Plastic Mulching (PM) is criticized for causing environmental problems in spite of the benefits to increase crop yield. Accurate map of Plastic-Mulched Farmland (PMF) is demanded to monitor the usage of plastic mulches and to mitigate its environmental damages. However, mapping PMF with remote sensing data over large scale is still technically challenging due to its variable spectral characteristic. This paper proposed a Random Forest (RF) workflow to map PMF by coupling multi-resource satellite observations, including high- and medium- resolution optical imageries and Synthetic Aperture Radar (SAR) imageries. Under the context of two typical PMF regions, Jizhou and Guyuan, China, we test the performance of the proposed method. Results indicated that coupling optical and SAR remote sensing data improved the mapping accuracy significantly. There were different strategies for optimal data coupling. Coupling of three data, which are the GaoFen-1 (GF-1) data, Landsat-8 data and Radarsat-2 data, generated the highest mapping accuracy. Besides, coupling the GF-1 data with SAR data produced better accuracy than coupling the Landsat-8 data with SAR data. Compared with the accuracies derived from the coupling of two data (such as coupling GF-1 data with Radarsat-2 data and coupling Landsat-8 data with Radarsat-2 data), coupling the three data improved the overall accuracies by 0.39 to 4.93%. Compared with the mapping accuracy produced from Radarsat-2 data alone, the overall accuracies generated from the optimum coupling increased by 20.96 and 26.06%, respectively, in Jizhou and Guyuan. When compared with the mapping accuracy of GF-1 data alone, the overall accuracies generated from the optimum coupling improved by 4.77% both in Jizhou and Guyuan. In general, contribution of optical remote sensing data was greater than that of SAR data for mapping PMF. Whereas, the SAR data also made great contribution to improve the mapping accuracy of PMF.

Information on land cover is vital to numerous United Nations (UN) missions, including achieving the Sustainable Development Goals (SDGs). Because land cover data are developed by a variety of organizations for a range of objectives, they are based on different classification schemes and have discrepancies. In addition, the sustainability for land cover is hampered by limited access to information and documentation. Accordingly, international standards for land cover are developed to improve interoperability between different land cover datasets. However, the use and development of land cover datasets are limited by various factors including availability of properly documented land cover legends in support of different applications including change assessment, comparison, and international reporting. The purpose of this article is to highlight the importance of land cover in achieving several goals and to introduce the first international platform for land cover legend, named Land Cover Legend Registry (LCLR). This registry is a contribution to the international land cover community and the UN in effort to promote and support data harmonization processes and interoperability from local to global level, and vice versa. Users can not only use the registry for preparing consistent datasets, but also contribute to it by providing the latest data to ensure the long-term availability of both updated and existing datasets around the world. Moreover, building on the experience developing land cover legends with different nations, a brief explanation on the preparation of legends is also provided. Additionally, it is more important than ever to develop land cover registers to support the use, expansion, integration, and use uptake of land cover data, particularly for innovative remote sensing, machine learning, and information and communication technologies and techniques that build on existing and national contexts.

The increasing area of Plastic-Mulched Farmland (PMF) is aggravating the conflict between agricultural development and environmental protection. The spatial distribution of PMF requires an effective and economic technique. However, most works are currently carried out on pixel-level algorithms, which leads inevitably to mixed spectral errors. In this connection, PMF has been mapped with Pléiades and Radarsat-2 data combining object-based image analysis (OBIA) and Random Forest (RF). At first, through visual interpretation, the outcomes of various segmenting scenarios were used to select the optimum segmentation parameters. The spectral characteristics, textural and geometric features were then extracted and tailored to the best PMF mapping function subset. Finally, we map the PMF using the optimized object-level feature subset based on RF. The results show that the ability of Pléiades data to map PMF in Northern China is higher than that of Radarsat-2. The overall mapping accuracy achieved is 90.27%. In general, the precision and reliability of the mapping are the product of extensive structural data and object-level features that can reduce the reliance on spectral data.

Accurate mapping of rice cultivation is vital for ensuring food security, reducing greenhouse gas emissions, and achieving sustainable development goals. However, large-scale deep learning–based crop mapping remains limited due to the demand for vast, uniformly distributed, high-quality samples. To address this challenge, we propose a Progressive Deep Learning Crop Mapping (PDLCM) framework for national-scale, high-resolution rice mapping. Beginning with a small set of localized rice and non-rice samples, PDLCM progressively refines model performance through iterative enhancement of positive and negative samples, effectively mitigating sample scarcity and spatial heterogeneity. By combining time-series Sentinel-2 optical data with Sentinel-1 synthetic aperture radar imagery, the framework captures distinctive phenological characteristics of rice while resolving spatiotemporal inconsistencies in large datasets. Applying PDLCM, we produced 10 m rice maps from 2022 to 2024 across the middle and lower Yangtze River Basin, covering more than one million square kilometers. The results achieved an overall accuracy of 96.8% and an F1 score of 0.88, demonstrating strong spatial and temporal generalization. All datasets and source codes are publicly accessible, supporting SDG 2 and providing a transferable paradigm for operational large-scale crop mapping.