Identifikasi Genangan Banjir di Persawahan Menggunakan Citra Satelit Sentinel-1 (Studi Kasus: Kabupaten Demak, Provinsi Jawa Tengah)

Abstract

Banjir merupakan meluapnya aliran sungai akibat hujan terus-menerus di hulu yang tidak tertampung oleh sungai,
sehingga menggenangi wilayah sekitar (Ningrum & Ginting, 2020). Pada 19 Maret 2024, banjir melanda Kabupaten
Demak, Jawa Tengah, merendam pemukiman, jalan raya, dan persawahan. Penelitian ini memanfaatkan data Citra
Sentinel-1 sebelum dan saat banjir untuk memetakan sebaran banjir di area persawahan menggunakan metode
Change Index (Amitrano dkk., 2017) dan pengolahan data di ArcGIS melalui tahapan raster calculator, clip data,
raster to polygon, pemilihan atribut, dan perhitungan luas. Hasilnya, banjir teridentifikasi melanda ±14.498,091 ha
area persawahan, dengan dampak terbesar di Kecamatan Karang Anyar (4.460,99 ha atau 30,77%) dan terkecil di
Kecamatan Kebon Agung (7,83 ha atau 0,05%).

Kata kunci: Banjir, Sentinel-1, Change Index

ABSTRACT
Flooding is defined as the overflow of river discharge resulting from continuous upstream rainfall that exceeds the
river's capacity, leading to the inundation of surrounding areas (Ningrum & Ginting, 2020). On March 19, 2024,
a significant flood event occurred in Demak Regency, Central Java, inundating residential areas, major roads, and
agricultural lands, particularly rice fields. This study employs Sentinel-1 satellite imagery—both pre-flood and
during-flood datasets—to accurately delineate the extent of flooding across agricultural zones. The analysis utilizes
the Change Index method as proposed by Amitrano et al. (2017), with data processing conducted in ArcGIS through
several stages: raster calculation, data clipping, raster-to- polygon conversion, attribute indexing, and flood area
computation. The findings reveal that approximately 14,498.091 hectares of rice fields were affected, with Karang
Anyar District experiencing the most extensive flooding (4,460.99 ha or 30.77%), while Kebon Agung District
recorded the smallest affected area (7.83 ha or 0.05%). These results underscore the critical value ofremote sensing
and geospatial analysis in supporting disaster response and agricultural impact assessment.

Keywords: Flood, Sentinel-1, Change Index