Development of a Biophysical Rice Yield Model Using All-weather Climate Data

With the increasing socio-economic importance of rice as a global staple food, several models have been developed for rice yield estimation by combining remote sensing data with carbon cycle modelling. In this study, we aimed to estimate rice yield in Korea using such an integrative model using satellite remote sensing data in combination with a biophysical crop growth model. Specifically, daily meteorological inputs derived from MODIS (Moderate Resolution imaging Spectroradiometer) and radar satellite products were used to run a light use efficiency based crop growth model, which is based on the MODIS gross primary production (GPP) algorithm. The modelled biomass was converted to rice yield using a harvest index model. We estimated rice yield from 2003 to 2014 at the county level and evaluated the modelled yield using the official rice yield and rice straw biomass statistics of Statistics Korea (KOSTAT). The estimated rice biomass, yield, and harvest index and their spatial distributions were investigated. Annual mean rice yield at the national level showed a good agreement with the yield statistics with the yield statistics, a mean error (ME) of +0.56% and a mean absolute error (MAE) of 5.73%. The estimated county level yield resulted in small ME (+0.10~+2.00%) and MAE (2.10~11.62%), respectively. Compared to the county-level yield statistics, the rice yield was over estimated in the counties in Gangwon province and under estimated in the urban and coastal counties in the south of Chungcheong province. Compared to the rice straw statistics, the estimated rice biomass showed similar error patterns with the yield estimates. The subpixel heterogeneity of the 1 km MODIS FPAR (Fraction of absorbed Photosynthetically Active Radiation) may have attributed to these errors. In addition, the growth and harvest index models can be further developed to take account of annually varying growth conditions and growth timings.

[1]  Suk-Young Hong,et al.  The Evaluation of Meteorological Inputs retrieved from MODIS for Estimation of Gross Primary Productivity in the US Corn Belt Region , 2011 .

[2]  Maosheng Zhao,et al.  Development of a global evapotranspiration algorithm based on MODIS and global meteorology data , 2007 .

[3]  A. J. Stern,et al.  Application of MODIS derived parameters for regional crop yield assessment , 2005 .

[4]  Sinkyu Kang,et al.  Comparisons of Collection 5 and 6 Aqua MODIS07_L2 air and Dew Temperature Products with Ground-Based Observation Dataset , 2014 .

[5]  John S. Kimball,et al.  Retrievals of All-Weather Daily Air Temperature Using MODIS and AMSR-E Data , 2014, Remote. Sens..

[6]  Suk Young Hong,et al.  Estimating Rice Yield Using MODIS NDVI and Meteorological Data in Korea , 2012 .

[7]  Sinkyu Kang,et al.  The Estimation of Gross Primary Productivity over North Korea Using MODIS FPAR and WRF Meteorological Data , 2012 .

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

[9]  Sinkyu Kang,et al.  Detection of Irrigation Timing and the Mapping of Paddy Cover in Korea Using MODIS Images Data , 2011 .

[10]  D. Lobell,et al.  Greater Sensitivity to Drought Accompanies Maize Yield Increase in the U.S. Midwest , 2014, Science.

[11]  Frank Ewert,et al.  Crop modelling for integrated assessment of risk to food production from climate change , 2015, Environ. Model. Softw..

[12]  J. Kiniry,et al.  Maize yield potential: critical processes and simulation modeling in a high-yielding environment , 2004 .

[13]  Keunchang Jang,et al.  Evaluation of Shortwave Irradiance and Evapotranspiration Derived from Moderate Resolution Imaging Spectroradiometer (MODIS) , 2009 .

[14]  C. Stöckle,et al.  CropSyst, a cropping systems simulation model , 2003 .

[15]  James W. Jones,et al.  The DSSAT cropping system model , 2003 .

[16]  J. R. Kiniry,et al.  CERES-Maize: a simulation model of maize growth and development , 1986 .

[17]  John S. Kimball,et al.  Monitoring daily evapotranspiration in Northeast Asia using MODIS and a regional Land Data Assimilation System , 2013 .

[18]  R. Bird,et al.  Simplified clear sky model for direct and diffuse insolation on horizontal surfaces , 1981 .

[19]  J. Wolf,et al.  WOFOST: a simulation model of crop production. , 1989 .

[20]  Jiahua Zhang,et al.  Estimation of maize yield by using a process-based model and remote sensing data in the Northeast China Plain , 2015 .

[21]  Fulu Tao,et al.  Remote sensing of crop production in China by production efficiency models: models comparisons, estimates and uncertainties , 2005 .

[22]  K. Mccree,et al.  An equation for the rate of respiration of white clover grown under controlled conditions. , 1970 .

[23]  Jihye Lee,et al.  A comparative study for reconstructing a high-quality NDVI time series data derived from MODIS surface reflectance , 2015 .

[24]  Christopher Conrad,et al.  Validation of the collection 5 MODIS FPAR product in a heterogeneous agricultural landscape in arid Uzbekistan using multitemporal RapidEye imagery , 2012 .