Mapping Cropland Extent by Asynchronous Fusion of Optical and Active Microwave Imagery

Chakrabarti, Subit; Cormier, Tina; Malizia, Nick; Potere, David; Sulla‐Menashe, Damien; Zmijewski, Kirk A.; Friedl, M. A.

doi:10.1109/igarss.2018.8518892

Cited by 2 publications

(1 citation statement)

References 8 publications

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“…Cropland mapping and global agriculture monitoring are therefore very essential for food security, agricultural resources management, agricultural economics, vegetation studies, and land use/land cover mapping purposes [ 4 , 5 , 6 ]. Croplands mapping and monitoring is a widely used application of remote sensing given its evident advantages of spatial coverage, cost effectiveness, monitoring ability for its revisiting frequency, and its ability to detect crops stress [ 7 , 8 , 9 , 10 , 11 , 12 , 13 ]. Crop intensity mapping, on the other hand, provides important information on the changes in and productivity of agricultural land [ 14 ].…”

Section: Introductionmentioning

confidence: 99%

A Light-Weight Cropland Mapping Model Using Satellite Imagery

Hussain

Abuhani

Khan

et al. 2023

Sensors

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Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine-learning-based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions, namely Iran, Mozambique, Sri-Lanka, and Sudan, where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction, and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 and 0.98 across the four test regions at hand.

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Section: Introductionmentioning

confidence: 99%

A Light-Weight Cropland Mapping Model Using Satellite Imagery

Hussain

Abuhani

Khan

et al. 2023

Sensors

View full text Add to dashboard Cite

show abstract

A Light-weight Cropland Mapping Model Using Satellite Imagery

Hussain,

Abuhani,

ElMohandes

et al. 2023

Preprint

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Many applications in agriculture as well as other related fields including natural resources, environment, health, and sustainability, depend on recent and reliable cropland maps. Cropland extent and intensity plays a critical input variable for the study of crop production and food security around the world. However, generating such variables manually is difficult, expensive, and time consuming. In this work, we discuss a cost effective, fast, and simple machine learning based approach to provide reliable cropland mapping model using satellite imagery. The study includes four test regions namely; Iran, Mozambique, Sri-Lanka, and Sudan where Sentinel-2 satellite imagery were obtained with assigned NDVI scores. The solution presented in this paper discusses a complete pipeline including data collection, time series reconstruction , and cropland extent and crop intensity mapping using machine learning models. The approach proposed managed to achieve high accuracy results ranging between 0.92 to 0.98 across the four test regions at hand.

show abstract

Mapping Cropland Extent by Asynchronous Fusion of Optical and Active Microwave Imagery

Cited by 2 publications

References 8 publications

A Light-Weight Cropland Mapping Model Using Satellite Imagery

A Light-Weight Cropland Mapping Model Using Satellite Imagery

A Light-weight Cropland Mapping Model Using Satellite Imagery

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