Therefore, this study will explore the result of the combination of vegetation indices and SAR data using Sentinel satellite on the Google Earth Engine (GEE) ...

... using the combination of optical vegetation indices and synthetic aperture radar (SAR) data. This method is designed to overcome the data-missing problem ...

Accurate mapping of paddy rice cropping intensity (PRCI) affects precision agriculture, water use management, and informed decision-making.

To solve this problem, this study proposed an approach to extract paddy rice area using the threshold-based method by 6 groups of optical vegetation indices, ...

Time series vegetation indices and phenology-based algorithms have been utilized to map paddy rice fields by identifying the flooding and seedling transplanting ...

In this study, we analyzed phenology differences between rice and wetlands based on the Sentinel-1/2 data and used the random forest algorithm to map vegetation ...

Feb 1, 2021 · Fields by Combining Multi-Temporal Vegetation Index and Synthetic Aperture #Radar Remote Sensing Data Using Google Earth Engine #MachineLearning ...

... rice fields by combining multi-temporal vegetation index and synthetic aperture radar remote sensing data using google earth engine machine learning platform.

The. RiceMapEngine will utilize the computing power and data catalog of GEE for fast paddy rice mapping by either supervised classification or a phenology-based ...

This study uses a combination of Sentinel-2 MSI, Sentinel-1 SAR, along with SRTM (elevation and slope data) to monitor rice fields land-conversion. NDVI, NDBI ...