Sugarcane yield prediction in Brazil using NDVI time series and neural networks ensemble

Multi-temporal images are now of standard use in remote sensing of vegetation during monitoring and classification. Temporal vegetation signatures (i. e., vegetation indices as functions of time) generated, poses many challenges, primarily due to signal to noise-related issues. This study investigates which methods generate the most appropriate smoothed curves of vegetation signatures on MODIS NDVI time series. The filtering techniques compared were the HANTS algorithm which is based on Fourier analyses and Wavelet temporal algorithm which uses the wavelet analysis to generate the smoothed curves. The study was conducted in four different regions of the Minas Gerais State. The smoothed data were used as input data vectors for vegetation classification by means of artificial neural networks for comparison purpose. A comparison of the results was ultimately discussed in this work showing encouraging results and similarity between the two filtering techniques used.

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A comparison of machine learning algorithms for regional wheat yield prediction using NDVI time series of SPOT-VGT

2016 Fifth International Conference on Agro-Geoinformatics (Agro-Geoinformatics) ◽

10.1109/agro-geoinformatics.2016.7577625 ◽

2016 ◽

Cited By ~ 1

Author(s):

Michiel Stas ◽

Jos Van Orshoven ◽

Qinghan Dong ◽

Stien Heremans ◽

Beier Zhang

Keyword(s):

Machine Learning ◽

Time Series ◽

Learning Algorithms ◽

Wheat Yield ◽

Machine Learning Algorithms ◽

Yield Prediction ◽

Ndvi Time Series

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PREDICTING SUGARCANE YIELDS IN KHUZESTAN USING A LARGE TIME-SERIES OF REMOTE SENSING IMAGERY REGION

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-4-w18-645-2019 ◽

2019 ◽

Vol XLII-4/W18 ◽

pp. 645-648 ◽

Cited By ~ 1

Author(s):

M. Khosravirad ◽

M. Omid ◽

F. Sarmadian ◽

S. Hosseinpour

Keyword(s):

Time Series ◽

Vegetation Index ◽

Time Series Data ◽

Vegetation Indices ◽

Series Data ◽

Linear Regression Models ◽

Khuzestan Province ◽

Second Growth ◽

Ndvi Time Series ◽

Sugarcane Yield

Abstract. This study aimed to evaluate the power of various vegetation indices for sugarcane yield modelling in Shoeibeyeh area in Khuzestan province of Iran. Seven indices were extracted from satellite images and were then converted to seven days' time-series via interpolation. To eliminate noise from the time-series data, all of them were reconstructed using the Savitzky-Golay algorithm. Thus seven different time-series of vegetation indices were obtained. The growth profile was drawn via averaging of NDVI time-series data and was divided into three growth intervals. Then the accumulative values of vegetation indices related to first and second periods of growth (from 2004 to 2016 extracted from time-series data) were evaluated by simple linear regression models against the average observed yields efficiency. The result showed the accumulative IAVI (γ = 1.4) vegetation index relative to first period of growth with R2 = 0.66 and RMSE = 3.78 ton/ha and the accumulative NDI vegetation index relative to second period of growth with R2 = 0.66 and RMSE = 3.79 ton/ha and the accumulative NDI vegetation index relative to sum of the first and the second growth periods with R2 = 0.78 and RMSE = 3.09 ton/ha had good agreement with sugarcane stem yield efficiency at the middle of growth and before harvesting season.

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Neural networks as a tool for constructing continuous NDVI time series from AVHRR and MODIS

International Journal of Remote Sensing ◽

10.1080/01431160802238435 ◽

2008 ◽

Vol 29 (24) ◽

pp. 7141-7158 ◽

Cited By ~ 35

Author(s):

M. E. Brown ◽

D. J. Lary ◽

A. Vrieling ◽

D. Stathakis ◽

H. Mussa

Keyword(s):

Neural Networks ◽

Time Series ◽

Ndvi Time Series

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Distributed Gaussian Granular Neural Networks Ensemble for Prediction Intervals Construction

Complexity ◽

10.1155/2019/2379584 ◽

2019 ◽

Vol 2019 ◽

pp. 1-17

Author(s):

Chunyang Sheng ◽

Haixia Wang ◽

Xiao Lu ◽

Zhiguo Zhang ◽

Wei Cui ◽

...

Keyword(s):

Neural Networks ◽

Time Series ◽

Additive Noise ◽

Prediction Intervals ◽

Local Network ◽

Data Noise ◽

Modelling Uncertainty ◽

Proposed Model ◽

Neural Networks Ensemble ◽

Granular Neural Networks

To overcome the weakness of generic neural networks (NNs) ensemble for prediction intervals (PIs) construction, a novel Map-Reduce framework-based distributed NN ensemble consisting of several local Gaussian granular NN (GGNNs) is proposed in this study. Each local network is weighted according to its contribution to the ensemble model. The weighted coefficient is estimated by evaluating the performance of the constructed PIs from each local network. A new evaluation principle is reported with the consideration of the predicting indices. To estimate the modelling uncertainty and the data noise simultaneously, the Gaussian granular is introduced to the numeric NNs. The constructed PIs can then be calculated by the variance of output distribution of each local NN, i.e., the summation of the model uncertainty variance and the data noise variance. To verify the effectiveness of the proposed model, a series of prediction experiments, including two classical time series with additive noise and two industrial time series, are carried out here. The results indicate that the proposed distributed GGNNs ensemble exhibits a good performance for PIs construction.

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Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Sensors ◽

10.3390/s21041406 ◽

2021 ◽

Vol 21 (4) ◽

pp. 1406

Author(s):

Zhonglin Ji ◽

Yaozhong Pan ◽

Xiufang Zhu ◽

Jinyun Wang ◽

Qiannan Li

Keyword(s):

Remote Sensing ◽

Time Series ◽

Arid Regions ◽

Vegetation Index ◽

Corn Yield ◽

Yield Prediction ◽

Multivariate Models ◽

Ndvi Time Series ◽

Phenological Metrics ◽

Semi Arid

Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.

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