepLabV3+-exhibited excellent performance, with IoU exceeding 75%. (3) Using U-Net as the baseline model, this study integrated the max-pooling indices mechanism, atrous spatial pyramid pooling, and residual connection modules to construct a semantic segmentation network tailored for sparse forests, named Sparse Segmentation Network (SPS-Net). This model achieved an IoU of 80%, a relative improvement of approximately 6.3% over the baseline model, and demonstrated good stability in large-scale classification tests. The identification scheme for sparse forests constructed using GF-2 imagery and deep learning models proposed in this study can provide effective technical support for the refined monitoring and protection of natural forests.