Estimation of the Relationship Between Urban Landscape Pattern and Crop Yield by Remote Sensing Data and Field Measurement

Exploring how urban landscape patterns and diversity affect crop yields is critical for landscape optimization to increase food production under rapid urbanization. In this study, we used Landsat remote sensing data combined with field-measured crop yields to map the spatial distribution of yields in suburban Changchun, Northeast China, and to examine their relationships with urban landscape patterns and diversity indices. Our results showed that the urban landscape composition, such as impervious surface areas (ISA) or forest coverage, significantly affected crop yield, and the suburban crop yield decreased consistently with increasing impervious surface and decreasing forest coverage (p < 0.001). Additionally, crop yield exhibited a nonlinear increase as impervious surface edge density (ED_ISA) decreased, with a threshold identified at 200 m/ha. We also identified that the driving mechanisms of landscape patterns and diversity on crop yield varied across different levels of urbanization intensities. In the low-urbanization area (ISA coverage < 50%), the crop yield was mainly affected by the composition and pattern of the surrounding landscape, such as ISA or forest coverage, patch and edge density, and the largest patch index; In the medium-urbanization area (50% ≤ ISA coverage ≤ 80%), landscape diversity played a dominant role and had a strong positive effect on crop yield. In the heavy-urbanization area (ISA coverage > 80%), crop yield was mainly affected by indicators of the farmland itself, such as coverage, edge density, and the largest cropland patch index. These findings clarify the relationship between urban landscapes and crop yields, offering new insights into reconciling urban development with food security.