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首页> 外文期刊>Journal of Agrometeorology >Spatial wheat yield prediction using crop simulation model, GIS, remote sensing and ground observed data.
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Spatial wheat yield prediction using crop simulation model, GIS, remote sensing and ground observed data.

机译：使用 作物 仿真模型 ，GIS， 遥感和地面 观测数据 空间 小麦产量 预测 。

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摘要

A study was conducted with a broad objective of developing and demonstrating a methodology for crop growth monitoring and yield forecasting which can provide periodical crop growth assessment with spatial information. The procedure was developed to generate grid-weather, link the point based simulation model WOFOST (World Food Studies) to spatial inputs like crop, soil and weather and predict wheat yield at grid and administrative scale. Two approaches were adopted to predict wheat yield; (a) the regression approach, in which simulated potential yields were regressed with final estimated yields by Directorate of Economics and Statistics (DES) for each of the six major wheat growing states and (b) forcing approach in which LAI for each grid (25 km x 25 km) derived from remote sensing was forced into the simulation model to divert the simulation output and final grain yield into right direction. The deviations between the estimated state yield and reported yield were more in case of the forcing (0.7-25.4%) as compared to regression approach (0.5-9.2%). However, the spatial variability at grid level was explained more in case of forcing approach. Results indicated that regression approach is suitable for in season yield forecasting at state level and forcing approach is better for spatial crop condition assessment and crop growth monitoring.
机译：通过开发和证明作物生长监测和产量预测的方法进行了一项研究，可以提供与空间信息的周期性作物生长评估。该程序是开发的，以产生网格天气，将基于点的模拟模型Wofost（世界粮食研究）联系起来，以种植，土壤和天气等空间输入，并预测网格和行政规模的小麦产量。采用两种方法来预测小麦产量; （a）回归方法，其中模拟潜在收益率因经济学和统计局（DES）的最终估计产量，为六个主要小麦生长状态和（b）迫使每个网格的赖尔（25从遥感的源自遥感的KM x 25km）被迫进入仿真模型，以将仿真输出和最终谷物产量转移到正确的方向上。与回归方法相比（0.5-9.2％）相比，抑制状态产量和报告的产量之间的差异更高（0.7-25.4％）。然而，在强迫方法的情况下，更多地解释了网格级别的空间变异。结果表明，回归方式适用于季节产量预测，州水平预测，迫使方法对空间作物条件评估和作物生长监测更好。

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