What is she best known for?
The fields of study she is best known for:
- Statistics
- Ecology
- Artificial intelligence
Jin Chen focuses on Remote sensing, Pixel, Thematic Mapper, Normalized Difference Vegetation Index and Vegetation. She mostly deals with Radiometry in her studies of Remote sensing. Her Pixel research integrates issues from Mixture model and Algorithm, Gaussian noise.
Her Thematic Mapper research includes themes of Cartography, Land cover, Land use, Support vector machine and Urbanization. Her work focuses on many connections between Normalized Difference Vegetation Index and other disciplines, such as Spatial ecology, that overlap with her field of interest in Vegetation cover, Vegetation fraction, Shadow, Vegetation Index and Climatology. Her Vegetation study incorporates themes from Hyperspectral imaging, Atmospheric sciences and Physical geography.
Her most cited work include:
- A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter (1112 citations)
- A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter (1112 citations)
- Finer resolution observation and monitoring of global land cover: first mapping results with Landsat TM and ETM+ data (790 citations)
What are the main themes of her work throughout her whole career to date?
Jin Chen mainly investigates Remote sensing, Vegetation, Normalized Difference Vegetation Index, Pixel and Land cover. The Remote sensing study combines topics in areas such as Image resolution, Reflectivity and Moderate-resolution imaging spectroradiometer. Her studies in Vegetation integrate themes in fields like Snow, Atmospheric sciences and Phenology.
Her biological study spans a wide range of topics, including Mean squared error, Correlation coefficient and Time series. Her research in Pixel intersects with topics in Statistics and Pattern recognition. Her Land cover study incorporates themes from Change detection, Data mining and Scale.
She most often published in these fields:
- Remote sensing (46.15%)
- Vegetation (18.55%)
- Normalized Difference Vegetation Index (18.10%)
What were the highlights of her more recent work (between 2018-2021)?
- Remote sensing (46.15%)
- Vegetation (18.55%)
- Phenology (10.41%)
In recent papers she was focusing on the following fields of study:
Her primary areas of investigation include Remote sensing, Vegetation, Phenology, Normalized Difference Vegetation Index and Ground-penetrating radar. Her Remote sensing research incorporates elements of Pixel, Visible Infrared Imaging Radiometer Suite and Defense Meteorological Satellite Program. Her Vegetation study integrates concerns from other disciplines, such as Atmospheric sciences and Linear regression.
The study incorporates disciplines such as Winter wheat, Climate change, Snow, Physical geography and Spring in addition to Phenology. Her Normalized Difference Vegetation Index study also includes fields such as
- Image resolution and related Temporal resolution, Sensor fusion and Pattern recognition,
- Mean squared error that intertwine with fields like Land cover. Her Ground-penetrating radar research incorporates themes from Soil science and Soil water.
Between 2018 and 2021, her most popular works were:
- An Improved Flexible Spatiotemporal DAta Fusion (IFSDAF) method for producing high spatiotemporal resolution normalized difference vegetation index time series (32 citations)
- A simple self-adjusting model for correcting the blooming effects in DMSP-OLS nighttime light images (22 citations)
- Measurement of soil water content using ground-penetrating radar: a review of current methods (12 citations)
In her most recent research, the most cited papers focused on:
- Statistics
- Ecology
- Artificial intelligence
Her scientific interests lie mostly in Remote sensing, Image resolution, Ground-penetrating radar, Temporal resolution and Vegetation. Her studies in Remote sensing integrate themes in fields like Intensity, Regression and Spatial variability. Her Image resolution research integrates issues from Remote sensing and Data mining.
Her Temporal resolution research is multidisciplinary, relying on both Normalized Difference Vegetation Index, Artificial intelligence and Pattern recognition. Her research in Normalized Difference Vegetation Index intersects with topics in Mean squared error, Spatial analysis and Spatial heterogeneity. Her studies deal with areas such as Land cover and Phenology as well as Vegetation.
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