Empirical model for forecasting sugarcane yield on a local scale in Brazil using Landsat imagery and random forest algorithm
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Guerric le Maire | Manoel Regis Lima Verde Leal | Daniel Garbellini Duft | Ana Cláudia dos Santos Luciano | Michelle Cristina Araújo Picoli | Jansle Vieira Rocha | G. Maire | M. Leal | J. Rocha | M. Picoli | D. G. Duft | A. Luciano
[1] A. Viña,et al. Comparison of different vegetation indices for the remote assessment of green leaf area index of crops , 2011 .
[2] S. K. McFeeters. The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features , 1996 .
[3] Agnès Bégué,et al. Forecasting Regional Sugarcane Yield Based on Time Integral and Spatial Aggregation of MODIS NDVI , 2013, Remote. Sens..
[4] Henrique Boriolo Dias,et al. Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields , 2017 .
[5] J. Thepaut,et al. The ERA‐Interim reanalysis: configuration and performance of the data assimilation system , 2011 .
[6] Felipe Ferreira Bocca,et al. The effect of tuning, feature engineering, and feature selection in data mining applied to rainfed sugarcane yield modelling , 2016, Comput. Electron. Agric..
[7] A. Rogers,et al. Reducing signature variability in unmixing coastal marsh Thematic Mapper scenes using spectral indices , 2004 .
[8] 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.
[9] S. Sader,et al. Detection of forest harvest type using multiple dates of Landsat TM imagery , 2002 .
[10] Johannes R. Sveinsson,et al. Random Forests for land cover classification , 2006, Pattern Recognit. Lett..
[11] Y. Everingham,et al. Accurate prediction of sugarcane yield using a random forest algorithm , 2016, Agronomy for Sustainable Development.
[12] Roger Stone,et al. Enhanced risk management and decision-making capability across the sugarcane industry value chain based on seasonal climate forecasts , 2002 .
[13] Sebastian Varela,et al. Forecasting maize yield at field scale based on high-resolution satellite imagery , 2018, Biosystems Engineering.
[14] Clement Atzberger,et al. Advances in Remote Sensing of Agriculture: Context Description, Existing Operational Monitoring Systems and Major Information Needs , 2013, Remote. Sens..
[15] S. Priya,et al. A study on pre-harvest forecast of sugarcane yield using climatic variables , 2009 .
[16] K. Stahl,et al. Response to comment on ‘Candidate Distributions for Climatological Drought Indices (SPI and SPEI)’ , 2016 .
[17] John R. Miller,et al. Estimating crop stresses, aboveground dry biomass and yield of corn using multi-temporal optical data combined with a radiation use efficiency model , 2010 .
[18] Y. L. Everingham,et al. Advanced satellite imagery to classify sugarcane crop characteristics , 2007, Agronomy for Sustainable Development.
[19] N. Silleos,et al. Vegetation Indices: Advances Made in Biomass Estimation and Vegetation Monitoring in the Last 30 Years , 2006 .
[20] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[21] Jansle Vieira Rocha,et al. Sugarcane yield estimates using time series analysis of spot vegetation images , 2011 .
[22] A. Huete. A soil-adjusted vegetation index (SAVI) , 1988 .
[23] Elfatih M. Abdel-Rahman,et al. The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature , 2008 .
[24] Zoltán Barcza,et al. Statistical modelling of crop yield in Central Europe using climate data and remote sensing vegetation indices , 2018, Agricultural and Forest Meteorology.
[25] Julien Morel,et al. Coupling a sugarcane crop model with the remotely sensed time series of fIPAR to optimise the yield estimation , 2014 .
[26] Bernardo Friedrich Theodor Rudorff,et al. Yield estimation of sugarcane based on agrometeorological-spectral models , 1990 .
[27] Naira Hovakimyan,et al. Modeling yield response to crop management using convolutional neural networks , 2020, Comput. Electron. Agric..
[28] R. Confalonieri,et al. Forecasting sugarcane yields using agro-climatic indicators and Canegro model: A case study in the main production region in Brazil , 2017 .
[29] D. Lobell,et al. A scalable satellite-based crop yield mapper , 2015 .
[30] P. Sentelhas,et al. Sugarcane Yield Prediction Through Data Mining and Crop Simulation Models , 2019, Sugar Tech.