CROP TYPE MAPPING USING MACHINE LEARNING-BASED APPROACH AND SENTINEL-2: STUDY IN LUMAJANG, EAST JAVA, INDONESIA-Reference-Cited by-同舟云学术

CROP TYPE MAPPING USING MACHINE LEARNING-BASED APPROACH AND SENTINEL-2: STUDY IN LUMAJANG, EAST JAVA, INDONESIA

Published:2024-03-31 Issue: Volume: Page:129-137
ISSN:2068-2239
Container-title:INMATEH Agricultural Engineering
language:en
Short-container-title:INMATEH

Author:

MAHRUS Irsyam¹,INDARTO Indarto¹,WHENY Khristianto¹,FAHMI Kurnianto¹

Affiliation:

1. Research Group of Agroindustrial and Food Safety (Keris Dimas KPPI), Research Centre (LP2M), University of Jember / Indonesia

Abstract

In general, sentinel-2 imagery can be used for crop mapping. Crop types mapping aims to develop future strategies for sustainable agricultural systems. This study used Sentinel-2 from June 25 to July 6, 2023, with 10% cloud cover. The research was conducted in Pasrujambe and Candipuro sub-districts (± 242.23 km2). The image is processed using a random forest on the GEE platform. Accuracy was generated using a confusion matrix with an overall accuracy of 85.82% and a kappa of 71.19%. Five main types of land use/cover were produced, namely: paddy (17.31%), sugarcane (0.93%), vegetation (69.74%), sand (7.4%) and built-up land (4.59%).

Publisher

INMA Bucharest-Romania

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