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来源类型 | Articles |
规范类型 | 论文 |
DOI | 10.3390/rs61110947 |
ISSN | 2072-4292 |
From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities | |
Bekele, M.; Kassa, H. | |
发表日期 | 2014 |
出处 | Remote Sensing 6(11): 10947-10965 |
出版年 | 2014 |
语种 | 英语 |
摘要 | 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. |
主题 | crop production ; statistics ; remote sensing ; food security |
区域 | Sahel,West Africa |
URL | https://www.cifor.org/library/5445/ |
来源智库 | Center for International Forestry Research (Indonesia) |
引用统计 | |
资源类型 | 智库出版物 |
条目标识符 | http://119.78.100.153/handle/2XGU8XDN/92872 |
推荐引用方式 GB/T 7714 | Bekele, M.,Kassa, H.. From Remotely Sensed Vegetation Onset to Sowing Dates: Aggregating Pixel-Level Detections into Village-Level Sowing Probabilities. 2014. |
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