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Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data

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Title Machine Learning Techniques for Phenology Assessment of Sugarcane Using Conjunctive SAR and Optical Data
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Creator Md Yeasin
Dipanwita Haldar
Suresh Kumar
Ranjit Kumar Paul
Sonaka Ghosh
 
Subject machine learning
optical data
sugarcane
SAR data
phenology
 
Description Not Available
Crop phenology monitoring is a necessary action for precision agriculture. Sentinel-1 and Sentinel-2 satellites provide us with the opportunity to monitor crop phenology at a high spatial resolution with high accuracy. The main objective of this study was to examine the potential of the Sentinel-1 and Sentinel-2 data and their combination for monitoring sugarcane phenological stages and evaluate the temporal behaviour of Sentinel-1 parameters and Sentinel-2 indices. Seven machine learning models, namely logistic regression, decision tree, random forest, artificial neural network, support vector machine, naïve Bayes, and fuzzy rule based systems, were implemented, and their predictive performance was compared. Accuracy, precision, specificity, sensitivity or recall, F score, area under curve of receiver operating characteristic and kappa value were used as performance metrics. The research was carried out in the Indo-Gangetic alluvial plains in the districts of Hisar and Jind, Haryana, India. The Sentinel-1 backscatters and parameters VV, alpha and anisotropy and, among Sentinel-2 indices, normalized difference vegetation index and weighted difference vegetation index were found to be the most important features for predicting sugarcane phenology. The accuracy of models ranged from 40 to 60%, 56 to 84% and 76 to 88% for Sentinel-1 data, Sentinel-2 data and combined data, respectively. Area under the ROC curve and kappa values also supported the supremacy of the combined use of Sentinel-1 and Sentinel-2 data. This study infers that combined Sentinel-1 and Sentinel-2 data are more efficient in predicting sugarcane phenology than Sentinel-1 and Sentinel-2 alone.
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Date 2022-09-13T06:25:52Z
2022-09-13T06:25:52Z
2022-07-06
 
Type Research Paper
 
Identifier Not Available
Not Available
http://krishi.icar.gov.in/jspui/handle/123456789/74086
 
Language English
 
Relation Not Available;
 
Publisher Not Available