Dataset Information

CARM30: China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 using multi-source data.

ABSTRACT: Rapeseed is a critical cash crop globally, and understanding its distribution can assist in refined agricultural management, ensuring a sustainable vegetable oil supply, and informing government decisions. China is the leading consumer and third-largest producer of rapeseed. However, there is a lack of widely available, long-term, and large-scale remotely sensed maps on rapeseed cultivation in China. Here this study utilizes multi-source data such as satellite images, GLDAS environmental variables, land cover maps, and terrain data to create the China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 (CARM30). Our product was validated using independent samples and showed average F1 scores of 0.869 and 0.971 for winter and spring rapeseed. The CARM30 has high spatial consistency with existing 10 m and 20 m rapeseed maps. Additionally, the CARM30-derived rapeseed planted area was significantly correlated with agricultural statistics (R² = 0.65-0.86; p < 0.001). The obtained rapeseed distribution information can serve as a reference for stakeholders such as farmers, scientific communities, and decision-makers.

SUBMITTER: Liu W

PROVIDER: S-EPMC11001952 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

CARM30: China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 using multi-source data.

Liu Wenbin W Li Shu S Tao Jianbin J Liu Xiangyu X Yin Guoying G Xia Yu Y Wang Ting T Zhang Hongyan H

Scientific data 20240408 1

Rapeseed is a critical cash crop globally, and understanding its distribution can assist in refined agricultural management, ensuring a sustainable vegetable oil supply, and informing government decisions. China is the leading consumer and third-largest producer of rapeseed. However, there is a lack of widely available, long-term, and large-scale remotely sensed maps on rapeseed cultivation in China. Here this study utilizes multi-source data such as satellite images, GLDAS environmental variabl ...[more]

PMID: 38589398

Similar Datasets

Project description:Mapping the characteristics of Africa's smallholder-dominated croplands, including the sizes and numbers of fields, can provide critical insights into food security and a range of other socioeconomic and environmental concerns. However, accurately mapping these systems is difficult because there is 1) a spatial and temporal mismatch between satellite sensors and smallholder fields, and 2) a lack of high-quality labels needed to train and assess machine learning classifiers. We developed an approach designed to address these two problems, and used it to map Ghana's croplands. To overcome the spatio-temporal mismatch, we converted daily, high resolution imagery into two cloud-free composites (the primary growing season and subsequent dry season) covering the 2018 agricultural year, providing a seasonal contrast that helps to improve classification accuracy. To address the problem of label availability, we created a platform that rigorously assesses and minimizes label error, and used it to iteratively train a Random Forests classifier with active learning, which identifies the most informative training sample based on prediction uncertainty. Minimizing label errors improved model F1 scores by up to 25%. Active learning increased F1 scores by an average of 9.1% between first and last training iterations, and 2.3% more than models trained with randomly selected labels. We used the resulting 3.7 m map of cropland probabilities within a segmentation algorithm to delineate crop field boundaries. Using an independent map reference sample (n = 1,207), we found that the cropland probability and field boundary maps had respective overall accuracies of 88 and 86.7%, user's accuracies for the cropland class of 61.2 and 78.9%, and producer's accuracies of 67.3 and 58.2%. An unbiased area estimate calculated from the map reference sample indicates that cropland covers 17.1% (15.4-18.9%) of Ghana. Using the most accurate validation labels to correct for biases in the segmented field boundaries map, we estimated that the average size and total number of field in Ghana are 1.73 ha and 1,662,281, respectively. Our results demonstrate an adaptable and transferable approach for developing annual, country-scale maps of crop field boundaries, with several features that effectively mitigate the errors inherent in remote sensing of smallholder-dominated agriculture.

Project description:Dengue, a vector-borne disease driven by climate change, urbanization, and the adaptation of its vector, Aedes aegypti, poses a significant public health challenge. This study analyzed 23 years (2000-2022) of epidemiological data from Peru to examine the temporal and spatial drivers influencing dengue reported cases. Using secondary data from the Peruvian Ministry of Health, 501,027 cases were stratified by clinical severity, gender, geographic distribution, and temporal trends. The Amazonian and coastal regions, particularly Loreto, Ucayali, and Piura, bore the highest burden, collectively accounting for more than 60% of cases during epidemic years. Seasonal spikes in transmission consistently aligned with the rainy season, underscoring the dependence of Aedes aegypti proliferation on climatic conditions. The analysis revealed progressive geographic expansion of dengue into previously low-risk areas, including Andean highlands and peri-urban zones, driven by climatic shifts and unregulated urbanization. Gender-based comparisons indicated a higher overall case burden among females, though severe forms of dengue were unequally observed in males, particularly in rural areas. Clinical classifications highlighted that 78% of cases presented without warning signs, 18% with warning signs, and 4% as severe dengue. Socioeconomic factors, such as inadequate sanitation, urban slums, and water storage practices, contributed significantly to vector breeding in high-burden areas. Moreover, the role of extreme climatic events, including El Niño, exacerbated outbreak intensity and duration. The findings emphasize the urgent need for innovative approaches and provides actionable insights into regional dynamics and highlights critical areas for research, including predictive climate-disease modeling and the integration of molecular surveillance. These strategies are essential to mitigating the growing dengue burden and strengthening public health systems in Peru and similar endemic regions.

Dataset Information

CARM30: China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 using multi-source data.

Publications

CARM30: China annual rapeseed maps at 30 m spatial resolution from 2000 to 2022 using multi-source data.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets