Monitoring maize growth on the North China Plain using a hybrid genetic algorithm-based back-propagation neural network model

[1]  Li Li,et al.  Monitoring maize growth conditions by training a BP neural network with remotely sensed vegetation temperature condition index and leaf area index , 2019, Comput. Electron. Agric..

[2]  Xin Qi,et al.  Feature-based phase retrieval wavefront sensing approach using machine learning. , 2018, Optics express.

[3]  Li Li,et al.  Developing an integrated indicator for monitoring maize growth condition using remotely sensed vegetation temperature condition index and leaf area index , 2018, Comput. Electron. Agric..

[4]  Özgür Kisi,et al.  Spatial and multi-depth temporal soil temperature assessment by assimilating satellite imagery, artificial intelligence and regression based models in arid area , 2018, Comput. Electron. Agric..

[5]  Xiaocui Wu,et al.  Numerical Terradynamic Simulation Group 2-2018 Regional Crop Gross Primary Productivity and Yield Estimation Using Fused Landsat-MODIS Data , 2018 .

[6]  Li Li,et al.  Mapping MODIS LST NDVI Imagery for Drought Monitoring in Punjab Pakistan , 2018, IEEE Access.

[7]  Lei Wang,et al.  Assimilation of the leaf area index and vegetation temperature condition index for winter wheat yield estimation using Landsat imagery and the CERES-Wheat model , 2017 .

[8]  Dailiang Peng,et al.  Improved modeling of gross primary production from a better representation of photosynthetic components in vegetation canopy , 2017 .

[9]  Huajun Tang,et al.  Perspective of Chinese GF-1 high-resolution satellite data in agricultural remote sensing monitoring , 2017 .

[10]  Salvatore L. Cosentino,et al.  Physiological responses of Arundo donax ecotypes to drought: a common garden study , 2017 .

[11]  Mahdi Hasanipanah,et al.  Airblast prediction through a hybrid genetic algorithm-ANN model , 2018, Neural Computing and Applications.

[12]  David M. Johnson,et al.  A comprehensive assessment of the correlations between field crop yields and commonly used MODIS products , 2016, Int. J. Appl. Earth Obs. Geoinformation.

[13]  Miao Tian,et al.  Drought Forecasting with Vegetation Temperature Condition Index Using ARIMA Models in the Guanzhong Plain , 2016, Remote. Sens..

[14]  Frédéric Baret,et al.  Vegetation baseline phenology from kilometric global LAI satellite products , 2016 .

[15]  J. Canadell,et al.  Greening of the Earth and its drivers , 2016 .

[16]  Jie Wang,et al.  Spatial Downscaling of Satellite Soil Moisture Data Using a Vegetation Temperature Condition Index , 2016, IEEE Transactions on Geoscience and Remote Sensing.

[17]  Vijay P. Singh,et al.  Retrieving vegetation growth patterns from soil moisture, precipitation and temperature using maximum entropy , 2015 .

[18]  Tae-Woong Kim,et al.  Development of a new composite drought index for multivariate drought assessment , 2015 .

[19]  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 .

[20]  B. Poulter,et al.  Detection and attribution of vegetation greening trend in China over the last 30 years , 2015, Global change biology.

[21]  Jean-Claude Léon,et al.  Functional restructuring of CAD models for FEA purposes , 2015 .

[22]  Mahdi Hasanipanah,et al.  A combination of the ICA-ANN model to predict air-overpressure resulting from blasting , 2015, Engineering with Computers.

[23]  F. Tao,et al.  Spatial and temporal changes of agro-meteorological disasters affecting maize production in China since 1990 , 2014, Natural Hazards.

[24]  Xin Du,et al.  Remote sensing-based global crop monitoring: experiences with China's CropWatch system , 2014, Int. J. Digit. Earth.

[25]  Liao Ya Evaluation of drought monitoring effects in the main growth and development stages of winter wheat using vegetation temperature condition index III.——Impact evaluation of drought on wheat yield , 2014 .

[26]  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.

[27]  J. Townshend,et al.  A long-term Global LAnd Surface Satellite (GLASS) data-set for environmental studies , 2013 .

[28]  Mahesh Panchal,et al.  Optimizing Weights of Artificial Neural Networks using Genetic Algorithms , 2012 .

[29]  Damien Sulla-Menashe,et al.  Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index , 2012 .

[30]  Sébastien Saint-Jean,et al.  Biophysical characteristics of maize pollen: Variability during emission and consequences on cross-pollination risks , 2012 .

[31]  Hui Li,et al.  Evolutionary artificial neural networks: a review , 2011, Artificial Intelligence Review.

[32]  F. Anctil,et al.  Site-specific early season potato yield forecast by neural network in Eastern Canada , 2011, Precision Agriculture.

[33]  M. Frei,et al.  Stressed food – The impact of abiotic environmental stresses on crop quality , 2011 .

[34]  Reza Akbari,et al.  A multilevel evolutionary algorithm for optimizing numerical functions , 2011 .

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

[36]  Yuan Zeng,et al.  Estimation and Validation of Land Surface Evaporation Using Remote Sensing and Meteorological Data in North China , 2010, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[37]  Yudong Zhang,et al.  Stock market prediction of S&P 500 via combination of improved BCO approach and BP neural network , 2009, Expert Syst. Appl..

[38]  Ersel Yilmaz,et al.  Effect of different water stress on the yield and yield components of second crop corn in semiarid climate , 2009 .

[39]  W. Sun,et al.  Using the vegetation temperature condition index for time series drought occurrence monitoring in the Guanzhong Plain, PR China , 2008 .

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

[41]  A. Fehér,et al.  The effect of drought and heat stress on reproductive processes in cereals. , 2007, Plant, cell & environment.

[42]  Yun Shi,et al.  Evaluation of MODIS Land Cover and LAI Products in Cropland of North China Plain Using In Situ Measurements and Landsat TM Images , 2007, IEEE Transactions on Geoscience and Remote Sensing.

[43]  Thomas J. Jackson,et al.  Crop condition and yield simulations using Landsat and MODIS , 2004 .

[44]  Nabil Kallas,et al.  Modeling soil collapse by artificial neural networks , 2004 .

[45]  Z. Wan,et al.  Using MODIS Land Surface Temperature and Normalized Difference Vegetation Index products for monitoring drought in the southern Great Plains, USA , 2004 .

[46]  E. Shields,et al.  An aerobiological framework for assessing cross-pollination in maize , 2003 .

[47]  J. Clevers,et al.  Combined use of optical and microwave remote sensing data for crop growth monitoring , 1996 .

[48]  Geoffrey E. Hinton,et al.  Learning representations by back-propagating errors , 1986, Nature.

[49]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .