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Synthetic Aperture Radar in Agricult...

Ghazi Zadeh Hashemi, Mahya Sadat,

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Synthetic Aperture Radar in Agriculture With AI-Enhanced Techniques for Crop Classification, Crop Monitoring, and Yield Prediction /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Synthetic Aperture Radar in Agriculture With AI-Enhanced Techniques for Crop Classification, Crop Monitoring, and Yield Prediction // Mahya Sadat Ghazi Zadeh Hashemi.
Author:	Ghazi Zadeh Hashemi, Mahya Sadat,
Description:	1 electronic resource (275 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
Subject:	Agriculture. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31565187
ISBN:	9798896074571

Synthetic Aperture Radar in Agriculture With AI-Enhanced Techniques for Crop Classification, Crop Monitoring, and Yield Prediction /
Ghazi Zadeh Hashemi, Mahya Sadat,

Synthetic Aperture Radar in Agriculture With AI-Enhanced Techniques for Crop Classification, Crop Monitoring, and Yield Prediction /Mahya Sadat Ghazi Zadeh Hashemi. - 1 electronic resource (275 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

This PhD research advances the application of high-resolution Synthetic Aperture Radar (SAR) and other satellite remote sensing technologies in agriculture, particularly focusing on crop classification, crop monitoring, and yield prediction. The study addresses critical challenges in effectively leveraging vast spatiotemporal data by integrating SAR data with deep learning, machine learning, and time-series analysis techniques to estimate crop attributes, crop biophysical parameters, and crop yield with improved accuracy.A novel contribution of this research is the development of self-supervised learning foundation models and the fusion of SAR and optical data to enhance predictions of crop yield, Vegetation Water Content (VWC), and crop height. The research also investigates the integration of dynamic SAR-based planting dates into crop models, improving yield estimation in rainfed paddy fields in Cambodia. The findings reveal that SAR-derived planting dates significantly enhance yield predictions by reducing uncertainty and improving accuracy compared to traditional methods.Spanning diverse climatic zones and management practices, this research demonstrates the exceptional potential of VH channel of Sentinel-1 SAR data for near-accurate yield prediction across different crops, including Michigan's non-irrigated corn, soybean, and winter wheat. The study also highlights the effectiveness of patch-based 3D Convolutional Neural Networks (3D-CNNs) and XGBoost in yield estimation, particularly in scenarios with limited reference data. In addition, this dissertation introduces a novel approach for estimating VWC and crop height using geospatial foundation models, demonstrating superior accuracy and generalizability across varied agricultural landscapes. The integration of SAR, optical indices, and climatic data significantly improved the reliability of VWC and crop height estimations, with NDVI, NDWI, VH backscatter, and precipitation emerging as key drivers. The research underscores the need for continued innovation in remote sensing technologies, offering new insights for precision agriculture and supporting sustainable farming practices.

English

ISBN: 9798896074571Subjects--Topical Terms:

518588
Agriculture.
Subjects--Index Terms:

Crop yield prediction

Synthetic Aperture Radar in Agriculture With AI-Enhanced Techniques for Crop Classification, Crop Monitoring, and Yield Prediction /
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520 $a This PhD research advances the application of high-resolution Synthetic Aperture Radar (SAR) and other satellite remote sensing technologies in agriculture, particularly focusing on crop classification, crop monitoring, and yield prediction. The study addresses critical challenges in effectively leveraging vast spatiotemporal data by integrating SAR data with deep learning, machine learning, and time-series analysis techniques to estimate crop attributes, crop biophysical parameters, and crop yield with improved accuracy.A novel contribution of this research is the development of self-supervised learning foundation models and the fusion of SAR and optical data to enhance predictions of crop yield, Vegetation Water Content (VWC), and crop height. The research also investigates the integration of dynamic SAR-based planting dates into crop models, improving yield estimation in rainfed paddy fields in Cambodia. The findings reveal that SAR-derived planting dates significantly enhance yield predictions by reducing uncertainty and improving accuracy compared to traditional methods.Spanning diverse climatic zones and management practices, this research demonstrates the exceptional potential of VH channel of Sentinel-1 SAR data for near-accurate yield prediction across different crops, including Michigan's non-irrigated corn, soybean, and winter wheat. The study also highlights the effectiveness of patch-based 3D Convolutional Neural Networks (3D-CNNs) and XGBoost in yield estimation, particularly in scenarios with limited reference data. In addition, this dissertation introduces a novel approach for estimating VWC and crop height using geospatial foundation models, demonstrating superior accuracy and generalizability across varied agricultural landscapes. The integration of SAR, optical indices, and climatic data significantly improved the reliability of VWC and crop height estimations, with NDVI, NDWI, VH backscatter, and precipitation emerging as key drivers. The research underscores the need for continued innovation in remote sensing technologies, offering new insights for precision agriculture and supporting sustainable farming practices.
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653 $a Planting dates
653 $a Synthetic Aperture Radar
653 $a Vegetation Water Content
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