Use of Satellite Technologies for the Agricultural Statistics
Thesis title: | Use of Satellite Technologies for the Agricultural Statistics |
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Author: | Feng, Qi |
Thesis type: | Diploma thesis |
Supervisor: | Fischer, Jakub |
Opponents: | Doležalová, Veronika |
Thesis language: | English |
Abstract: | Obtaining timely and accurate crop yield estimates on a national scale is important for shaping effective food security policies, evaluating agricultural production efficiency, and directing resources to regions facing food shortages. Conventional methods of yield estimation often rely on labor-intensive field surveys, which are not only time-consuming and resource-intensive but also are vulnerable to localized variations. They may not capture the full range of environmental factors and variables that can affect crop yield in different regions or even within the same field. Embracing remotely sensed satellite data presents a transformative alternative. This thesis delves into this emerging field by synthesizing insights drawn from an exhaustive analysis of methodologies employed by 10 countries (Poland, Germany, Sweden, Finland, Slovenia, Spain, Morocco, the United States, Vietnam, and China). The analysis provides a roadmap for employing satellite data to enhance agricultural statistics, with a specific emphasis on crop yield estimation. The research comprises various aspects, including free and open accesses to satellite data selection, parameters selection, processing methodology, and requirements as adopted by these countries. Furthermore, within the context of yield estimation, it outlines a conceptual framework for leveraging satellite technology to estimate the yield of crop within the Czech Republic. |
Keywords: | Satellite data; Crop yield estimation; Agricultural Statistics; Machine Learning; the Czech Republic |
Thesis title: | Use of Satellite Technologies for the Agricultural Statistics |
---|---|
Author: | Feng, Qi |
Thesis type: | Diplomová práce |
Supervisor: | Fischer, Jakub |
Opponents: | Doležalová, Veronika |
Thesis language: | English |
Abstract: | Obtaining timely and accurate crop yield estimates on a national scale is important for shaping effective food security policies, evaluating agricultural production efficiency, and directing resources to regions facing food shortages. Conventional methods of yield estimation often rely on labor-intensive field surveys, which are not only time-consuming and resource-intensive but also are vulnerable to localized variations. They may not capture the full range of environmental factors and variables that can affect crop yield in different regions or even within the same field. Embracing remotely sensed satellite data presents a transformative alternative. This thesis delves into this emerging field by synthesizing insights drawn from an exhaustive analysis of methodologies employed by 10 countries (Poland, Germany, Sweden, Finland, Slovenia, Spain, Morocco, the United States, Vietnam, and China). The analysis provides a roadmap for employing satellite data to enhance agricultural statistics, with a specific emphasis on crop yield estimation. The research comprises various aspects, including free and open accesses to satellite data selection, parameters selection, processing methodology, and requirements as adopted by these countries. Furthermore, within the context of yield estimation, it outlines a conceptual framework for leveraging satellite technology to estimate the yield of crop within the Czech Republic. |
Keywords: | Satellite data; Crop yield estimation; Agricultural Statistics; Machine Learning; the Czech Republic |
Information about study
Study programme: | Economic Data Analysis/Official Statistics |
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Type of study programme: | Magisterský studijní program |
Assigned degree: | Ing. |
Institutions assigning academic degree: | Vysoká škola ekonomická v Praze |
Faculty: | Faculty of Informatics and Statistics |
Department: | Department of Economic Statistics |
Information on submission and defense
Date of assignment: | 2. 4. 2024 |
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Date of submission: | 28. 4. 2024 |
Date of defense: | 5. 6. 2024 |
Identifier in the InSIS system: | https://insis.vse.cz/zp/88173/podrobnosti |