What is he best known for?
The fields of study he is best known for:
- Ecology
- Statistics
- Meteorology
His primary areas of study are Meteorology, Evapotranspiration, Remote sensing, Water cycle and Precipitation.
His Meteorology study incorporates themes from Eddy covariance, Atmospheric sciences, Forcing, Range and Scale.
His Evapotranspiration research is multidisciplinary, incorporating elements of Water balance, Hydrometeorology, Climatology and Water resources.
He focuses mostly in the field of Remote sensing, narrowing it down to matters related to Water content and, in some cases, Soil water.
His Water cycle research is multidisciplinary, incorporating perspectives in Data assimilation, Surface water and Moderate-resolution imaging spectroradiometer.
His research investigates the link between Precipitation and topics such as Biomass that cross with problems in Ecosystem.
His most cited work include:
- Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data (784 citations)
- Trend-preserving blending of passive and active microwave soil moisture retrievals (490 citations)
- Developing an improved soil moisture dataset by blending passive and active microwave satellite-based retrievals (459 citations)
What are the main themes of his work throughout his whole career to date?
Matthew F. McCabe mainly investigates Remote sensing, Evapotranspiration, Meteorology, Climatology and Water content.
The study incorporates disciplines such as Pixel, Canopy, Vegetation and Scale in addition to Remote sensing.
His Evapotranspiration research incorporates themes from Atmospheric sciences, Latent heat, Water balance, Water cycle and Data assimilation.
The Meteorology study combines topics in areas such as Scale, Eddy covariance, Energy balance, Spatial variability and Forcing.
His Climatology research integrates issues from Snow, Climate model and Precipitation.
Matthew F. McCabe combines subjects such as Soil science and Soil water with his study of Water content.
He most often published in these fields:
- Remote sensing (39.04%)
- Evapotranspiration (24.56%)
- Meteorology (20.61%)
What were the highlights of his more recent work (between 2017-2021)?
- Remote sensing (39.04%)
- Vegetation (10.53%)
- Climatology (19.30%)
In recent papers he was focusing on the following fields of study:
Matthew F. McCabe mainly focuses on Remote sensing, Vegetation, Climatology, Remote sensing and Agriculture.
His work carried out in the field of Remote sensing brings together such families of science as Canopy, Crop water use, Evapotranspiration and Interception.
Matthew F. McCabe has researched Evapotranspiration in several fields, including Latent heat, Satellite remote sensing and Eddy covariance.
His Vegetation research includes themes of Data collection, Satellite imagery and Leaf area index.
His Climatology research includes elements of Surface urban heat island, State and Precipitation.
His Agriculture research incorporates elements of Agroforestry, Agronomy and Water resources.
Between 2017 and 2021, his most popular works were:
- On the Use of Unmanned Aerial Systems for Environmental Monitoring (204 citations)
- A Cubesat enabled Spatio-Temporal Enhancement Method (CESTEM) utilizing Planet, Landsat and MODIS data (82 citations)
- A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning (71 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Statistics
- Artificial intelligence
His main research concerns Remote sensing, Evapotranspiration, Random forest, Scale and Precision agriculture.
His study in Remote sensing is interdisciplinary in nature, drawing from both Land cover, Water balance, Moderate-resolution imaging spectroradiometer and Interception.
The various areas that Matthew F. McCabe examines in his Evapotranspiration study include Latent heat, Meteorology and Crop water use.
His research integrates issues of Leaf area index, Agronomy, Photosynthetic pigment, Chlorophyll content and Normalized Difference Vegetation Index in his study of Random forest.
His Scale research focuses on Image resolution and how it connects with VNIR, Earth observation and Vegetation.
His Precision agriculture study combines topics in areas such as Image matching, Crop and Crop production.
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.
