From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities
Monitoring the start of the crop season in Sahel provides decision makers with valuable information for an early assessment of potential production and food security threats. Presently, the most common method for the estimation of sowing dates in West African countries consists of applying given thr...
| Main Authors: | , , , , , |
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| Format: | Journal Article |
| Language: | Inglés |
| Published: |
MDPI
2014
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10568/93511 |
| _version_ | 1855520056500289536 |
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| author | Marinho, E. Vancutsem, C. Fasbender, D. Kayitakire, F Pini, G Pekel, J.F. |
| author_browse | Fasbender, D. Kayitakire, F Marinho, E. Pekel, J.F. Pini, G Vancutsem, C. |
| author_facet | Marinho, E. Vancutsem, C. Fasbender, D. Kayitakire, F Pini, G Pekel, J.F. |
| author_sort | Marinho, E. |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | Monitoring the start of the crop season in Sahel provides decision makers with valuable information for an early assessment of potential production and food security threats. Presently, the most common method for the estimation of sowing dates in West African countries consists of applying given thresholds on rainfall estimations. However, the coarse spatial resolution and the possible inaccuracy of these estimations are limiting factors. In this context, the remote sensing approach, which consists of deriving green-up onset dates from satellite remote sensing data, appears as an interesting alternative. It builds upon a novel statistic model that translates vegetation onset detections derived from MODIS time series into sowing probabilities at the village level. Results for Niger show that this approach outperforms the standard method adopted in the region based on rainfall thresholds. |
| format | Journal Article |
| id | CGSpace93511 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2014 |
| publishDateRange | 2014 |
| publishDateSort | 2014 |
| publisher | MDPI |
| publisherStr | MDPI |
| record_format | dspace |
| spelling | CGSpace935112025-06-17T08:23:29Z From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities Marinho, E. Vancutsem, C. Fasbender, D. Kayitakire, F Pini, G Pekel, J.F. crop production statistics remote sensing food security Monitoring the start of the crop season in Sahel provides decision makers with valuable information for an early assessment of potential production and food security threats. Presently, the most common method for the estimation of sowing dates in West African countries consists of applying given thresholds on rainfall estimations. However, the coarse spatial resolution and the possible inaccuracy of these estimations are limiting factors. In this context, the remote sensing approach, which consists of deriving green-up onset dates from satellite remote sensing data, appears as an interesting alternative. It builds upon a novel statistic model that translates vegetation onset detections derived from MODIS time series into sowing probabilities at the village level. Results for Niger show that this approach outperforms the standard method adopted in the region based on rainfall thresholds. 2014 2018-07-03T10:55:53Z 2018-07-03T10:55:53Z Journal Article https://hdl.handle.net/10568/93511 en Open Access MDPI Marinho, E., Vancutsem, C., Fasbender, D., Kayitakire, F., Pini, G., Pekel, J.-F. . 2014. From Remotely Sensed Vegetation Onset to Sowing Dates : Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities. Remote Sensing, 6 (11) : 10947-10965. https://doi.org/10.3390/rs61110947 |
| spellingShingle | crop production statistics remote sensing food security Marinho, E. Vancutsem, C. Fasbender, D. Kayitakire, F Pini, G Pekel, J.F. From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities |
| title | From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities |
| title_full | From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities |
| title_fullStr | From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities |
| title_full_unstemmed | From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities |
| title_short | From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities |
| title_sort | from remotely sensed vegetation onset to sowing dates aggregating pixel level detections into village level sowing probabilities |
| topic | crop production statistics remote sensing food security |
| url | https://hdl.handle.net/10568/93511 |
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