Vol. 22 No. 3 (2025): Edisi Oktober 2025
General

Pemodelan Spasial Kelembaban Tanah Berbasis Indeks Spektral dengan Integrasi Citra Satelit Multi Sensor

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  • Al Fath Riza Kholdani,
  • Adani - Dharmawati,
  • Desy Ika Puspitasari,
  • Tri Wahyu Qur'ana,
  • Rezky Izzatul Y. Anwar
Al Fath Riza Kholdani
UNISKA MAB Banjarmasin
Adani - Dharmawati
UNISKA MAB Banjarmasin
Desy Ika Puspitasari
UNISKA MAB Banjarmasin
Tri Wahyu Qur'ana
UNISKA MAB Banjarmasin
Rezky Izzatul Y. Anwar
UNISKA MAB Banjarmasin
DOI: https://doi.org/10.31315/telematika.v22i3.13866

Published 2025-11-24

Keywords

  • Estimasi,
  • Kelembaban Tanah,
  • Citra Satelit,
  • Multi Sensor,
  • Random Forest.

How to Cite

Kholdani, A. F. R., Dharmawati, A. .-., Puspitasari, D. I., Qur'ana, T. W., & Y. Anwar, R. I. (2025). Pemodelan Spasial Kelembaban Tanah Berbasis Indeks Spektral dengan Integrasi Citra Satelit Multi Sensor. Telematika: Jurnal Informatika Dan Teknologi Informasi, 22(3), 107–116. https://doi.org/10.31315/telematika.v22i3.13866

Copyright (c) 2025 Telematika : Jurnal Informatika dan Teknologi Informasi

Creative Commons License

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Abstract

Tujuan: Keakuratan estimasi kelembaban tanah merupakan faktor penting dalam memonitor dan manajemen sumber daya air serta mitigasi dampak lingkungan. Pengukuran kelembaban tanah secara in-situ terbatas oleh biaya dan cakupan spasial yang rendah. Karenanya, integrasi data iklim dan citra satelit menjadi alternatif yang menarik untuk meningkatkan keakuratan estimasi kelembaban tanah. Penelitian ini mengembangkan model spasial kelembaban tanah dengan menggabungkan data iklim (suhu permukaan tanah dan curah hujan) dan indeks spektral dari citra satelit multi-sensor, termasuk Landsat 8 dan Sentinel-2, serta menggunakan algoritma Random Forest untuk klasifikasi kelembaban tanah. Hasil penelitian menunjukkan bahwa pendekatan ini menghasilkan peta kelembaban tanah dengan akurasi Overall Accuracy (OA) sebesar 0.8 dan kappa 0.75 untuk Random Forest, dan akurasi OA sebesar 0.93 dan kappa 0.92 untuk Gradient Boosting. Penelitian ini menyimpulkan bahwa integrasi data iklim dan citra satelit multi-sensor secara signifikan meningkatkan akurasi prediksi kelembaban tanah, memberikan manfaat signifikan bagi perencanaan dan pengelolaan lahan.

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