Use of Satellite Technologies for the Agricultural Statistics

Qi Feng

Master's thesis

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.

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

Keywords

Satellite data, Crop yield estimation, Agricultural Statistics, Machine Learning, the Czech Republic

Language used: English

Date on which the thesis was submitted / produced: 28. 4. 2024

Thesis defence

• Date of defence: 5. 6. 2024
• Supervisor: Jakub Fischer
• Reader: Veronika Doležalová

Vysoká škola ekonomická v Praze

Master programme / field:
Economic Data Analysis / Official Statistics