Rice crop growth monitoring with sentinel 1 SAR data using machine learning models in google earth engine cloud

Singha, Chiranjit; Swain, Kishore Chandra

doi:10.1016/j.rsase.2023.101029

Cited by 2 publications

(2 citation statements)

References 34 publications

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“…However, previous studies [28,43] developed vegetation and farmland monitoring methods using remote sensing, relying solely on Optical images such as Landsat and Sentinel-2 databases. Similarly, other research [4,7,13,44,45] has employed Sentinel-1 time series (SAR data), primarily for mapping and monitoring rice crop stands, identifying land use and cover, and identifying water coverage. This improved the validation and suitability of the results, demonstrating a positive correlation in crop yield prediction.…”

Section: Discussionmentioning

confidence: 99%

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

Medina Medina,

Salas López,

Zabaleta Santisteban

et al. 2024

Agronomy

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One of the world’s major agricultural crops is rice (Oryza sativa), a staple food for more than half of the global population. In this research, synthetic aperture radar (SAR) and optical images are used to analyze the monthly dynamics of this crop in the lower Utcubamba river basin, Peru. In addition, this study addresses the need to obtain accurate and timely information on the areas under cultivation in order to calculate their agricultural production. To achieve this, SAR sensor and Sentinel-2 optical remote sensing images were integrated using computer technology, and the monthly dynamics of the rice crops were analyzed through mapping and geometric calculation of the surveyed areas. An algorithm was developed on the Google Earth Engine (GEE) virtual platform for the classification of the Sentinel-1 and Sentinel-2 images and a combination of both, the result of which was improved in ArcGIS Pro software version 3.0.1 using a spatial filter to reduce the “salt and pepper” effect. A total of 168 SAR images and 96 optical images were obtained, corrected, and classified using machine learning algorithms, achieving a monthly average accuracy of 96.4% and 0.951 with respect to the overall accuracy (OA) and Kappa Index (KI), respectively, in the year 2019. For the year 2020, the monthly averages were 94.4% for the OA and 0.922 for the KI. Thus, optical and SAR data offer excellent integration to address the information gaps between them, are of great importance to obtaining more robust products, and can be applied to improving agricultural production planning and management.

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Section: Discussionmentioning

confidence: 99%

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

Medina Medina,

Salas López,

Zabaleta Santisteban

et al. 2024

Agronomy

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“…[37]. In some instances, researchers employ multimodal satellite imagery, incorporating Sentinel-2 multispectral data alongside Sentinel-1 radar data [24][30][34] [35]. Concurrently, other studies [33] make use of multispectral data from both MODIS and Sentinel-2.…”

Section: Features and Image Collectionmentioning

confidence: 99%

Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine

Wijaya,

Munir,

Utama

2023

icdsos

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In the era of modern agriculture, satellite imagery has been widely used to monitor crops, one of which is paddy. This paper tries to describe the vegetation indices, climate, and soil index features related to paddy plants and curates a collection of satellite imagery on the Google Earth Engine (GEE). This paper reveals how GEE can be used to collect and process multimodal satellite imagery to form a precision agriculture dataset. The objective of this study is to establish a comprehensive precision agriculture dataset by leveraging multimodal satellite imagery to monitor paddy crops. The data collected as a dataset originates from 306 locations in Karawang Regency, Indonesia, during the 2019-2020 period. In the first step, we identify the relevant features essential for paddy crop analysis. Subsequently, we carefully select image collections within GEE based on these features. Afterward, we perform data acquisition and necessary preprocessing through the Google Colab environment. The results showed that satellite imagery from Sentinel-2 outperforms Landsat 8 in terms of spatial and temporal resolution. Apart from that, the generated dataset successfully captures the growth patterns of paddy plants.

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Rice crop growth monitoring with sentinel 1 SAR data using machine learning models in google earth engine cloud

Cited by 2 publications

References 34 publications

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

An Analysis of the Rice-Cultivation Dynamics in the Lower Utcubamba River Basin Using SAR and Optical Imagery in Google Earth Engine (GEE)

Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine

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