The added value of high-resolution above coarse-resolution remote sensing images in crop yield forecasting: A case study in the Egyptian Nile Delta
Crop growth models play a major role in sustaining the world-wide food security. These models are used to simulate crop growth during the growing season, and the final crop yield at the end of the growing season, given the farmers’ management practices. At a more strategic level, these crop growth m...
| Main Authors: | , , , , |
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| Format: | Informe técnico |
| Language: | Inglés |
| Published: |
FutureWater
2012
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/27650 |
| _version_ | 1855514983426686976 |
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| author | Droogers, Peter Dam, Jos C. van Simons, G. Voogt, M. Terink, W. |
| author_browse | Dam, Jos C. van Droogers, Peter Simons, G. Terink, W. Voogt, M. |
| author_facet | Droogers, Peter Dam, Jos C. van Simons, G. Voogt, M. Terink, W. |
| author_sort | Droogers, Peter |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Crop growth models play a major role in sustaining the world-wide food security. These models are used to simulate crop growth during the growing season, and the final crop yield at the end of the growing season, given the farmers’ management practices. At a more strategic level, these crop growth models play an important role to decision makers to take timely decisions regarding food import and/or export strategies. The simulation accuracy of crop growth models relies on the quality of the input data. Since crop yield forecasting applications are often applied over large areas that rely on a spatially distributed crop growth model, the uncertainty in the spatial variation of the input data increases. Remote sensing images are often used in crop growth models because remote sensing images provide spatially distributed input data to these models. These images are available in numerous spatial resolutions, where coarse resolution images are often freely available compared to the more expensive high-resolution images. Therefore, the objective of the current study was to evaluate the added value of high-resolution satellite imagery above coarse-resolution satellite imagery in crop yield forecasting. |
| format | Informe técnico |
| id | CGSpace27650 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2012 |
| publishDateRange | 2012 |
| publishDateSort | 2012 |
| publisher | FutureWater |
| publisherStr | FutureWater |
| record_format | dspace |
| spelling | CGSpace276502025-12-10T12:44:55Z The added value of high-resolution above coarse-resolution remote sensing images in crop yield forecasting: A case study in the Egyptian Nile Delta Droogers, Peter Dam, Jos C. van Simons, G. Voogt, M. Terink, W. crop forecasting risk management yield forecasting remote sensing Crop growth models play a major role in sustaining the world-wide food security. These models are used to simulate crop growth during the growing season, and the final crop yield at the end of the growing season, given the farmers’ management practices. At a more strategic level, these crop growth models play an important role to decision makers to take timely decisions regarding food import and/or export strategies. The simulation accuracy of crop growth models relies on the quality of the input data. Since crop yield forecasting applications are often applied over large areas that rely on a spatially distributed crop growth model, the uncertainty in the spatial variation of the input data increases. Remote sensing images are often used in crop growth models because remote sensing images provide spatially distributed input data to these models. These images are available in numerous spatial resolutions, where coarse resolution images are often freely available compared to the more expensive high-resolution images. Therefore, the objective of the current study was to evaluate the added value of high-resolution satellite imagery above coarse-resolution satellite imagery in crop yield forecasting. 2012-12-01 2013-03-01T17:40:21Z 2013-03-01T17:40:21Z Report https://hdl.handle.net/10568/27650 en Open Access application/pdf FutureWater Terink W, Droogers P, Van Dam J, Simons G, Voogt M. 2012. The added value of high-resolution above coarse-resolution remote sensing images in crop yield forecasting: A case study in the Egyptian Nile Delta. CGIAR Research Program on Climate Change, Agriculture and Food Security (CCAFS), in collaboration with FutureWater, eLeaf and Wageningen University. |
| spellingShingle | crop forecasting risk management yield forecasting remote sensing Droogers, Peter Dam, Jos C. van Simons, G. Voogt, M. Terink, W. The added value of high-resolution above coarse-resolution remote sensing images in crop yield forecasting: A case study in the Egyptian Nile Delta |
| title | The added value of high-resolution above coarse-resolution remote sensing images in crop yield forecasting: A case study in the Egyptian Nile Delta |
| title_full | The added value of high-resolution above coarse-resolution remote sensing images in crop yield forecasting: A case study in the Egyptian Nile Delta |
| title_fullStr | The added value of high-resolution above coarse-resolution remote sensing images in crop yield forecasting: A case study in the Egyptian Nile Delta |
| title_full_unstemmed | The added value of high-resolution above coarse-resolution remote sensing images in crop yield forecasting: A case study in the Egyptian Nile Delta |
| title_short | The added value of high-resolution above coarse-resolution remote sensing images in crop yield forecasting: A case study in the Egyptian Nile Delta |
| title_sort | added value of high resolution above coarse resolution remote sensing images in crop yield forecasting a case study in the egyptian nile delta |
| topic | crop forecasting risk management yield forecasting remote sensing |
| url | https://hdl.handle.net/10568/27650 |
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