Document Type
Conference Proceeding
Publication Date
7-2023
Abstract
California, known for its diverse agriculture, is also a major producer of rice, especially in its northern regions in Sacramento River Valley. Traditional methods, predominantly reliant on optical-based satellite imagery, encounter limitations due to atmospheric interference and sensor resolution. The ability of Synthetic Aperture Radar (SAR) to penetrate atmospheric distortions and exhibit high sensitivity to vegetation structure presents a distinct advantage over optical-based methods. Utilizing Optical and SAR data fusion, this study advances the enhanced pixel-based phenological feature composite (Eppf) method using SVM classification algorithm, which can track phenological changes and patterns, providing valuable insights for agricultural planning and management. We demonstrate that Radar Vegetation Index (RVI) derived from SAR data, offers an improved alternative for identifying and mapping rice fields with enhanced accuracy. Subsequent research will focus on enhancing the suggested approach and investigating its relevance and adaptability to different types of crops.
Recommended Citation
W. Li, H. El-Askary and D. C. Struppa, "Mapping California Rice Using Optical and SAR Data Fusion with Phenological Features in Google Earth Engine," IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium, Pasadena, CA, USA, 2023, pp. 5619-5622, https://doi.org/10.1109/IGARSS52108.2023.10281418.
Copyright
© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Comments
This is a pre-copy-editing, author-produced PDF of an article accepted for publication in IGARSS 2023 - 2023 IEEE International Geoscience and Remote Sensing Symposium. This article may not exactly replicate the final published version. The definitive publisher-authenticated version is available online at https://doi.org/10.1109/IGARSS52108.2023.10281418.