Opium poppies are a major source of traditional drugs, which are not only harmful to physical and mental health, but also threaten the economy and society. Monitoring poppy cultivation in key regions through remote sensing is therefore a crucial task; the location coordinates of poppy parcels represent particularly important information for their eradication by local governments. We propose a new methodology based on deep learning target detection to identify the location of poppy parcels and map their spatial distribution. We first make six training datasets with different band combinations and slide window sizes using two ZiYuan3 (ZY3) remote sensing images and separately train the single shot multibox detector (SSD) model. Then, we choose the best model and test its performance using 225 km² verification images from Lao People’s Democratic Republic (Lao PDR), which exhibits a precision of 95% for a recall of 85%. The speed of our method is 4.5 km²/s on 1080TI Graphics Processing Unit (GPU). This study is the first attempt to monitor opium poppies with the deep learning method and achieve a high recognition rate. Our method does not require manual feature extraction and provides an alternative way to rapidly obtain the exact location coordinates of opium poppy cultivation patches.

This paper investigates the application of multipath mitigation algorithms in the context of Global Navigation Satellite Systems receivers. We resort to particle filtering to track the evolving parameters of either the line-of-sight signal and its multipath replicas. The considered architecture consists of a multicorrelation scheme, focused on the tracking of one of the visible satellites. In contrast to other works, here we explore the use of antenna arrays for the problem and setup at hand. Particularly, we study and compare two signal models: (i) one explicitly including the multipath replicas and (ii) another which includes the contribution of multipath in the noise term. The concept is assessed by computer simulations.

Center-pivot irrigated cropland (CPIC) is a large-scale, intensive agricultural irrigation practice with high water-use efficiency. This characteristic enables CPIC to increase crop yields and promote agricultural development, while simultaneously causing groundwater overexploitation and subsequent environmental issues. Mapping the global distribution of CPIC is critical for enhancing water resource management, guiding agricultural policies, and assessing ecological impacts. This study developed a novel methodological framework integrate CPIC prior knowledge into the generation of a global training dataset through designed algorithms, ultimately producing a 2020 global CPIC map (GCMap) based on Landsat imagery. Specifically, we first proposed a shape-prior guided Projection Simulation (PS) to enhance the diversity of national-scale CPIC samples, enabling global CPIC pseudo-label generation through a teacher model. Subsequently, a stratified sampling strategy was implemented to improving sample representativeness across different regions using the pseudo-labels of global CPIC. Furthermore, a dual-temporal correction mechanism was proposed to clean sample labels, ultimately generating high-confidence global training samples. Finally, based on this dataset, a student model was trained using a noise-resistant loss function and deployed on cloud computing platforms to generate the GCMap rapidly. Compared to existing CPIC products, our GCMap achieves superior Overall Accuracy (OA=0.95) and significantly higher Producer’s Accuracy (PA=0.91), with substantially reduced omission errors. Our results reveal that global CPIC extent has been significantly underestimated in previous studies. Our estimate of 16.44±2.37 million hectares (Mha) of CPIC in 2020 is 43% higher than prior reports (11.5 Mha). Geographically, the semi-arid zone has the largest share of CPIC, covering 8.31±1.17 Mha (50.55% of the total), followed by the arid zone (4.05±0.61 Mha, 24.63%) and humid zone (2.44±0.33 Mha, 14.83%). Further analysis indicates that most CPIC is distributed in aquifers with limited underwater recharge. Approximately 13.64±1.98 Mha (82.95%) of CPIC areas are located in regions with recharge rates below 100 mm/year, of which 9.00±1.30 Mha (54.74% of total CPIC) fall within extremely low recharge zones (<20 mm/year). These findings provide an essential foundation for further research on irrigation-groundwater interactions and sustainable policymaking about agricultural development and water usage. Our GCMap inventory is publicly available in an online repository at: https://doi.org/10.12436/GCMap.62.20250711.

Center-pivot irrigated cropland (CPIC) is a large-scale, intensive agricultural irrigation practice with high water-use efficiency. This characteristic enables CPIC to increase crop yields and promote agricultural development, while simultaneously causing groundwater overexploitation and subsequent environmental issues. Mapping the global distribution of CPIC is critical for enhancing water resource management, guiding agricultural policies, and assessing ecological impacts. This study developed a novel methodological framework integrate CPIC prior knowledge into the generation of a global training dataset through designed algorithms, ultimately producing a 2020 global CPIC map (GCMap) based on Landsat imagery. Specifically, we first proposed a shape-prior guided Projection Simulation (PS) to enhance the diversity of national-scale CPIC samples, enabling global CPIC pseudo-label generation through a teacher model. Subsequently, a stratified sampling strategy was implemented to improving sample representativeness across different regions using the pseudo-labels of global CPIC. Furthermore, a dual-temporal correction mechanism was proposed to clean sample labels, ultimately generating high-confidence global training samples. Finally, based on this dataset, a student model was trained using a noise-resistant loss function and deployed on cloud computing platforms to generate the GCMap rapidly. Compared to existing CPIC products, our GCMap achieves superior Overall Accuracy (OA=0.95) and significantly higher Producer’s Accuracy (PA=0.91), with substantially reduced omission errors. Our results reveal that global CPIC extent has been significantly underestimated in previous studies. Our estimate of 16.44±2.37 million hectares (Mha) of CPIC in 2020 is 43% higher than prior reports (11.5 Mha). Geographically, the semi-arid zone has the largest share of CPIC, covering 8.31±1.17 Mha (50.55% of the total), followed by the arid zone (4.05±0.61 Mha, 24.63%) and humid zone (2.44±0.33 Mha, 14.83%). Further analysis indicates that most CPIC is distributed in aquifers with limited underwater recharge. Approximately 13.64±1.98 Mha (82.95%) of CPIC areas are located in regions with recharge rates below 100 mm/year, of which 9.00±1.30 Mha (54.74% of total CPIC) fall within extremely low recharge zones (<20 mm/year). These findings provide an essential foundation for further research on irrigation-groundwater interactions and sustainable policymaking about agricultural development and water usage. Our GCMap inventory is publicly available in an online repository at: https://doi.org/10.12436/GCMap.62.20250711.