A carregar...
Publicação
Rice crop classification and yield estimation using multi-temporal sentinel-2 data: a case study of Terai districts of Nepal
| Nome: | Descrição: | Tamanho: | Formato: | |
|---|---|---|---|---|
| 5.01 MB | Adobe PDF |
Autores
Resumo(s)
Crop 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).
Descrição
Dissertation submitted in partial fulfilment of the requirements for the degree of Master of Science in Geospatial Technologies
Palavras-chave
Sentinel-2 (S2) data Rice Crop Classification Yield Estimation Deep Learning Convolutional Neural Network