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A REMOTE SENSING-DERIVED CORN YIELD ASSESSMENT MODEL

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dc.contributor.advisor Di, Liping
dc.contributor.author Shrestha, Ranjay M.
dc.creator Shrestha, Ranjay M.
dc.date.accessioned 2018-10-22T01:19:54Z
dc.date.available 2018-10-22T01:19:54Z
dc.date.issued 2017
dc.identifier.uri https://hdl.handle.net/1920/11277
dc.description.abstract Agricultural studies and food security have become critical research topics due to continuous growth in human population and simultaneous shrinkage in agricultural land. In spite of modern technological advancements to improve agricultural productivity, more studies on crop yield assessments and food productivities are still necessary to fulfill the constantly increasing food demands. Besides human activities, natural disasters such as flood and drought, along with rapid climate changes, also inflect an adverse effect on food productivities. Understanding the impact of these disasters on crop yield and making early impact estimations could help planning for any national or international food crisis. Similarly, the United States Department of Agriculture (USDA) Risk Management Agency (RMA) insurance management utilizes appropriately estimated crop yield and damage assessment information to sustain farmers’ practice through timely and proper compensations. Through County Agricultural Production Survey (CAPS), the USDA National Agricultural Statistical Service (NASS) uses traditional methods of field interviews and farmer-reported survey data to perform annual crop condition monitoring and production estimations at the regional and state levels. As these manual approaches of yield estimations are highly inefficient and produce very limited samples to represent the entire area, NASS requires supplemental spatial data that provides continuous and timely information on crop production and annual yield.
dc.format.extent 224 pages
dc.language.iso en
dc.rights Copyright 2017 Ranjay M. Shrestha
dc.subject Remote sensing en_US
dc.subject Agriculture en_US
dc.subject Geographic information science and geodesy en_US
dc.subject Agriculture en_US
dc.subject Corn Yield en_US
dc.subject Flood Corn Impact en_US
dc.subject NDVI en_US
dc.subject Regression Model en_US
dc.subject Remote Sensing en_US
dc.title A REMOTE SENSING-DERIVED CORN YIELD ASSESSMENT MODEL
dc.type Dissertation
thesis.degree.level Ph.D.
thesis.degree.discipline Earth Systems and Geoinformation Sciences
thesis.degree.grantor George Mason University


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