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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Forecasting of Cereal Yields in a Semi-arid Area Using the Simple Algorithm for Yield Estimation (SAFY) Agro-Meteorological Model Combined with Optical SPOT/HRV Images
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Forecasting of Cereal Yields in a Semi-arid Area Using the Simple Algorithm for Yield Estimation (SAFY) Agro-Meteorological Model Combined with Optical SPOT/HRV Images

机译：结合光学SPOT / HRV图像的简单估计产量（SAFY）农业气象模型算法对半干旱地区的谷物产量进行预测

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摘要

In semi-arid areas characterized by frequent drought events, there is often a strong need for an operational grain yield forecasting system, to help decision-makers with the planning of annual imports. However, monitoring the crop canopy and production capacity of plants, especially for cereals, can be challenging. In this paper, a new approach to yield estimation by combining data from the Simple Algorithm for Yield estimation (SAFY) agro-meteorological model with optical SPOT/ High Visible Resolution (HRV) satellite data is proposed. Grain yields are then statistically estimated as a function of Leaf Area Index (LAI) during the maximum growth period between 25 March and 5 April. The LAI is retrieved from the SAFY model, and calibrated using SPOT/HRV data. This study is based on the analysis of a rich database, which was acquired over a period of two years (2010–2011, 2012–2013) at the Merguellil site in central Tunisia (North Africa) from more than 60 test fields and 20 optical satellite SPOT/HRV images. The validation and calibration of this methodology is presented, on the basis of two subsets of observations derived from the experimental database. Finally, an inversion technique is applied to estimate the overall yield of the entire studied site.
机译：在干旱频发的半干旱地区，经常需要可操作的谷物单产预测系统，以帮助决策者制定年度进口计划。但是，监测作物的冠层和植物的生产能力，尤其是谷物的能力可能是具有挑战性的。本文提出了一种将单产估算简单农业气象模型数据与光学SPOT /高可见度（HRV）卫星数据相结合的单产估算新方法。然后在3月25日至4月5日期间的最大生长期，根据叶面积指数（LAI）进行谷物单产的统计估算。从SAFY模型检索LAI，并使用SPOT / HRV数据进行校准。这项研究基于对丰富数据库的分析，该数据库是在过去两年（2010-2011年，2012-2013年）在突尼斯中部（北非）的Merguellil站点上从60多个试验场和20个光学场获得的卫星SPOT / HRV图像。在从实验数据库中获得的两个观察子集的基础上，提出了该方法的验证和校准。最后，应用反演技术估算整个研究地点的总产量。

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