Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh-Reference-Cited by-同舟云学术

Automated in-season rice crop mapping using Sentinel time-series data and Google Earth Engine: A case study in climate-risk prone Bangladesh

Published:2024-02 Issue: Volume:351 Page:119615
ISSN:0301-4797
Container-title:Journal of Environmental Management
language:en
Short-container-title:Journal of Environmental Management

Author:

Tiwari Varun,Tulbure Mirela G.,Caineta Júlio,Gaines Mollie D.^ORCID,Perin Vinicius,Kamal Mustafa,Krupnik Timothy J.,Aziz Md Abdullah,Islam AFM Tariqul

Publisher

Elsevier BV

Subject

Management, Monitoring, Policy and Law,Waste Management and Disposal,General Medicine,Environmental Engineering

Reference119 articles.

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2. Remote sensing-based quantification of the impact of flash flooding on the rice production: a case study over northeastern Bangladesh;Ahmed;Sensors,2017

3. Boro rice yield estimation model using modis ndvi data for Bangladesh;Alam,2019

4. A comparative study of ALOS-2 PALSAR and landsat-8 imagery for land cover classification using maximum likelihood classifier;Ali;The Egypt. J. Rem. Sens. Space Sci.,2018

5. Application of Google Earth engine cloud computing platform, Sentinel imagery, and neural networks for crop mapping in Canada;Amani;Rem. Sens.,2020

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