Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data

Abstract Crop yield estimation at regional scale using crop model is generally subjected to large uncertainties from insufficient spatial information on heterogeneous growth environment and agronomic management practices. To solve this problem, we assimilated crop phenology and leaf area index (LAI) derived from remote sensing into a crop model (MCWLA-Wheat) to improve its reliability in estimating winter wheat yields at regional scale. Since the LAI magnitude was obviously underestimated however its spatial pattern was relatively well captured by remote sensing, we developed a novel spatial assimilation scheme that assimilated the spatial differences instead of the absolute values of LAI into crop model. Firstly, we retrieved the information of critical development stages of winter wheat from remote sensing data to adjust the simulation of phenology by MCWLA-Wheat model; then the spatial differences of LAI derived from remote sensing were assimilated into the MCWLA-Wheat model using a kind of constant gain Kalman Filter algorithm to improve the ability of the model in estimating winter wheat LAI and yields at regional scale in the North China Plain. This assimilation scheme extracted effective information from remote sensing LAI and meanwhile abandoned the information with obvious errors, ensuring that the assimilation variables could be close to the reality. It avoids the requirement for correction of the LAI derived from remote sensing using other high-quality ancillary data from field measurements. Using this assimilation scheme, the performance of crop model improved substantially. It successfully produced more accurate yield estimates at regional scale during the period of 2001–2008 (mean R 2  = 0.42, RMSE = 737/ha) than those without assimilation (mean R 2  = 0.26, RMSE = 1012 kg/ha) and those directly assimilating the absolute LAI values derived from remote sensing (mean R 2  = 0.30, RMSE = 1257/ha). Our findings demonstrated a reliable and promising assimilation scheme for improving yield estimation of crop model at regional scale with low data requirement.

[1]  Yang Fei,et al.  Assessment of MODIS LAI product accuracy based on the PROSAIL model, TM and field measurements. , 2010 .

[2]  Dehai Zhu,et al.  Improving winter wheat yield estimation by assimilation of the leaf area index from Landsat TM and MODIS data into the WOFOST model , 2015 .

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

[4]  M. Claverie,et al.  Maize and sunflower biomass estimation in southwest France using high spatial and temporal resolution remote sensing data , 2012 .

[5]  F. Baret,et al.  Influence of landscape spatial heterogeneity on the non-linear estimation of leaf area index from moderate spatial resolution remote sensing data , 2006 .

[6]  Elizabeth Pattey,et al.  Using Leaf Area Index, retrieved from optical imagery, in the STICS crop model for predicting yield and biomass of field crops , 2012 .

[7]  Yanxia Zhao,et al.  Assimilating remote sensing information with crop model using Ensemble Kalman Filter for improving LAI monitoring and yield estimation , 2013 .

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

[9]  I. C. Prentice,et al.  Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model , 2003 .

[10]  James Kennedy,et al.  Particle swarm optimization , 2002, Proceedings of ICNN'95 - International Conference on Neural Networks.

[11]  Jianxi Huang,et al.  Assimilating a synthetic Kalman filter leaf area index series into the WOFOST model to improve regional winter wheat yield estimation , 2016 .

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

[13]  Shenghui Fang,et al.  Leaf Area Index Estimation Using Time-Series MODIS Data in Different Types of Vegetation , 2014, Journal of the Indian Society of Remote Sensing.

[14]  Jianxi Huang,et al.  Improving the timeliness of winter wheat production forecast in the United States of America, Ukraine and China using MODIS data and NCAR Growing Degree Day information , 2015 .

[15]  Marie Weiss,et al.  Mapping Biophysical Variables From Solar and Thermal Infrared Remote Sensing: Focus on Agricultural Landscapes With Spatial Heterogeneity , 2014, IEEE Geoscience and Remote Sensing Letters.

[16]  Shenfeng Fei,et al.  Ecological forecasting and data assimilation in a data-rich era. , 2011, Ecological applications : a publication of the Ecological Society of America.

[17]  Qingguo Zhou,et al.  Assimilating remote sensing information into a coupled hydrology-crop growth model to estimate regional maize yield in arid regions , 2014 .

[18]  M. Guérif,et al.  Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications , 2005 .

[19]  Frédéric Baret,et al.  Using Thermal Time and Pixel Purity for Enhancing Biophysical Variable Time Series: An Interproduct Comparison , 2013, IEEE Transactions on Geoscience and Remote Sensing.

[20]  James W. Jones,et al.  Uncertainty in Simulating Wheat Yields Under Climate Change , 2013 .

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

[22]  F. Baret,et al.  Remotely sensed green area index for winter wheat crop monitoring:10-Year assessment at regional scale over a fragmented landscape , 2012 .

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

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

[25]  G. Collatz,et al.  Physiological and environmental regulation of stomatal conductance, photosynthesis and transpiration: a model that includes a laminar boundary layer , 1991 .

