Use of Satellite Technologies for the Agricultural Statistics

Thesis title: Use of Satellite Technologies for the Agricultural Statistics
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
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
Date of submission: 28. 4. 2024
Date of defense: 5. 6. 2024
Identifier in the InSIS system: https://insis.vse.cz/zp/88173/podrobnosti

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