Machine Learning Model to Predict Manganese Micronutrient Content in Oil Palm Plantation Soil Using Sentinel 1A and Sentinel 2A Image Integration

Aug 5, 2026

Predict manganese micronutrient levels in oil palm plantation soil by combining remote sensing and machine learning. The approach integrates Sentinel-1A and Sentinel-2A imagery to overcome Sentinel-2A cloud limitations while leveraging Sentinel-2A’s high spectral resolution and Sentinel-1A’s cloud-free sensing. A random forest regression model is trained using 103 soil samples from Central Kalimantan and Riau, then evaluated with MAPE and correctness metrics. Integration yields 25% correctness and 75% accuracy (MAPE 25%), supporting nutrient distribution mapping to guide precision fertilizer planning.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages8

Reading Time20 min

AccessFree to download

Introduction

Background on oil palm plantations and nutrient management

Importance of manganese micronutrients

Remote sensing and satellite integration

Method Overview

Data collection and study sites

Remote sensing features from Sentinel 1A and Sentinel 2A

Machine learning model (random forest regression)

Results and Evaluation

Performance using integrated imagery

Performance using Sentinel 1A alone

Performance using Sentinel 2A alone

Implications

Nutrient distribution maps for precision fertilization

INTERNATIONAL JOURNAL ON INFORMATICS VISUALIZATION [journal homepage :](journal homepage : www.joiv.org/index.php/joiv)[ www.joiv.org/index.php/joiv](journal homepage : www.joiv.org/index.php/joiv) Machine Learning Model to Predict Manganese Micronutrient Content in Oil Palm Plantation Soil Using Sentinel 1A and Sentinel 2A Image Integration Suhendi a,d, Kudang Boro Seminar b,*, Sudradjatc, Liyantonob, Sirojul Munir d, Fatimah Az Zahrae aAgricultural Engineering Science Study Program, Faculty of Agricultural Engineering and Technology, IPB University, Indonesia b Department of Mechanical and Biosystem Engineering, Faculty of Agricultural Engineering and Technology, IPB University, Bogor, Indonesia c Department of Agronomy and Horticulture, Faculty of Agriculture, IPB University, Bogor, Indonesia d Department of Informatics Engineering, Nurul Fikri College of Technology, Depok, Indonesia e Department of Information Systems, Nurul Fikri College of Technology, Depok, Indonesia Corresponding author:*[kseminar@apps.ipb.ac.id](kseminar@apps.ipb.ac.id) Abstract—This study aims to predict manganese micronutrients in oil palm plantation soil using machine learning. Materials and technological tools use remote sensing with the integration of Sentinel 1A and Sentinel 2A satellites for monitoring micronutrients in peat soil in oil palm plantations. Integrating Sentinel 1A with Sentinel 2A will complement the shortcomings of Sentinel 2A, which isnot free from cloud cover. Sentinel 1A has the advantage of being free from cloud cover. Meanwhile, Sentinel 2A has a high spectral resolution with 12 to 13 bands, which Sentinel 1A does not have, and only has dual polarization (VV-VH) and local incident angle (LIA). This study uses a machine learning method to obtain a model with a random forest regression algorithm and 103 soil samples in Central Kalimantan and Riau locations. The results of the model performance evaluation using integration showed MAPE and correctness of 25% and 75%, respectively. Suppose using Sentinel 1A, MAPE, and accuracy are 59.63% and 40.23%. Using Sentinel 2A, the MAPE and accuracy obtained are 48.40% and 51.59%. These results suggest that the integration of Sentinel 1A and Sentinel 2A plays a significant role, given their good predictive power. The implications of this study are the status of nutrient distribution maps, which can help determine the status of manganese micronutrients in soil in oil palm plantations for fertilizer application plans according to the needs of each oil palm plant. Keywords—Palm oil; integration; Sentinel 1A and Sentinel 2A; manganese; random forest regression; soil. Manuscript received 29 Oct. 2024; revised 3 Dec. 2024; accepted 29 Jan. 2025. Date of publication 30 Sep. 2025. International Journal on Informatics Visualization is licensed under a Creative Commons Attribution-Share Alike 4.0 International License. I. INTRODUCTION Oil palm plantations have a huge role in increasing regional development income and farmers' income [1] . Oil palm plantations in Indonesia are spread across several provinces: 54% on the island of Sumatra, 41% on the island of Kalimantan, 3% on the island of Sulawesi, and 2% on other islands such as Java, Nusa Tenggara, Maluku, and Papua [2] . Data from the Central Statistics Agency indicate that oil palm plantations must be managed well to obtain good harvests, especially regarding adequate macronutrient and micronutrient content. This research aims to estimate or predict the manganese micronutrients of palm oil in soil using the integration of Sentinel 1A and Sentinel 2A. Even though oil palm plants require small amounts of micronutrients, they play an essential role in the plant, so adequate nutrition and precise fertilization for each plant play an essential role [3]. The micronutrient content needed by palm oil includes iron (Fe), manganese (Mn), copper (Cu), zinc (Zn), and boron (B) [4] . Micronutrients such as manganese (Mn) control

FAQs

Why integrate Sentinel 1A and Sentinel 2A for predicting manganese micronutrients?

Sentinel 2A is limited by cloud cover, while Sentinel 1A is cloud-free. Their strengths complement each other to improve prediction reliability for peat-soil conditions in oil palm plantations.

What machine learning method is used in the model?

A random forest regression algorithm is used to build the predictive model from soil samples and integrated satellite data.

How does model performance change when using integration versus single-satellite data?

Integrated imagery achieves stronger evaluation results (reported MAPE and correctness of 25% and 75%), while using Sentinel 1A alone yields MAPE 59.63% and accuracy 40.23%, and Sentinel 2A alone yields MAPE 48.40% and accuracy 51.59%.

Aug 5, 2026

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