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DC Field | Value | Language |
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dc.contributor.author | Kaur, R. | - |
dc.contributor.author | Tiwari, R.K. | - |
dc.contributor.author | Maini, R. | - |
dc.contributor.author | Singh, S. | - |
dc.date.accessioned | 2024-10-13T13:56:05Z | - |
dc.date.available | 2024-10-13T13:56:05Z | - |
dc.date.issued | 2024-10-13 | - |
dc.identifier.uri | http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4731 | - |
dc.description.abstract | Crop yield prediction is one of the crucial components of agriculture that plays an important role in the decision-making process for sustainable agriculture. Remote sensing provides the most efficient and cost-effective solution for the measurement of important agricultural parameters such as soil moisture level, but retrieval of the soil moisture contents from coarse resolution datasets, especially microwave datasets, remains a challenging task. In the present work, a machine learning-based framework is proposed to generate the enhanced resolution soil moisture products, i.e., classified maps and change maps, using an optical-based moderate resolution imaging spectroradiometer (MODIS) and microwave-based scatterometer satellite (SCATSAT-1) datasets. In the proposed framework, nearest-neighbor-based image fusion (NNIF), artificial neural networks (ANN), and post-classification-based change detection (PCCD) have been integrated to generate thematic and change maps. To confirm the effectiveness of the proposed framework, random forest post-classification-based change detection (RFPCD) has also been implemented, and it is concluded that the proposed framework achieved better results (88.67–91.80%) as compared to the RFPCD (86.80–87.80%) in the computation of change maps with σ°-HH. This study is important in terms of crop yield prediction analysis via the delivery of enhanced-resolution soil moisture products under all weather conditions. | en_US |
dc.language.iso | en_US | en_US |
dc.subject | scatterometer satellite (SCATSAT-1) | en_US |
dc.subject | moderate resolution imaging spectroradiometer (MODIS) | en_US |
dc.subject | soil moisture | en_US |
dc.subject | crop yield | en_US |
dc.subject | fusion | en_US |
dc.title | A Framework for Crop Yield Estimation and Change Detection Using Image Fusion of Microwave and Optical Satellite Dataset | en_US |
dc.type | Article | en_US |
Appears in Collections: | Year-2023 |
Files in This Item:
File | Description | Size | Format | |
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A Framework full text.pdf | 12.01 MB | Adobe PDF | View/Open Request a copy |
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