Please use this identifier to cite or link to this item: http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4731
Title: A Framework for Crop Yield Estimation and Change Detection Using Image Fusion of Microwave and Optical Satellite Dataset
Authors: Kaur, R.
Tiwari, R.K.
Maini, R.
Singh, S.
Keywords: scatterometer satellite (SCATSAT-1)
moderate resolution imaging spectroradiometer (MODIS)
soil moisture
crop yield
fusion
Issue Date: 13-Oct-2024
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.
URI: http://dspace.iitrpr.ac.in:8080/xmlui/handle/123456789/4731
Appears in Collections:Year-2023

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