Application of satellite imagery and remote sensing technology to estimate rice yield

Subject Areas : Farm water management with the aim of improving irrigation management indicators

حدیث یاقوتی ¹ (دانشجوی دکتری فیزیک و حفاظت خاک، دانشکده کشاورزی و منابع طبیعی، واحد علوم و تحقیقات، دانشگاه آزاد اسلامی، تهران، ایران)
ابراهیم پذیرا پذیرا ² (استادگروه خاکشناسی،دانشکده کشاورزی ومنابع طبیعی،واحدعلوم و تحقیقات،دانشگاه آزاد اسلامی، تهران، ایران.)
Ebrahim Amiri ³ (دانشگاه آزاد اسلامی واحد لاهیجان)
محمدحسن مسیح آبادی ⁴ (استادیار موسسه تحقیقات خاک و آب، تهران، ایران)

Received: 2017-04-04 Accepted : 2017-11-07 Published : 2018-04-21

Keywords: paddy rice, empirical models, Remote sensing, NDVI,

Abstract :

Rice is the main product of northern provinces of IRAN. Rice, as a strategic product that plays an important role in agricultural planning, needs to monitor annual production and crop area. Today, management practices can be improved using remote sensing technology and vegetation indices. The present study was conducted in order to determine the regression correlation between local and high-yielding varieties of rice in 2012, with NDVI, SAVI, DVI and RVI indices extracted from landsat7 ETM+ images, and to identify the most appropriate index in paddies Shaft. The highest coefficient of determination between yield and indices belonged to the flowering period, among these indices, NDVI is the most suitable indices for estimating the yield of local and high yielding rice varieties in the region. The statistical criteria show a good ability to simulate the model. Modeling efficiency (EF) were for local and high yielding varieties in crop year 2013 to the equivalent of 0.60 and 0.41 respectively, which represents a high efficient model in predicting product yield is acceptable percent. Modeling results indicate that the simulated yield level is well fitted with observed values. The results of the statistical test show that there is no significant difference between the observed and simulated values (P>0.05). The correlation coefficient of determination 0.70 and 0.66 between the observed and estimated yields in the evaluation of the models might be related to the changes in planting conditions and agricultural management, plant pests and diseases, climatic condition of the area and multiple local variables.

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