[26]  James Hansen,et al.  Assimilation of remotely sensed soil moisture and vegetation with a crop simulation model for maize yield prediction , 2013 .

[27]  C. Müller,et al.  Modelling the role of agriculture for the 20th century global terrestrial carbon balance , 2007 .

[28]  Zhao Zhang,et al.  Response of crop yields to climate trends since 1980 in China , 2012 .

[29]  Jiyuan Liu,et al.  Tracking the dynamics of paddy rice planting area in 1986–2010 through time series Landsat images and phenology-based algorithms , 2015 .

[30]  Michael A. Lefsky,et al.  A flexible spatiotemporal method for fusing satellite images with different resolutions , 2016 .

[31]  Jindi Wang,et al.  Use of General Regression Neural Networks for Generating the GLASS Leaf Area Index Product From Time-Series MODIS Surface Reflectance , 2014, IEEE Transactions on Geoscience and Remote Sensing.

[32]  M. Bindi,et al.  Contribution of crop model structure, parameters and climate projections to uncertainty in climate change impact assessments , 2018, Global change biology.

[33]  Jiyuan Liu,et al.  Modelling the impacts of weather and climate variability on crop productivity over a large area: A new super-ensemble-based probabilistic projection , 2009 .

[34]  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.

[35]  Zhuoqi Chen,et al.  Validation of China-wide interpolated daily climate variables from 1960 to 2011 , 2015, Theoretical and Applied Climatology.

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

[37]  A. Challinor,et al.  Design and optimisation of a large-area process-based model for annual crops , 2004 .

[38]  Baohui Zhang,et al.  Assessment of the MODIS LAI Product Using Ground Measurement Data and HJ-1A/1B Imagery in the Meadow Steppe of Hulunber, China , 2014, Remote. Sens..

[39]  Andrew E. Suyker,et al.  A Two-Step Filtering approach for detecting maize and soybean phenology with time-series MODIS data , 2010 .

[40]  F. Hall,et al.  Global Crop Forecasting , 1980, Science.

[41]  C. Hutchinson,et al.  Uses of satellite data for famine early warning in sub-Saharan Africa , 1991 .

[42]  G. Collatz,et al.  Coupled Photosynthesis-Stomatal Conductance Model for Leaves of C4 Plants , 1992 .

[43]  Christopher Conrad,et al.  Optimising Phenological Metrics Extraction for Different Crop Types in Germany Using the Moderate Resolution Imaging Spectrometer (MODIS) , 2017, Remote. Sens..

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

[45]  Nereu Augusto Streck,et al.  Improving predictions of developmental stages in winter wheat: a modified Wang and Engel model ☆ , 2003 .

[46]  Huajun Tang,et al.  Regional yield estimation for winter wheat with MODIS-NDVI data in Shandong, China , 2008, Int. J. Appl. Earth Obs. Geoinformation.

[47]  Thuy Le Toan,et al.  Potentiality of optical and radar satellite data at high spatio-temporal resolutions for the monitoring of irrigated wheat crops in Morocco , 2010, Int. J. Appl. Earth Obs. Geoinformation.

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

[49]  J. Berry,et al.  A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species , 1980, Planta.

[50]  J. Paz,et al.  Examples of strategies to analyze spatial and temporal yield variability using crop models , 2002 .

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

[52]  S. Liang,et al.  Validation of MODIS and CYCLOPES LAI products using global field measurement data , 2012 .

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

[54]  I. Prentice,et al.  A general model for the light-use efficiency of primary production , 1996 .

[55]  Xiaojun Xu,et al.  Method for improvement of MODIS leaf area index products based on pixel-to-pixel correlations , 2016 .

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

[57]  Jeffrey W. White,et al.  Rising Temperatures Reduce Global Wheat Production , 2015 .

[58]  Y. Knyazikhin,et al.  Validation and intercomparison of global Leaf Area Index products derived from remote sensing data , 2008 .

[59]  F. Tao,et al.  Climate change, wheat productivity and water use in the North China Plain: A new super-ensemble-based probabilistic projection , 2013 .

[60]  Wang Zhi-gang,et al.  A modified particle swarm optimization , 2009 .

[61]  Jing Wang,et al.  Estimating near future regional corn yields by integrating multi-source observations into a crop growth model , 2013 .

[62]  R. P. Shimpi,et al.  Modified Particle Swarm Optimization , 2009 .

[63]  Taifeng Dong,et al.  Remote Sensing Based Detection of Crop Phenology for Agricultural Zones in China Using a New Threshold Method , 2013, Remote. Sens..

[64]  R. Reichle.  Data assimilation methods in the Earth sciences , 2008 .

[65]  David B. Lobell,et al.  Remote sensing of regional crop production in the Yaqui Valley, Mexico: estimates and uncertainties , 2003 .

[66]  F. Baret,et al.  Crop specific green area index retrieval from MODIS data at regional scale by controlling pixel-target adequacy , 2011 .