Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal

Detalhes bibliográficos
Autor(a) principal: Baidar, Tina
Data de Publicação: 2020
Tipo de documento: Dissertação
Idioma: eng
Título da fonte: Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
Texto Completo: http://hdl.handle.net/10362/95146
Resumo: Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
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spelling Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of NepalSentinel-2 (S2) dataRice Crop ClassificationYield EstimationDeep LearningConvolutional Neural NetworkDissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial TechnologiesCrop monitoring, especially in developing countries, can improve food production, address food security issues, and support sustainable development goals. Crop type mapping and yield estimation are the two major aspects of crop monitoring that remain challenging due to the problem of timely and adequate data availability. Existing approaches rely on ground-surveys and traditional means which are time-consuming and costly. In this context, we introduce the use of freely available Sentinel-2 (S2) imagery with high spatial, spectral and temporal resolution to classify crop and estimate its yield through a deep learning approach. In particular, this study uses patch-based 2D and 3D Convolutional Neural Network (CNN) algorithms to map rice crop and predict its yield in the Terai districts of Nepal. Firstly, the study reviews the existing state-of-art technologies in this field and selects suitable CNN architectures. Secondly, the selected architectures are implemented and trained using S2 imagery, groundtruth and auxiliary data in addition for yield estimation.We also introduce a variation in the chosen 3D CNN architecture to enhance its performance in estimating rice yield. The performance of the models is validated and then evaluated using performance metrics namely overall accuracy and F1-score for classification and Root Mean Squared Error (RMSE) for yield estimation. In consistency with the existing works, the results demonstrate recommendable performance of the models with remarkable accuracy, indicating the suitability of S2 data for crop mapping and yield estimation in developing countries. Reproducibility self-assessment (https://osf.io/j97zp/): 2, 2, 2, 1, 2 (input data, preprocessing, methods, computational environment, results).Pla Bañón, FilibertoFernández-Beltrán, RubénCaetano, Mário Sílvio Rochinha de AndradeRUNBaidar, Tina2020-03-27T14:04:41Z2020-03-052020-03-05T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/masterThesisapplication/pdfhttp://hdl.handle.net/10362/95146TID:202465187enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2024-03-11T04:43:10Zoai:run.unl.pt:10362/95146Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-20T03:38:14.651829Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse
dc.title.none.fl_str_mv Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal
title Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal
spellingShingle Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal
Baidar, Tina
Sentinel-2 (S2) data
Rice Crop Classification
Yield Estimation
Deep Learning
Convolutional Neural Network
title_short Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal
title_full Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal
title_fullStr Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal
title_full_unstemmed Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal
title_sort Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal
author Baidar, Tina
author_facet Baidar, Tina
author_role author
dc.contributor.none.fl_str_mv Pla Bañón, Filiberto
Fernández-Beltrán, Rubén
Caetano, Mário Sílvio Rochinha de Andrade
RUN
dc.contributor.author.fl_str_mv Baidar, Tina
dc.subject.por.fl_str_mv Sentinel-2 (S2) data
Rice Crop Classification
Yield Estimation
Deep Learning
Convolutional Neural Network
topic Sentinel-2 (S2) data
Rice Crop Classification
Yield Estimation
Deep Learning
Convolutional Neural Network
description Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
publishDate 2020
dc.date.none.fl_str_mv 2020-03-27T14:04:41Z
2020-03-05
2020-03-05T00:00:00Z
dc.type.status.fl_str_mv info:eu-repo/semantics/publishedVersion
dc.type.driver.fl_str_mv info:eu-repo/semantics/masterThesis
format masterThesis
status_str publishedVersion
dc.identifier.uri.fl_str_mv http://hdl.handle.net/10362/95146
TID:202465187
url http://hdl.handle.net/10362/95146
identifier_str_mv TID:202465187
dc.language.iso.fl_str_mv eng
language eng
dc.rights.driver.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.source.none.fl_str_mv reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)
instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação
instacron:RCAAP
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