What is he best known for?
The fields of study he is best known for:
- Ecology
- Climate change
- Ecosystem
His main research concerns Phenology, Vegetation, Climatology, Climate change and Growing season.
He combines subjects such as Epigeal and Atmospheric sciences with his study of Vegetation.
His work deals with themes such as Normalized Difference Vegetation Index and Precipitation, which intersect with Climatology.
As a part of the same scientific family, Miaogen Shen mostly works in the field of Climate change, focusing on Ecosystem and, on occasion, Ecology.
His research integrates issues of Global warming, East Asian Monsoon and Plateau in his study of Growing season.
His research in Plateau intersects with topics in Arid and Growing degree-day.
His most cited work include:
- Influences of temperature and precipitation before the growing season on spring phenology in grasslands of the central and eastern Qinghai-Tibetan Plateau (240 citations)
- Leaf onset in the northern hemisphere triggered by daytime temperature (212 citations)
- Evaporative cooling over the Tibetan Plateau induced by vegetation growth (203 citations)
What are the main themes of his work throughout his whole career to date?
Miaogen Shen mainly investigates Vegetation, Climate change, Phenology, Plateau and Climatology.
Miaogen Shen has included themes like Arid, Northern Hemisphere, Atmospheric sciences and Remote sensing in his Vegetation study.
His studies in Climate change integrate themes in fields like Temperate climate, Ecosystem, Physical geography and Growing season.
His work in Phenology tackles topics such as East Asian Monsoon which are related to areas like Weather and climate.
He focuses mostly in the field of Plateau, narrowing it down to topics relating to Precipitation and, in certain cases, Terrestrial ecosystem.
His work in the fields of Climatology, such as Monsoon, intersects with other areas such as Advanced very-high-resolution radiometer.
He most often published in these fields:
- Vegetation (74.12%)
- Climate change (75.29%)
- Phenology (69.41%)
What were the highlights of his more recent work (between 2019-2021)?
- Vegetation (74.12%)
- Physical geography (31.76%)
- Phenology (69.41%)
In recent papers he was focusing on the following fields of study:
His primary areas of study are Vegetation, Physical geography, Phenology, Latitude and Climate change.
Miaogen Shen works on Vegetation which deals in particular with Normalized Difference Vegetation Index.
The Physical geography study combines topics in areas such as Plateau and Precipitation.
His Plateau research is multidisciplinary, incorporating elements of Monsoon and Water content.
Within one scientific family, he focuses on topics pertaining to Remote sensing under Phenology, and may sometimes address concerns connected to Stage and Winter wheat.
His studies deal with areas such as Spatial ecology and Snowmelt as well as Climate change.
Between 2019 and 2021, his most popular works were:
- A New Cross-Fusion Method to Automatically Determine the Optimal Input Image Pairs for NDVI Spatiotemporal Data Fusion (9 citations)
- Coarse-Resolution Satellite Images Overestimate Urbanization Effects on Vegetation Spring Phenology (7 citations)
- Warming-induced unprecedented high-elevation forest growth over the monsoonal Tibetan Plateau (6 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Climate change
- Ecosystem
Image resolution, Vegetation, Normalized Difference Vegetation Index, Similarity and Temporal resolution are his primary areas of study.
His Image resolution study incorporates themes from Remote sensing, Spring, Phenology and Urbanization.
His Vegetation research is multidisciplinary, incorporating perspectives in Diurnal temperature variation, Climatology and Growing season.
His Normalized Difference Vegetation Index research includes themes of Northern Hemisphere and Seasonality.
Miaogen Shen interconnects Sensor fusion and Pattern recognition in the investigation of issues within Similarity.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
