Crop yield estimation model for Iowa using remote sensing and surface parameters
Crop yield estimation model for Iowa using remote sensing and surface parameters
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DOI：
10.1016/j.jag.2005.06.002
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发表时间：
2006
影响因子：
7.5
通讯作者：
A. Prasad;L. Chai;Ramesh P. Singh;M. Kafatos
中科院分区：
文献类型：
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作者：
A. Prasad;L. Chai;Ramesh P. Singh;M. Kafatos
已经作出了许多努力，利用遥感数据制定各种指数，如归一化差异植被指数、植被状况指数和温度状况指数，用于干旱绘图和监测以及植被健康和生产力评估。归一化差异植被指数、土壤湿度、地表温度和降雨量是估计和预测作物状况的宝贵信息来源。利用美国爱荷华州19年的植被指数、土壤湿度、地表温度和降雨量资料，采用带断点的分段线性回归方法进行作物产量评估和预测。作物生产环境由其内在的异质性源及其非线性行为组成。利用非线性拟牛顿多变量优化方法，合理地减少产量预测中的不一致性和误差。最小二乘损失函数的最小化已通过迭代收敛进行，使用预定义的经验方程，提供了可接受的较低的残差值与预测值非常接近的玉米和大豆作物（R2 =0.86）为爱荷华州的观察值（R2 =0.78）。本文所讨论的作物产量预测模型将随着长周期数据的使用而进一步完善。类似的模型可以为其他地区的不同作物开发。
Numerous efforts have been made to develop various indices using remote sensing data such as normalized difference vegetation index (NDVI), vegetation condition index (VCI) and temperature condition index (TCI) for mapping and monitoring of drought and assessment of vegetation health and productivity. NDVI, soil moisture, surface temperature and rainfall are valuable sources of information for the estimation and prediction of crop conditions. In the present paper, we have considered NDVI, soil moisture, surface temperature and rainfall data of Iowa state, US, for 19 years for crop yield assessment and prediction using piecewise linear regression method with breakpoint. Crop production environment consists of inherent sources of heterogeneity and their non-linear behavior. A non-linear Quasi-Newton multi-variate optimization method is utilized, which reasonably minimizes inconsistency and errors in yield prediction. Minimization of least square loss function has been carried out through iterative convergence using pre-defined empirical equation that provided acceptable lower residual values with predicted values very close to observed ones (R2=0.78) for Corn and Soybean crop (R2=0.86) for Iowa state. The crop yield prediction model discussed in the present paper will further improve in future with the use of long period dataset. Similar model can be developed for different crops of other locations.