What is he best known for?
The fields of study he is best known for:
- Ecology
- Remote sensing
- Artificial intelligence
Jeffrey G. Masek focuses on Remote sensing, Satellite imagery, Land cover, Vegetation and Moderate-resolution imaging spectroradiometer.
His research on Remote sensing focuses in particular on Thematic Mapper.
His Satellite imagery research incorporates elements of Change detection, Canopy, Tundra and Data set.
His Land cover research integrates issues from Remote sensing and Reference data.
His study looks at the intersection of Remote sensing and topics like Snow with Albedo.
His work on Plant cover and Normalized Difference Vegetation Index is typically connected to Time series as part of general Vegetation study, connecting several disciplines of science.
His most cited work include:
- Landsat-8: Science and Product Vision for Terrestrial Global Change Research (1061 citations)
- A Landsat surface reflectance dataset for North America, 1990-2000 (1006 citations)
- On the blending of the Landsat and MODIS surface reflectance: predicting daily Landsat surface reflectance (926 citations)
What are the main themes of his work throughout his whole career to date?
His main research concerns Remote sensing, Land cover, Vegetation, Disturbance and Satellite imagery.
Jeffrey G. Masek is studying Remote sensing, which is a component of Remote sensing.
His study in the field of Normalized Difference Vegetation Index also crosses realms of Time series.
His research integrates issues of Forestry, Ecosystem, Physical geography and Atmospheric sciences in his study of Disturbance.
His Satellite imagery research incorporates themes from Pixel, Random forest and Data set.
His work in Bidirectional reflectance distribution function addresses issues such as Moderate-resolution imaging spectroradiometer, which are connected to fields such as Albedo.
He most often published in these fields:
- Remote sensing (58.08%)
- Land cover (16.16%)
- Vegetation (15.15%)
What were the highlights of his more recent work (between 2016-2021)?
- Remote sensing (58.08%)
- Biogeosciences (5.56%)
- Bidirectional reflectance distribution function (8.08%)
In recent papers he was focusing on the following fields of study:
Jeffrey G. Masek mostly deals with Remote sensing, Biogeosciences, Bidirectional reflectance distribution function, Normalization and Satellite imagery.
His Remote sensing study combines topics in areas such as Image resolution and Atmospheric correction.
As part of one scientific family, he deals mainly with the area of Image resolution, narrowing it down to issues related to the Normalized Difference Vegetation Index, and often Growing degree-day, Boreal and Range.
Jeffrey G. Masek focuses mostly in the field of Atmospheric correction, narrowing it down to matters related to Data set and, in some cases, Outlier.
Within one scientific family, he focuses on topics pertaining to Moderate-resolution imaging spectroradiometer under Bidirectional reflectance distribution function, and may sometimes address concerns connected to Nadir.
His studies deal with areas such as Algorithm, Random forest and Shadow as well as Satellite imagery.
Between 2016 and 2021, his most popular works were:
- The Harmonized Landsat and Sentinel-2 surface reflectance data set (265 citations)
- Current status of Landsat program, science, and applications (184 citations)
- Monitoring land surface albedo and vegetation dynamics using high spatial and temporal resolution synthetic time series from Landsat and the MODIS BRDF/NBAR/albedo product. (34 citations)
In his most recent research, the most cited papers focused on:
- Ecology
- Remote sensing
- Artificial intelligence
Jeffrey G. Masek focuses on Remote sensing, Bidirectional reflectance distribution function, Normalization, Artificial intelligence and Moderate-resolution imaging spectroradiometer.
Jeffrey G. Masek works on Remote sensing which deals in particular with Satellite imagery.
His Normalization study integrates concerns from other disciplines, such as Atmospheric correction, Outlier, Data set and Pattern recognition.
The various areas that he examines in his Artificial intelligence study include Transformation and Computer vision.
His studies in Moderate-resolution imaging spectroradiometer integrate themes in fields like Albedo and Nadir.
His Temporal resolution research includes a combination of various areas of study, such as Pixel and Image resolution.
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