Crops Classification in Fragmented Agricultural Land Using Integrated Radar and Optical Remote Sensing Satellite Data
DOI:
https://doi.org/10.19184/jid.v26i2.53691Keywords:
Crop classification; Fragmented agricultural land; Radar-optical fusion; Remote sensing; Google Earth EngineAbstract
This study aims to classify crops on fragmented agricultural land by integrating radar (Sentinel-1) and optical (Sentinel-2) satellite remote sensing data. The research responds to the pressing issue of decreasing agricultural land in Jember Regency due to land conversion, which threatens food security. Feature-level fusion is applied to combine spectral indices (NDVI, NDWI, NDBI) from Sentinel-2 and radar backscatter characteristics (VV, VH) from Sentinel-1. Classification was performed using the Random Forest algorithm in the Google Earth Engine (GEE) platform. The results showed that the combination of both datasets provided high overall accuracy (81.58%) in classifying eight land cover types including agricultural crops such as paddy, corn, sugarcane, and citrus. This integration enables better monitoring of complex agricultural landscapes, offering a practical tool for sustainable land management.
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