Search

Accurate spatial information for agricultural field parcels is important for agricultural production management and understanding agro-industrialization and intensification. However, traditional remote sensing methods that rely on single-modal or single-date data struggle to identify heterogeneous field parcels, particularly in regions dominated by smallholder farming systems. To address this challenge, we proposed a Dual branch Spatiotemporal Fusion Network (DSTFNet) that integrated very high-resolution (VHR) images and medium-resolution satellite image time series (MRSITS) to extract agricultural field parcels over various landscapes. The DSTFNet consisted of two branches: a spatial branch that extracted spatial features from VHR images and a temporal branch that explored seasonal spectral dynamics from MRSITS data by using ConvLSTM units and an attention module. We evaluated the DSTFNet in four regions across China by using GF-2 and Sentinel-2 data. The results showed that DSTFNet performed well in delineating agricultural field parcels, achieving the highest Matthew's correlation coefficient (MCC) = 0.823 for the field extent, the highest F1-score of edge (F edge) = 0.865 for field boundary, and the lowest errors of segmentation evaluation index (SEI) = 0.191 for the vectorized field parcels in Hubei province. In addition, DSTFNet significantly outperformed two single-branch models that used VHR or MRSITS alone, as well as existing BsiNet, ResUNet_a, UNet and RAUNet models. DSTFNet also showed good spatial transferability in distinct regions without training data (on average, MCC = 0.728, F edge = 0.729, and SEI = 0.281 for three target regions). Using limited training data to fine-tune the DSTFNet can further improve its ability to delineate field parcels over complex regions. The visualization analysis of temporal attention weights demonstrated that DSTFNet can well capture cropland spectral dynamics, making it advantageous in extracting diverse cropland parcels. By exploiting important spectral, spatial and temporal information from multimodal satellite data, DSTFNet provided an effective, robust, and transferable solution for accurately delineating agricultural field parcels across heterogeneous farming systems. [ABSTRACT FROM AUTHOR]

Accurate information on crop type spatial distributions is crucial for yield estimation, agroecological modeling, and agrarian policy development. In comparison with moderate resolution (10–30 m) satellite images, very high-resolution (VHR, ≤ 1 m) images are superior at describing the spatial details and complex texture features of agricultural land. However, VHR images generally suffer from insufficient spatiotemporal coverage and high spectral variation in crop type, making fine-scale crop type mapping at regional or larger scales challenging. In this study, we developed a novel data-driven residual attention U-shape network (RAUNet) to identify crop types at the regional scale based on VHR images. RAUNet was adapted from UNet by introducing a residual module and a gated attention mechanism to strengthen the multilevel representation of crop features from VHR images. We selected rice-crayfish fields (RCFs) in Hubei Province, China, as the case crop type and area to test the performance of RAUNet. Seven models, including RAUNet pan , ResUNet, AttnUNet, UNet, DeepLabV3 + and random forest at the pixel and object levels, were adopted for comparison with RAUNet. Moreover, the model transferability was evaluated over regions with different agricultural landscapes. Our results showed that RAUNet-derived RCF maps obtained an average F1-score and MCC of 0.90 and 0.85 at different cultivation stages, which significantly outperformed other methods. Additionally, RAUNet had good spatiotemporal transferability, with an average F1-score and MCC of 0.93 and 0.88 in four transfer areas. The visualization results of the attention weights indicated RAUNet's prominent capability in combining useful and relevant information from different feature levels to produce refinement predictions for the target crop type. Furthermore, the high-level features derived from RAUNet can well separate RCFs and non-RCFs over different landscapes, which were also consistent across different cultivation periods. These encouraging results suggest that RAUNet works well for fine-scale crop type mapping in smallholder farming systems, and it is an efficient method for land cover mapping based on VHR images at the regional scale. [ABSTRACT FROM AUTHOR]