G2TT
来源类型Articles
规范类型论文
DOI10.3390/rs61110947
ISSN2072-4292
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
URLhttps://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
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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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