Rice Phenology Classification Model Based on Sentinel-1 Using Machine Learning Method on Google Earth Engine

Author:

Muradi Hengki1ORCID,Domiri Dede Dirgahayu2ORCID,Parsa I Made2,Yoga I Kadek3ORCID,Bustamam Alhadi45ORCID,Rarasati Anisa2ORCID,Harini Sri2,Manalu R. Johannes2ORCID,Subehi Mokhamad6

Affiliation:

1. Data and Information Science research center, The National Research and Innovation Agency of the Republic of Indonesia, Jakarta, Indonesia

2. Geoinformatics Research Center, The National Research and Innovation Agency of the Republic of Indonesia, Jakarta, Indonesia

3. Data and Information Center, The National Research and Innovation Agency of the Republic of Indonesia, Jakarta, Indonesia

4. Department of Mathematic, Faculty of Mathematics and Natural Sciences, Universitas Indonesia, Depok, Indonesia

5. Data Science Center, Universitas Indonesia, Depok, Indonesia

6. Data and Information Center, Ministry of Agriculture of the Republic of Indonesia, Jakarta, Indonesia

Publisher

Informa UK Limited

Reference33 articles.

1. Comparing Sampling Strategies for Tackling Imbalanced Data in Human Activity Recognition

2. Badan Pusat Statistik RI 2018. “Upaya Perbaikan Data Padi Dengan Metode Kerangka Sampel Area (KSA) 2018 ” 1–16.

3. Badan Pusat Statistik RI 2023. “Luas Panen Produksi Dan Produktivitas Padi Menurut Provinsi 2020-2022.” https://www.bps.go.id/indicator/53/1498/1/luas-panen-produksi-dan-produktivitas-padi-menurut-provinsi.html.

4. Mapping Paddy Rice Using Sentinel-1 SAR Time Series in Camargue, France

5. Chang L. Chen Y-t. Wang J-h. and Chang Y-l. 2021. “Rice-Field Mapping with Sentinel-1A SAR Time-Series Data.”

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