Normalized Difference Vegetation Index, Remote sensing, Moderate-resolution imaging spectroradiometer, Water content and Land cover are his primary areas of study. His biological study focuses on Vegetation Index. His work in Remote sensing addresses subjects such as Growing season, which are connected to disciplines such as Phenology.
His studies in Moderate-resolution imaging spectroradiometer integrate themes in fields like Normalized difference water index, Grassland, Multiple cropping and Crop. The study incorporates disciplines such as Precipitation index, Meteorology, Evapotranspiration, Surface runoff and Vegetation in addition to Water content. His work carried out in the field of Land cover brings together such families of science as Enhanced vegetation index, Cropping and Plant cover.
His primary areas of study are Remote sensing, Normalized Difference Vegetation Index, Vegetation, Agriculture and Vegetation. His Remote sensing study, which is part of a larger body of work in Remote sensing, is frequently linked to Reflectivity, bridging the gap between disciplines. His studies deal with areas such as Land cover, Grassland, Growing season and Moderate-resolution imaging spectroradiometer as well as Normalized Difference Vegetation Index.
The concepts of his Growing season study are interwoven with issues in Irrigation and Crop. His study looks at the relationship between Vegetation and topics such as Water content, which overlap with Evapotranspiration, Precipitation, Crop yield, Soil water and Data assimilation. In his study, which falls under the umbrella issue of Agriculture, Cartography is strongly linked to Satellite imagery.
The scientist’s investigation covers issues in Agriculture, Arid, Physical geography, Normalized Difference Vegetation Index and Agronomy. His Physical geography course of study focuses on Satellite imagery and Growing season and Spatial ecology. His Growing season study combines topics in areas such as Crop, Phenology and Scale.
His Normalized Difference Vegetation Index study frequently draws connections to adjacent fields such as Composite number. Series and Remote sensing are two areas of study in which Brian D. Wardlow engages in interdisciplinary work. His Remote sensing research incorporates elements of Calibration and Field conditions.
His main research concerns Agriculture, Vegetation, Precipitation, Water supply and Vegetation response. Brian D. Wardlow combines subjects such as Forestry, Crop yield and Water resource management with his study of Agriculture. Brian D. Wardlow has included themes like Satellite imagery, Multispectral pattern recognition and Physical geography in his Vegetation study.
His research integrates issues of Yield, Land surface temperature, Evaporative cooler, Global warming and Water content in his study of Precipitation. The Water supply study combines topics in areas such as Agronomy, Irrigation, Crop, Enhanced vegetation index and Environmental planning. His Vegetation response study typically links adjacent topics like Arid.
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.
Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains
Brian D. Wardlow;Stephen L. Egbert;Jude H. Kastens.
Remote Sensing of Environment (2007)
Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains
Brian D. Wardlow;Stephen L. Egbert.
Remote Sensing of Environment (2008)
A five‐year analysis of MODIS NDVI and NDWI for grassland drought assessment over the central Great Plains of the United States
Yingxin Gu;Jesslyn F. Brown;James P. Verdin;Brian Wardlow.
Geophysical Research Letters (2007)
Remote sensing of drought: Progress, challenges and opportunities
A. AghaKouchak;A. Farahmand;F. S. Melton;J. Teixeira.
Reviews of Geophysics (2015)
The Vegetation Drought Response Index (VegDRI): A New Integrated Approach for Monitoring Drought Stress in Vegetation
Jesslyn F. Brown;Brian D. Wardlow;Tsegaye Tadesse;Michael J. Hayes.
Giscience & Remote Sensing (2008)
Evaluation of Drought Indices Based on Thermal Remote Sensing of Evapotranspiration over the Continental United States
Martha C. Anderson;Christopher R. Hain;Brian Wardlow;Agustin Pimstein.
Journal of Climate (2011)
A high-performance and in-season classification system of field-level crop types using time-series Landsat data and a machine learning approach
Yaping Cai;Kaiyu Guan;Jian Peng;Shaowen Wang.
Remote Sensing of Environment (2018)
Evaluation of MODIS NDVI and NDWI for vegetation drought monitoring using Oklahoma Mesonet soil moisture data
Yingxin Gu;Eric Hunt;Brian Wardlow;Jeffrey B. Basara.
Geophysical Research Letters (2008)
A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data
Toshihiro Sakamoto;Brian D. Wardlow;Anatoly A. Gitelson;Shashi B. Verma.
Remote Sensing of Environment (2010)
An Intercomparison of Drought Indicators Based on Thermal Remote Sensing and NLDAS-2 Simulations with U.S. Drought Monitor Classifications
Martha C. Anderson;Christopher Hain;Jason Otkin;Xiwu Zhan.
Journal of Hydrometeorology (2013)
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