A new four-stage approach based on normalized vegetation indices for detecting and mapping sugarcane hail damage using multispectral remotely sensed data

Mafuratidze, Pride; Chibarabada, Tendai Polite; Shekede, Munyaradzi Davis; Masocha, Mhosisi

doi:10.1080/10106049.2023.2245788

Geocarto International

2023

DOI: 10.1080/10106049.2023.2245788

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A new four-stage approach based on normalized vegetation indices for detecting and mapping sugarcane hail damage using multispectral remotely sensed data

Pride Mafuratidze,

Tendai Polite Chibarabada,

Munyaradzi Davis Shekede

et al.

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Cited by 3 publications

References 35 publications

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Sugarcane Mapping and Harvest Monitoring using ML and Google Earth Engine

Awan,

Mustafa,

Tahir

et al. 2024

E3S Web of Conf.

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The increasing global population intensifies the pressure on food production, necessitating optimized agricultural practices. This study explores the potential of remote sensing and machine learning to enhance sugarcane identification, mapping, and harvest monitoring in Dera Ismail Khan, Pakistan. Employing a two-step approach, we leverage Sentinel-2 satellite imagery and NDVI analysis for sample selection followed by a comprehensive field survey using Kobo Toolbox. Of 467 collected samples, 80% served for model training, identifying a total sugarcane area of ~21,875 hectare. We implemented RF, SVM, CART, KNN, and K- Means classification models, with SVM achieving the highest overall accuracy of 90%. Furthermore, we propose a novel sugarcane harvest monitoring approach using an NDVI threshold of 0.35 and analyzing the decrease in NDVI value of sugarcane areas to delineate harvested areas, offering real-time insights into cropping patterns. This research aligns with two key objectives: (1) Enhancing precision in sugarcane identification and mapping, and (2) Automating and improving growth and harvest tracking. The findings demonstrate the potential of this methodology for sustainable crop monitoring, contributing to informed decision-making in agriculture and potentially mitigating the global food demand-production gap.

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Sugarcane Mapping and Harvest Monitoring using ML and Google Earth Engine

Awan,

Mustafa,

Tahir

et al. 2024

E3S Web of Conf.

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Hyperspectral microscopy- applications of hyperspectral imaging techniques in different fields of science: a review of recent advances

Banu,

Lerma,

Ahmed

et al. 2023

Applied Spectroscopy Reviews

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Mapping cropland in Yunnan Province during 1990–2020 using multi-source remote sensing data with the Google Earth Engine Platform

Wang,

Huang,

Tang

et al. 2024

Geocarto International

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A new four-stage approach based on normalized vegetation indices for detecting and mapping sugarcane hail damage using multispectral remotely sensed data

Cited by 3 publications

References 35 publications

Sugarcane Mapping and Harvest Monitoring using ML and Google Earth Engine

Sugarcane Mapping and Harvest Monitoring using ML and Google Earth Engine

Hyperspectral microscopy- applications of hyperspectral imaging techniques in different fields of science: a review of recent advances

Mapping cropland in Yunnan Province during 1990–2020 using multi-source remote sensing data with the Google Earth Engine Platform

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