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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]

Soil erosion on cropland is a result of the interaction between nature and human activities. The socioeconomic influencing factors of soil erosion have been less studied than the biophysical processes and previous studies have mainly focused on the impacts of local socioeconomic factors on soil erosion in the same region. However, since agricultural activities are densely connected to other socio-economic activities, the need for agricultural products from distant regions could potentially drive local soil erosion accompanying agricultural production. To the best of our knowledge, these telecoupling effects have not been studied. Here, we combined the Revised Universal Soil Loss Equation (RUSLE) and multiregional input-output analysis (MRIO) models to quantify the contribution of China's cross-provincial economic demand to local soil erosion at the provincial, sectoral, and supply chain levels. Our results show that a large amount of soil erosion in the southwest, northeast, and central regions is linked to the economic needs across provinces. Agriculture and food processing are the most important distant driving sectors. The driving effect of household consumption on soil erosion mainly occurs on shorter supply chains, while exports and capital formation drive soil erosion through longer chains. Our results indicate that local soil erosion management must consider the impact of distant agricultural product needs and coordinate food production and supply on a national scale to protect the ecological function of the land. [Display omitted] • Cropland soil erosion was linked to a multi-regional input-output table. • 28.19% of cropland soil erosion was driven by distant final demand. • Ten provinces had more than 1/4 of their soil erosion driven by other provinces' consumption. • Agriculture, Food Processing, and Construction contributed 72.28% telecouped erosion. • The telecoupled share increases along supply chains from 19.60% (tier 0) to 33.70% (tier 6). [ABSTRACT FROM AUTHOR]