Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions

Regional crop yield prediction is a significant component of national food policy making and security assessments. A data assimilation method that combines crop growth models with remotely sensed data has been proven to be the most effective method for regional yield estimates. This paper describes an assimilation method that integrates a time series of leaf area index (LAI) retrieved from ETM+ data and a coupled hydrology-crop growth model which links a crop growth model World Food Study (WOFOST) and a hydrology model HYDRUS-1D for regional maize yield estimates using the ensemble Kalman filter (EnKF). The coupled hydrology-crop growth model was calibrated and validated using field data to ensure that the model accurately simulated associated state variables and maize growing processes. To identify the parameters that most affected model output, an extended Fourier amplitude sensitivity test (EFAST) was applied to the model before calibration. The calibration results indicated that the coupled hydrology-crop growth model accurately simulated maize growth processes for the local cultivation variety tested. The coefficient of variations (CVs) for LAI, total above-ground production (TAGP), dry weight of storage organs (WSO), and evapotranspiration (ET) were 13%, 6.9%, 11% and 20%, respectively. The calibrated growth model was then combined with the regional ETM+ LAI data using a sequential data assimilation algorithm (EnKF) to incorporate spatial heterogeneity in maize growth into the coupled hydrology-crop growth model. The theoretical LAI profile for the near future and the final yield were obtained through the EnKF algorithm for 50 sample plots. The CV of the regional yield estimates for these sample plots was 8.7%. Finally, the maize yield distribution for the Zhangye Oasis was obtained as a case study. In general, this research and associated model could be used to evaluate the impacts of irrigation, fertilizer and field management on crop yield at a regional scale. Crown Copyright (C) 2014 Published by Elsevier B.V. All rights reserved.

[1]  Atsushi Maruyama,et al.  Coupling land surface and crop growth models to estimate the effects of changes in the growing season on energy balance and water use of rice paddies , 2010 .

[2]  Miroslav Trnka,et al.  Comparison of CERES, WOFOST and SWAP models in simulating soil water content during growing season under different soil conditions , 2004 .

[3]  R. Feddes,et al.  Simulation of field water use and crop yield , 1978 .

[4]  Felix Kogan,et al.  Global drought and flood-watch from NOAA polar-orbitting satellites , 1998 .

[5]  G. Hoogenboom,et al.  Integration of MODIS LAI and vegetation index products with the CSM–CERES–Maize model for corn yield estimation , 2011 .

[6]  Michael E. Schaepman,et al.  A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling , 2007, Int. J. Appl. Earth Obs. Geoinformation.

[7]  Xin Li,et al.  Modelling irrigated maize with a combination of coupled-model simulation and uncertainty analysis, in the northwest of China , 2012 .

[8]  J. Goudriaan,et al.  Simulation of crop growth for potential and water - limited production situations : as applied to spring wheat , 1992 .

[9]  Geir Evensen,et al.  The Ensemble Kalman Filter: theoretical formulation and practical implementation , 2003 .

[10]  M. Benoît,et al.  Spatial dynamics of farming practices in the Seine basin: methods for agronomic approaches on a regional scale. , 2007, The Science of the total environment.

[11]  G. Evensen,et al.  Analysis Scheme in the Ensemble Kalman Filter , 1998 .

[12]  S. Running,et al.  Relationship of thematic mapper simulator data to leaf area index , 1987 .

[13]  K. K. Mayo,et al.  Monitoring land-cover change by principal component analysis of multitemporal Landsat data. , 1980 .

[14]  A. Huete,et al.  MODIS VEGETATION INDEX ( MOD 13 ) ALGORITHM THEORETICAL BASIS DOCUMENT Version 3 . 1 Principal Investigators , 1999 .

[15]  Steven W. Running,et al.  Remote sensing of temperate coniferous forest leaf area index The influence of canopy closure, understory vegetation and background reflectance , 1990 .

[16]  O. Klepper,et al.  FSEOPT a FORTRAN program for calibration and uncertainty analysis of simulation models. , 1992 .

[17]  Limin Yang,et al.  COMPLETION OF THE 1990S NATIONAL LAND COVER DATA SET FOR THE CONTERMINOUS UNITED STATES FROM LANDSAT THEMATIC MAPPER DATA AND ANCILLARY DATA SOURCES , 2001 .

[18]  Jean-Paul Chilès,et al.  Wiley Series in Probability and Statistics , 2012 .

[19]  W. Cohen,et al.  Landsat's Role in Ecological Applications of Remote Sensing , 2004 .

[20]  W. Cohen,et al.  North American forest disturbance mapped from a decadal Landsat record , 2008 .

[21]  C. A. van Diepen,et al.  User's guide for the WOFOST 7.1 crop growth simulation model and WOFOST Control Center 1.5. , 1998 .

[22]  David B Lobell,et al.  Remote sensing of soil degradation: introduction. , 2010, Journal of environmental quality.

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

[24]  Pierre Defourny,et al.  Potential performances of remotely sensed LAI assimilation in WOFOST model based on an OSS experiment , 2011 .

[25]  Gregory Duveiller,et al.  Estimating regional winter wheat yield with WOFOST through the assimilation of green area index retrieved from MODIS observations , 2012 .

[26]  S. Moulin Impacts of model parameter uncertainties on crop reflectance estimates: a regional case study on wheat , 1999 .

[27]  Xin Li,et al.  Improving the estimation of hydrothermal state variables in the active layer of frozen ground by assimilating in situ observations and SSM/I data , 2009 .

