The Feasibility of Using Vegetation Indices and Soil Texture to Predict Rice Yield

Identifying plant-environment interactions along with remote sensing provides grounds for designing management methods as well as predicting rice yield in different conditions; accordingly, it is very helpful to use vegetation indices for identifying the vegetation and greenness of farms. The regression between the local and high-yield varieties of rice in 2012 and the NDVI, SAVI, LAI, DVI, and RVI indices derived from Landsat 7 in northern Iran indicate the superiority of the NDVI index in the flowering stage of rice. Results show that the coefficient of determination of the fitted model for local and high-yielding varieties is 0.71 and 0.70, respectively, which indicates the good consistency of the results with the regional data. We evaluated the models for the local and high-yielding varieties in crop year 2013 with RMSE of 406 and 272 kg ha-1 and NRMSE of 12% and 6%, respectively. Moreover, the simulation results show that the yield of the models is well fitted with the observed values; besides, there is high correlation (R>0.80) between the real and predicted yield values. As shown by the investigation of the region’s soil texture, the fine-texture paddy fields have better yield.

[1]  S. Szabó,et al.  Influence of soil properties on crop yield: a multivariate statistical approach , 2015 .

[2]  Atiqur Rahman,et al.  Use of Vegetation Health Data for Estimation of Aus Rice Yield in Bangladesh , 2009, Sensors.

[3]  M. Rezaei,et al.  Improving Agricultural Management in a Large‐Scale Paddy Field by Using Remotely Sensed Data in the Ceres‐Rice Model , 2016 .

[4]  Ramesh P. Singh,et al.  Crop yield estimation model for Iowa using remote sensing and surface parameters , 2006 .

[5]  Abd-Elraouf M. Ali,et al.  Rice yield forecasting models using satellite imagery in Egypt , 2013 .

[6]  C. Jordan.  Derivation of leaf-area index from quality of light on the forest floor , 1969 .

[7]  Farhad Khormali,et al.  Spatial variability of some soil properties for site specific farming in northern Iran , 2012 .

[8]  Quazi K. Hassan,et al.  Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review , 2015, Sensors.

[9]  Q. Cheng,et al.  Mapping paddy rice yield in Zhejiang Province using MODIS spectral index , 2011 .

[10]  Keith D. Shepherd,et al.  Unravelling the effects of soil and crop management on maize productivity in smallholder agricultural systems of western Kenya—An application of classification and regression tree analysis , 2008 .

[11]  C. A. van Diepen,et al.  Crop model data assimilation with the Ensemble Kalman filter for improving regional crop yield forecasts , 2007 .

[12]  K. Cassman,et al.  Estimating crop yield potential at regional to national scales , 2013 .

[13]  R. Ali,et al.  An approach for precision farming under pivot irrigation system using remote sensing and GIS techniques , 2011 .

[14]  K. Sahrawat,et al.  Relationships of barley biomass and grain yields to soil properties within a field in the arid region: Use of factor analysis , 2009 .

[15]  Nguyen Hieu Trung,et al.  A comparative analysis of multitemporal MODIS EVI and NDVI data for large-scale rice yield estimation , 2014 .

[16]  J. A. Schell,et al.  Monitoring vegetation systems in the great plains with ERTS , 1973 .

[17]  Lin Wang,et al.  Multi-Temporal Detection of Rice Phenological Stages Using Canopy Spectrum , 2014 .

[18]  F. Nishio,et al.  Rice Yield Estimation Using Landsat ETM+ Data and Field Observation , 2011 .

[19]  N. U. Ahmed,et al.  Relations between evaporation coefficients and vegetation indices studied by model simulations , 1994 .

[20]  M. A.M.,et al.  Simulating Phenology, Growth and Yield of Transplanted Rice at Different Seedling Ages in Northern Iran Using ORYZA2000 , 2012 .

[21]  A. Tagar,et al.  Impact of RIce cRop on SoIl QualIty and feRtIlIty * 1 , 2013 .

[22]  Y. Mo,et al.  Effect of Continuous Cropping on Soil Chemical Properties and Crop Yield in Banana Plantation , 2014 .

[23]  M. R. Neishabouri,et al.  Delineation of site specific nutrient management zones for a paddy cultivated area based on soil fertility using fuzzy clustering , 2012 .

[24]  S. M. Ali صالح مهدي علي,et al.  Gap-Filling Restoration Methods for ETM+ Sensor Images , 2013 .

[25]  P. Sánchez.  Tripling crop yields in tropical Africa , 2010 .

[26]  Hongwei Zhang,et al.  THE MODEL OF WHEAT YIELD FORECAST BASED ON MODIS-NDVI ——A CASE STUDY OF XINXIANG , 2012 .

[27]  Inbal Becker-Reshef,et al.  Rice yield estimation using Landsat ETM+ Data , 2015 .

[28]  Comparison of regression models to predict potential yield of wheat from some measured soil properties. , 2015 .

[29]  Sayed M. Arafat,et al.  Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta , 2011 .

[30]  Sudhanshu Sekhar Panda,et al.  Application of Vegetation Indices for Agricultural Crop Yield Prediction Using Neural Network Techniques , 2010, Remote. Sens..

[31]  Hua Li,et al.  Estimating Rice Yield by HJ-1A Satellite Images , 2011 .

[32]  J. Everitt,et al.  Using spectral vegetation indices to estimate rangeland productivity , 1992 .

[33]  A. Huete.  A soil-adjusted vegetation index (SAVI) , 1988 .

[34]  S. Lakshmi,et al.  A Comparison of Soil Texture Distribution and Soil Moisture Mapping of Chennai Coast using Landsat ETM+ and IKONOS Data , 2015 .