<![CDATA[Agriculture has contributed with the highest percentage of Pemalang Grodd Regional Product in 2022 and still continue to become regency’s primary source of income. However, the GRDP figures published at regency level do not really show how agricultural productivity varies from one place to another, something that matters a lot for making good policy decisions. In this paper, we combine official economic records with satellite-derived vegetation indicators to map where primary-sector GRDP is concentrated. We used a Geographically Weighted Regression (GWR) approach for this analysis. We downscaled GRDP data from BPS (Statistics Indonesia) onto a 500 × 500 m grid as the response variable. We used vegetation indices like NDVI, EVI, MSAVI, LAI, and MNDWI, which we calculated from Landsat and MODIS imagery through Google Earth Engine, as predictors that reflect crop condition, land productivity, and water availability. The GWR model showed a much better fit than ordinary global regression, with an Adjusted R² of 0.77. This indicates that spatial variation is significant here. The highest GRDP coefficients appeared in northern and northeastern coastal subdistricts such as Ulujami, Comal, and Petarukan, as well as in areas of the fertile hinterland, where irrigated farming and aquaculture thrive. Lower values emerged in central and southern areas that are more urbanized. A clear dual landscape exists in Pemalang: strong agricultural output in rural regions, alongside a gradual shift toward manufacturing and services in urban centers. Our results highlight how remote sensing, spatial modeling, and economic statistics can be brought together to produce detailed agricultural GRDP maps, and we think these maps can be genuinely useful for protecting farmland, targeting agricultural support, and planning more balanced regional development.]]>
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