A5015672094- Remote Sensing in Agriculture
- Land Use and Ecosystem Services
- Remote Sensing and Land Use
- Spectroscopy and Chemometric Analyses
- Smart Agriculture and AI
- Advanced Photocatalysis Techniques
- Remote Sensing and LiDAR Applications
- Climate change impacts on agriculture
- Remote-Sensing Image Classification
- Advanced Breast Cancer Therapies
- Soil Geostatistics and Mapping
- Colorectal Cancer Treatments and Studies
- Perovskite Materials and Applications
- Agricultural Economics and Policy
- Urban Heat Island Mitigation
- Environmental Monitoring and Data Management
- Agricultural Productivity and Crop Improvement
- Water-Energy-Food Nexus Studies
- Solar-Powered Water Purification Methods
- Hydrology and Watershed Management Studies
- Copper-based nanomaterials and applications
- Agriculture and Rural Development Research
- Genetics and Plant Breeding
- Sustainable Agricultural Systems Analysis
- Agriculture Sustainability and Environmental Impact
Changchun University of Technology
2023-2024
Jiangsu University of Science and Technology
2023-2024
Food and Agriculture Organization of the United Nations
2021-2023
Chengde Medical University
2023
Nautilus (United States)
2022
Baoding People's Hospital
2022
George Mason University
2020-2021
Chinese Academy of Agricultural Sciences
2018-2020
Ministry of Agriculture and Rural Affairs
2020
Shenzhen University
2018-2020
Currently, accurate information on crop area coverage is vital for food security and industry, there strong demand timely mapping. In this study, we used MODIS time series data to investigate the effect of length Eight with different lengths (ranging from one month eight months) were tested. For each series, first Random Forest (RF) algorithm calculate importance score all features (including multi-spectral data, Normalized Difference Vegetation Index (NDVI), Water (NDWI), phenological...
As satellite observation technology develops and the number of Earth (EO) satellites increases, observations have become essential to developments in understanding its environment. However, current impacts remote sensing community different EO data possible future trends applications not been systematically examined. In this paper, we review use based on an analysis from 15 whose are widely used. Articles that reference missions included Web Science core collection for 2020 were analyzed...
A timely and detailed crop-specific land cover map can support many agricultural applications decision makings. However, in-season crop mapping over a large area is still challenging due to the insufficiency of ground truth in early stage growing season. To address this issue, paper presents an efficient machine-learning workflow for rapid corn soybeans fields without data current year. We use trusted pixels, set pixels that are predicted from historical Cropland Data Layer (CDL) with high...
Global land cover has undergone extensive and rapid changes as a result of human activities climate change. These have had significant impact on biodiversity, the surface energy balance, sustainable development. data underpins research development earth system models, resource management, evaluation ecological environment. However, there are limitations in classification detail, spatial resolution, change monitoring capability global data. Building earlier Land Cover Mapping (Finer...
An increase in crop intensity could improve yield but may also lead to a series of environmental problems, such as depletion ground water and increased soil salinity. The generation high resolution (30 m) maps is an important method used monitor these changes, this challenging because the temporal 30-m image time low due long satellite revisit period cloud coverage. recently launched Sentinel-2 provide optical images at 10–60 m thus series. This study harmonized Landsat (HLS) data identify...
Abstract The cropping intensity has received growing concern in the agriculture field applications such as harvest area research. Notwithstanding significant amount of existing literature on local intensities, research considering global datasets appears to be limited spatial resolution and precision. In this paper, we present an annual dynamic dataset covering period from 2001 2019 at a 250-m with average overall accuracy 89%, exceeding current data 500-m resolution. We used enhanced...
Early-season crop type mapping could provide important information for growth monitoring and yield prediction, but the lack of ground-surveyed training samples is main challenge identification. Although reference time series based method (RBM) has been proposed to identify types without use samples, methods are not suitable study regions with small field size because mainly generated using data set low spatial resolution. As combination Landsat Sentinel-2 increase temporal resolution 30-m...
Abstract. Cropland greatly impacts food security, energy supply, biodiversity, biogeochemical cycling, and climate change. Accurately systematically understanding the effects of agricultural activities requires cropland spatial information with high resolution a long time span. In this study, first 1 km global proportion dataset for 10 000 BCE–2100 CE was produced. With map initialized in 2010 CE, we harmonized demands extracted from History Database Global Environment 3.2 (HYDE 3.2)...
Tennis is one of the most popular sports worldwide. Audiences love tennis matches that are full excitement and swings. In match, to capture flow play great importance both coaches athletes themselves. this paper, we analyze dataset Wimbledon 2023 Gentlemen’s develop a model related “momentum” with LightGBM, which captures play. We first introduce some indexes, such as net point-won rate, number breakpoints, “momentum”. The proposed constructed using utilized train validate model....
Time series data capture crop growth dynamics and are some of the most effective sources for mapping. However, a drawback precise classification at medium resolution (30 m) using multi-temporal is that images crucial time periods absent from single sensor. In this research, medium-resolution, 15-day was obtained by merging Landsat-5 TM HJ-1 CCD (with similar radiometric performances in multi-spectral bands). Subsequently, optimal temporal windows accurate mapping were evaluated an extension...
With the recent launch of new satellites and developments spatiotemporal data fusion methods, we are entering an era high resolution remote-sensing analysis. This study proposed a method to reconstruct daily 30 m for monitoring crop types phenology in two areas located Xinjiang Province, China. First, Spatial Temporal Data Fusion Approach (STDFA) was used time series from Huanjing satellite charge coupled device (HJ CCD), Gaofen no. 1 wide field-of-view camera (GF-1 WFV), Landsat, Moderate...
Most methods used for crop classification rely on the ground-reference data of same year, which leads to considerable financial and labor cost. In this study, we presented a method that can avoid requirements large number in year. Firstly, extracted Normalized Difference Vegetation Index (NDVI) time series profiles dominant crops from MODIS using historical multiple years (2006, 2007, 2009 2010). Artificial Antibody Network (ABNet) was then employed build reference NDVI each based profiles....
A range of single classifiers have been proposed to classify crop types using time series vegetation indices, and hybrid are used improve discriminatory power. Traditional fusion rules use the product multi-single classifiers, but that strategy cannot integrate classification output machine learning classifiers. In this research, performance two strategies, multiple voting (M-voting) probabilistic (P-fusion), for NDVI were tested with different training sample sizes at both pixel object...