[28]  E. Hanert,et al.  Simulating dynamic crop growth with an adapted land surface model - JULES-SUCROS : Model development and validation , 2011 .

[29]  Jindi Wang,et al.  Leaf area index estimation from MODIS data using the ensemble Kalman smoother method , 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

[30]  W. G. M. Bastiaanssen,et al.  Assimilation of satellite data into agrohydrological models to improve crop yield forecasts , 2009 .

[31]  D. Roy,et al.  A method for integrating MODIS and Landsat data for systematic monitoring of forest cover and change in the Congo Basin , 2008 .

[32]  A. Shepherd,et al.  Simulation of the effect of water shortage on the yields of winter wheat in North-East England , 2002 .

[33]  Shen Shuang-he Applicability of PyWOFOST Model Based on Ensemble Kalman Filter in Simulating Maize Yield in Northeast China , 2012 .

[34]  R. Colombo,et al.  Retrieval of leaf area index in different vegetation types using high resolution satellite data , 2003 .

[35]  F. Kogan,et al.  Monitoring Brazilian soybean production using NOAA/AVHRR based vegetation condition indices , 2002 .

[36]  G. Hoogenboom,et al.  Understanding Options for Agricultural Production , 1998, Systems Approaches for Sustainable Agricultural Development.

[37]  Wei Su,et al.  Estimating regional winter wheat yield by assimilation of time series of HJ-1 CCD NDVI into WOFOST-ACRM model with Ensemble Kalman Filter , 2013, Math. Comput. Model..

[38]  J. Monteith Evaporation and surface temperature , 2007 .

[39]  Jianxi Huang,et al.  Regional Crop Yield Assessment by Combination of a Crop Growth Model and Phenology Information Derived from MODIS , 2011 .

[40]  I. Rodríguez‐Iturbe,et al.  Intensive or extensive use of soil moisture: Plant strategies to cope with stochastic water availability , 2001 .

[41]  Chunlin Huang,et al.  Experiments of one-dimensional soil moisture assimilation system based on ensemble Kalman filter , 2008 .

[42]  Didier Tanré,et al.  Atmospherically resistant vegetation index (ARVI) for EOS-MODIS , 1992, IEEE Trans. Geosci. Remote. Sens..

[43]  G. Evensen Sequential data assimilation with a nonlinear quasi‐geostrophic model using Monte Carlo methods to forecast error statistics , 1994 .

[44]  Michael D. McKay,et al.  Evaluating Prediction Uncertainty , 1995 .

[45]  D. McLaughlin,et al.  Hydrologic Data Assimilation with the Ensemble Kalman Filter , 2002 .

[46]  S. Gayler,et al.  The impact of crop growth sub-model choice on simulated water and nitrogen balances , 2006, Nutrient Cycling in Agroecosystems.

[47]  J. Monteith Evaporation and environment. , 1965, Symposia of the Society for Experimental Biology.

[48]  M. Anwar,et al.  Water-use efficiency and the effect of water deficits on crop growth and yield of Kabuli chickpea (Cicer arietinum L.) in a cool-temperate subhumid climate , 2003, The Journal of Agricultural Science.

[49]  Jeffrey P. Walker,et al.  Extended versus Ensemble Kalman Filtering for Land Data Assimilation , 2002 .

[50]  Robin Matthews,et al.  Crop-Soil Simulation Models: Applications in Developing Countries , 2002 .

[51]  R. Lawford,et al.  Large-scale simulation of wheat yields in a semi-arid environment using a crop-growth model , 1999 .

[52]  J. Judge,et al.  Estimation of energy and moisture fluxes for dynamic vegetation using coupled SVAT and crop‐growth models , 2008 .

[53]  Xu Ziwei,et al.  A Study on the Data Processing and Quality Assessment of the Eddy Covariance System , 2008 .

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

[55]  H.F.M. ten Berge,et al.  ORYZA2000 : modeling lowland rice , 2001 .

[56]  Jun Qin,et al.  Data Assimilation Methods for Land Surface Variable Estimation , 2008 .

[57]  B. Barrett,et al.  Carbon cycling of European croplands: A framework for the assimilation of optical and microwave Earth observation data , 2013 .

[58]  Rafael Muñoz-Carpena,et al.  Parameter importance and uncertainty in predicting runoff pesticide reduction with filter strips. , 2010, Journal of environmental quality.

[59]  Huang Chunlin,et al.  Development of a Chinese land data assimilation system: its progress and prospects , 2007 .

[60]  L. Dente,et al.  Assimilation of leaf area index derived from ASAR and MERIS data into CERES - wheat model to map wheat yield , 2008 .

[61]  Philip Lewis,et al.  Assimilating canopy reflectance data into an ecosystem model with an Ensemble Kalman Filter , 2008 .

[62]  Jianxi Huang,et al.  Assimilation of MODIS-LAI into the WOFOST model for forecasting regional winter wheat yield , 2013, Math. Comput. Model..

[63]  S. Running,et al.  The seasonality of AVHRR data of temperate coniferous forests - Relationship with leaf area index , 1990 .

[64]  Joost Wolf,et al.  Comparison of two soya bean simulation models under climate change : I Model calibration and sensitivity analyses , 2002 .

[65]  W. Cohen,et al.  Comparison of Tasseled Cap-based Landsat data structures for use in forest disturbance detection , 2005 .