A5004933118- Remote Sensing in Agriculture
- Land Use and Ecosystem Services
- Remote Sensing and Land Use
- Climate change impacts on agriculture
- Environmental and Agricultural Sciences
- Rice Cultivation and Yield Improvement
- Remote Sensing and LiDAR Applications
- Wheat and Barley Genetics and Pathology
- Spectroscopy and Chemometric Analyses
- Genetic Mapping and Diversity in Plants and Animals
- Plant Water Relations and Carbon Dynamics
- Mycotoxins in Agriculture and Food
- Rangeland Management and Livestock Ecology
- Environmental Changes in China
Sun Yat-sen University
2022-2025
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou)
2022-2024
Beijing Institute of Big Data Research
2023
Abstract. Paddy rice is the second-largest grain crop in China and plays an important role ensuring global food security. However, there no high-resolution map of covering all China. This study developed a new rice-mapping method by combining optical synthetic aperture radar (SAR) images cloudy areas based on time-weighted dynamic time warping (TWDTW) produced distribution maps single-season 21 provincial administrative regions from 2017 to 2022 at 10 or 20 m resolution. The accuracy was...
Abstract China is the world’s second-largest maize producer, contributing 23% to global production and playing a crucial role in stabilizing supply. Therefore, accurately mapping distribution of great significance for regional food security international cereals trade. However, it still lacks long-term dataset with fine spatial resolution, because existing high resolution satellite datasets suffer from data gaps caused by cloud cover, especially humid cloudy regions. This study aimed produce...
Restricted by the design of satellite sensors, existing satellite-based Normalized Difference Vegetation Index (NDVI) cannot simultaneously have a high temporal resolution and spatial resolution, which substantially limits its applications. In recent years, several spatiotemporal fusion models been developed to produce vegetation index datasets with both resolutions, but large uncertainties remain. This study proposes model (i.e., Integrating ENvironmental VarIable model, InENVI) based on...
Grazing is a significant anthropogenic disturbance to grasslands, impacting their function and composition, affecting carbon budgets greenhouse gas emissions. However, accurate evaluations of grazing impacts are limited by the absence long-term high-resolution intensity data (i.e., number livestock per unit area). This study utilized census satellite-based vegetation index develop first Long-term High-resolution Intensity (LHGI) dataset grassland in seven pastoral provinces western China...
Abstract. Winter-triticeae crops, such as winter wheat, barley, rye and triticale, are important in human diets planted worldwide, thus accurate spatial distribution information on winter-triticeae crops is crucial for monitoring crop production food security. However, there still a lack of global high-resolution maps because the reliance existing mapping methods training samples, which limits their application at scale. In this study, we propose new method based Winter-Triticeae Crops Index...
The vegetation index is a key satellite-based variable used to monitor global distribution and growth. However, existing datasets face limitations in achieving both high spatial temporal resolution, restricting their application potential. This study revised machine learning spatiotemporal fusion model (InENVI) produce high-resolution NDVI dataset with 8-day 30 m covering China from 2001 2020. A total of 432,230 Landsat scenes were processed, enhancing data quality accuracy. was validated...
India, as the world’s second-largest rice producer, accounting for 21.7% of global production, plays a crucial role in ensuring food supply stability. However, creating high-resolution maps such those at 10 to 30 m, poses significant challenges due frequent cloudy weather conditions and complexities its agricultural systems. This study used sample-independent mapping method India using synthetic aperture radar (SAR)-based Rice Index (SPRI). We produced m spatial resolution distribution three...
The prediction of vegetation growth under climate change has become much more important in recent years, but is still a challenge. This study developed machine learning method to predict growth, indicated by satellite-based normalized difference index, for the coming growing season based on meteorological forecast dataset SEAS5. First, we evaluated accuracy data against site-based observations China. Air temperature, surface net radiation, and relative humidity monthly from SEAS5 were found...
Introduction: Timely and accurately mapping the spatial distribution of rice is great significance for estimating crop yield, ensuring food security freshwater resources, studying climate change. Double-season a dominant planting system in China, but it challenging to map from remote sensing data due its complex temporal profiles that requires high-frequency observations. Methods: We used an automated method based on Synthetic Aperture Radar (SAR)-based Rice Mapping Index (SPRI), no samples...
Winter wheat is a major staple food grown worldwide. China contributes about 19% of the winter production in world. Therefore, quickly and accurately acquiring distribution over long time periods critical to achieve grain safety understand spatiotemporal patterns wheat. However, there lack widely available, high spatial resolution, long-term, large-scale remotely sensed maps on cultivation China. In this study, we utilized phenology-based algorithm identify location. The integrated three key...
Abstract. Paddy rice is the second-largest grain crop in China and plays an important role ensuring global food security. However, there no high-resolution map of covering all China. This study developed a new mapping method by combining optical synthetic aperture radar (SAR) images cloudy areas based on time-weighted dynamic time warping (TWDTW) produced distribution maps single-season 21 provincial administrative regions from 2017 to 2022 at 10 or 20-m resolution. The accuracy was examined...
Abstract Climate change has significantly altered crop phenology, which further impacted growth and yield. Accurate monitoring of phenology is essential for managing agricultural production in response. However, regional requires high spatial resolution distribution data, as medium data suffers from mixed pixel issues. This study based on a long‐term spatiotemporal fusion set Normalized Difference Vegetation Index an annually updated maize set, used the relative threshold method to identify...
Abstract. As one of the most widely cultivated grain crops, paddy rice is a vital staple food in China and plays crucial role ensuring security. Over past decades, planting area has shown substantial variability. Yet, there are no long-term high-resolution distribution maps China, which hinders our ability to estimate greenhouse gas fluxes crop production. This study developed new optical satellite-based mapping method using machine learning model appropriate data preprocessing strategies...
Introduction: Using satellite data to identify the planting area of summer crops is difficult because their similar phenological characteristics. Methods: This study developed a new method for differentiating maize from other based on revised time-weighted dynamic time warping (TWDTW) method, phenology-based classification by combining information multiple spectral bands and indexes instead one single index. First, we compared characteristics four main in Henan Province China terms indexes....
Abstract. As one of the most widely cultivated grain crops, paddy rice is a vital staple food in China and plays crucial role ensuring security. Over past decades, planting area has shown substantial variability. Yet, there are no long-term high-resolution distribution maps China, which hinders our ability to estimate greenhouse gas fluxes crop production. This study developed new optical satellite-based mapping method using machine learning model appropriate data preprocessing strategies...
Abstract. Winter-triticeae crops, such as winter wheat, barley, rye, and triticale, are important in human diets planted worldwide, thus accurate spatial distribution information of winter-triticeae crops is crucial for monitoring crop production food security. However, there still a lack global high-resolution maps because the reliance existing mapping methods on training samples, which limits their application at scale. In this study, we propose new method based Winter-Triticeae Crops...