A REMOTE SENSING-DERIVED CORN YIELD ASSESSMENT MODEL

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dc.contributor.advisorDi, Liping
dc.contributor.authorShrestha, Ranjay M.
dc.creatorShrestha, Ranjay M.
dc.date.accessioned2018-10-22T01:19:54Z
dc.date.available2018-10-22T01:19:54Z
dc.date.issued2017
dc.description.abstractAgricultural 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.extent224 pages
dc.identifier.urihttps://hdl.handle.net/1920/11277
dc.language.isoen
dc.rightsCopyright 2017 Ranjay M. Shrestha
dc.subjectRemote sensing
dc.subjectAgriculture
dc.subjectGeographic information science and geodesy
dc.subjectAgriculture
dc.subjectCorn Yield
dc.subjectFlood Corn Impact
dc.subjectNDVI
dc.subjectRegression Model
dc.subjectRemote Sensing
dc.titleA REMOTE SENSING-DERIVED CORN YIELD ASSESSMENT MODEL
dc.typeDissertation
thesis.degree.disciplineEarth Systems and Geoinformation Sciences
thesis.degree.grantorGeorge Mason University
thesis.degree.levelPh.D.

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