Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China

Abstract The significance of crop yield estimation is well known in agricultural management and policy development at regional and national levels. The primary objective of this study was to test the suitability of the method, depending on predicted crop production, to estimate crop yield with a MODIS-NDVI-based model on a regional scale. In this paper, MODIS-NDVI data, with a 250 m resolution, was used to estimate the winter wheat ( Triticum aestivum L.) yield in one of the main winter-wheat-growing regions. Our study region is located in Jining, Shandong Province. In order to improve the quality of remote sensing data and the accuracy of yield prediction, especially to eliminate the cloud-contaminated data and abnormal data in the MODIS-NDVI series, the Savitzky–Golay filter was applied to smooth the 10-day NDVI data. The spatial accumulation of NDVI at the county level was used to test its relationship with winter wheat production in the study area. A linear regressive relationship between the spatial accumulation of NDVI and the production of winter wheat was established using a stepwise regression method. The average yield was derived from predicted production divided by the growing acreage of winter wheat on a county level. Finally, the results were validated by the ground survey data, and the errors were compared with the errors of agro-climate models. The results showed that the relative errors of the predicted yield using MODIS-NDVI are between −4.62% and 5.40% and that whole RMSE was 214.16 kg ha −1 lower than the RMSE (233.35 kg ha −1 ) of agro-climate models in this study region. A good predicted yield data of winter wheat could be got about 40 days ahead of harvest time, i.e. at the booting-heading stage of winter wheat. The method suggested in this paper was good for predicting regional winter wheat production and yield estimation.

[1]  M. S. Rasmussen.  Assessment of millet yields and production in northern Burkina Faso using integrated NDVI from the AVHRR. , 1992 .

[2]  A. Gitelson,et al.  Use of a green channel in remote sensing of global vegetation from EOS- MODIS , 1996 .

[3]  A. Belward,et al.  The Best Index Slope Extraction ( BISE): A method for reducing noise in NDVI time-series , 1992 .

[4]  James Rowland,et al.  Estimating maize production in Kenya using NDVI: Some statistical considerations , 1998 .

[5]  C. Elvidge,et al.  Comparison of broad-band and narrow-band red and near-infrared vegetation indices , 1995 .

[6]  Didier Tanré,et al.  Second Simulation of the Satellite Signal in the Solar Spectrum, 6S: an overview , 1997, IEEE Trans. Geosci. Remote. Sens..

[7]  A. Huete,et al.  Optical-Biophysical Relationships of Vegetation Spectra without Background Contamination , 2000 .

[8]  Bo-Cai Gao,et al.  A practical method for simulating AVHRR-consistent NDVI data series using narrow MODIS channels in the 0.5-1.0 μm spectral range , 2000, IEEE Trans. Geosci. Remote. Sens..

[9]  Terry L. Kastens,et al.  Image masking for crop yield forecasting using AVHRR NDVI time series imagery , 2005 .

[10]  A. Huete,et al.  A comparison of vegetation indices over a global set of TM images for EOS-MODIS , 1997 .

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

[12]  B. Wardlow,et al.  Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains , 2007 .

[13]  T. Sakamoto,et al.  A crop phenology detection method using time-series MODIS data , 2005 .

[14]  M. Hayes,et al.  Using NOAA AVHRR data to estimate maize production in the United States Corn Belt , 1996 .

[15]  Hui Qing Liu,et al.  An error and sensitivity analysis of the atmospheric- and soil-correcting variants of the NDVI for the MODIS-EOS , 1994, IEEE Trans. Geosci. Remote. Sens..

[16]  D. R. Jones,et al.  A statistical inquiry into crop-weather dependence , 1982 .

[17]  I. Csiszár,et al.  On correlation of maize and wheat yield with NDVI: Example of Hungary (1985-1998) , 2002 .

[18]  P. Beck,et al.  Improved monitoring of vegetation dynamics at very high latitudes: A new method using MODIS NDVI , 2006 .

[19]  James Rowland,et al.  A weighted least-squares approach to temporal NDVI smoothing , 1999 .

[20]  James E. McMurtrey,et al.  Relationship of spectral data to grain yield variation , 1980 .

[21]  Changsheng Li,et al.  Mapping paddy rice agriculture in southern China using multi-temporal MODIS images , 2005 .

[22]  A. Huete,et al.  MODIS Vegetation Index Compositing Approach: A Prototype with AVHRR Data , 1999 .

[23]  J. Cihlar.  Identification of contaminated pixels in AVHRR composite images for studies of land biosphere , 1996 .

[24]  Jin Chen,et al.  A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter , 2004 .

[25]  P. K. Sharma,et al.  Wheat production forecasts using remote sensing and other techniques - experience of Punjab State. , 2000 .

[26]  C. Justice,et al.  Development of vegetation and soil indices for MODIS-EOS , 1994 .

[27]  Yoram J. Kaufman,et al.  MODIS NDVI Optimization To Fit the AVHRR Data Series—Spectral Considerations , 1998 .

[28]  R. Lunetta,et al.  Land-cover change detection using multi-temporal MODIS NDVI data , 2006 .

[29]  Wenpeng Lin,et al.  Winter wheat yield estimation based on MODIS EVI , 2005 .

[30]  A. J. Stern,et al.  Crop Yield Assessment from Remote Sensing , 2003 .

[31]  A. Huete,et al.  Overview of the radiometric and biophysical performance of the MODIS vegetation indices , 2002 .

[32]  S. S. Ray,et al.  Crop assessment using remote sensing - Part-II: Crop condition and yield assessment. , 2000 .

[33]  A. Savitzky,et al.  Smoothing and Differentiation of Data by Simplified Least Squares Procedures. , 1964 .

[34]  M. Salam,et al.  Comparing Simulated and Measured Values Using Mean Squared Deviation and its Components , 2000 .

[35]  V. N. Sridhar,et al.  A non-linear regression form for vegetation index-crop yield relation incorporating acquisition date normalization , 1997 .

[36]  D. Tanré,et al.  Strategy for direct and indirect methods for correcting the aerosol effect on remote sensing: From AVHRR to EOS-MODIS , 1996 .

[37]  Masami Kaneko,et al.  Comparison of image data acquired with AVHRR, MODIS, ETM+ and ASTER over Hokkaido, Japan , 2003 .

[38]  S. M. E. Groten,et al.  NDVI—crop monitoring and early yield assessment of Burkina Faso , 1993 .

[39]  W. Bastiaanssen,et al.  A new crop yield forecasting model based on satellite measurements applied across the Indus Basin, Pakistan , 2003 .

[40]  W. Verhoef,et al.  Reconstructing cloudfree NDVI composites using Fourier analysis of time series , 2000 .

[41]  Raghavan Srinivasan,et al.  Using Satellite and Field Data with Crop Growth Modeling to Monitor and Estimate Corn Yield in Mexico , 2002 .

[42]  M. Mkhabela,et al.  Early maize yield forecasting in the four agro-ecological regions of Swaziland using NDVI data derived from NOAA's-AVHRR , 2005 .

[43]  Per Jönsson,et al.  Seasonality extraction by function fitting to time-series of satellite sensor data , 2002, IEEE Trans. Geosci. Remote. Sens..

[44]  T. M. Stout,et al.  Central Great Plains , 1965 .