03413ntm a22005177i 4500 000714163 CZ-PrVSE 20240608092444.0 m d cr n|||||||||| 240608s2024 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Feng, Qi ISIS:172426 dis Use of Satellite Technologies for the Agricultural Statistics eng Use of Satellite Technologies for the Agricultural Statistics / Qi Feng 2024 ?? stran : digital, PDF soubor Vedoucí práce: Jakub Fischer Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky, 2024 Obsahuje bibliografii Textový (vysokoškolská kvalifikační práce) Rok obhajoby 2024 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. Způsob přístupu: Internet official statistics [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas Satellite data Crop yield estimation Agricultural Statistics Machine Learning the Czech Republic Fischer, Jakub, 1978- mzk2004265373 ths Doležalová, Veronika opn Vysoká škola ekonomická v Praze. Fakulta informatiky a statistiky kn20010709399 dgg https://insis.vse.cz/zp/88173/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/88173 Hlavní práce https://insis.vse.cz/zp/88173/posudek/vedouci Hodnocení vedoucího https://insis.vse.cz/zp/88173/posudek/oponent/83304 Oponentura https://insis.vse.cz/zp/88173/podrobnosti dc:identifier NEPOSILAT VSKP vse88173 240606 88173