Timely and accurate information of cotton planting areas is essential for monitoring and managing cotton fields. However, there is no large-scale and high-resolution method suitable for mapping cotton fields, and the problems associated with low resolution and poor timeliness need to be solved. Here, we proposed a new framework for mapping cotton fields based on Sentinel-1/2 data for different phenological periods, random forest classifiers, and the multi-scale image segmentation method. A cotton field map for 2019 at a spatial resolution of 10 m was generated for northern Xinjiang, a dominant cotton planting region in China. The overall accuracy and kappa coefficient of the map were 0.932 and 0.813, respectively. The results showed that the boll opening stage was the best phenological phase for mapping cotton fields and the cotton fields was identified most accurately at the early boll opening stage, about 40 days before harvest. Additionally, Sentinel-1 and the red edge bands in Sentinel-2 are important for cotton field mapping, and there is great potential for the fusion of optical images and microwave images in crop mapping. This study provides an effective approach for high-resolution and high-accuracy cotton field mapping, which is vital for sustainable monitoring and management of cotton planting.

Future passive optical network (PON) should combine various multiplexing method to realize larger-scale access. In this paper, mode division multiplexed (MDM) as a new multiplexing dimension is proposed, in which both the transmission few-mode fiber and the mode multiplexer/demultiplexer have low mode-crosstalk so that no multi-input-multi-output (MIMO) digital signal processing is required at the receiver. We show by experiments the feasibility of MDM-PON and hybrid MDM-TDM-PON. 10-Gb/s on-off keying (OOK) signals are successfully transmitted over 1.8-km 2-mode fiber without MIMO processing.

To achieve the “30×30” conservation target, Other Effective Area Conservation Measures (OECMs) serve as an innovative conservation approach that can provide a critical supplement to existing protected areas. However, related research and practice remained in their infancy, with particularly limited exploration in urban ecosystems. To address the challenge of ongoing biodiversity loss amid rapid urbanization, this study took the biodiversity-rich Yunnan Province in China as an example. Utilizing the International Union for Conservation of Nature (IUCN) criteria for identifying OECMs, we constructed a systematic identification framework for potential OECMs in urban areas. This framework assessed conservation value across three dimensions: habitat importance, ecosystem services, and functional connectivity. We employed the Zonation model for spatial conservation prioritization and identified potential OECMs within the top 30% conservation priority areas by selecting urban units with clear spatial boundaries and management entities. The result showed that 104 potential urban OECMs were identified in Yunnan, predominantly distributed in Kunming, Dali, and Qujing. These sites were primarily small-to-medium-sized patches averaging approximately 0.29 km². Urban parks and campuses constituted the main types. High-connectivity patches are predominantly larger campuses, while patches with high habitat importance and ecosystem services were mainly small-to-medium-sized parks. Based on the categorical characteristics of these potential OECMs, the study further recommended tailored management strategies to promote long-term and effective urban biodiversity conservation. By focusing the OECMs identification framework on urban areas, this study provided an operational approach for implementing conservation goals within highly artificial landscapes and presented a new practical pathway toward achieving the “30×30” target.