Academic Level at Time of Presentation
Graduate
Major
Geoinformatics
List all Project Mentors & Advisor(s)
Dr. Robin Q. Zhang
Presentation Format
Poster Presentation
Abstract/Description
Remote sensing provides complementary information on crop development and surface conditions, but radar backscatter over agricultural areas can be influenced by vegetation and soil characteristics. This study examines seasonal relationships between Sentinel-1 radar backscatter and Landsat-derived vegetation greenness over corn-growing areas in the South Fork Iowa River Watershed, Iowa, during the 2025 growing season. Landsat 8/9 was used to derive the Normalized Difference Vegetation Index (NDVI), while OPERA RTC-S1 imagery provided VV and VH backscatter and a VH/VV polarization measure. USDA Cropland Data Layer was used to identify corn growing areas, and analysis was restricted to areas with common satellite coverage. Preliminary analysis focused on the early vegetative period using Landsat observations from June 22 and Sentinel-1 observations from June 26, 2025. Around 2,400 valid spatial samples were used to compare NDVI with VV, VH, and VH/VV. It showed high corn greenness, with a median NDVI, while median VV and VH backscatter were -7.29 and -14.16 dB, respectively. Relationships between NDVI and radar response were weak: NDVI showed a very weak positive correlation with VV (r = 0.120, R2 = 0.014), essentially no linear relationship with VH (r = -0.020, R2 < 0.001), and a very weak negative relationship with VH/VV (r= -0.106, R2 = 0.011). These results indicate that vegetation greenness alone explained little of the spatial variability in radar backscatter during the early vegetative growth period, suggesting that other factors such as exposed soil conditions, surface wetness, roughness, and developing canopy structure may have contributed to the radar response. Further analysis from peak canopy development to senescence will evaluate how the radar–vegetation relationship changes throughout the growing season.
Fall Scholars Week 2026
Earth and Environmental Sciences Poster Session
Seasonal Variations in Radar Backscatter and Corn Vegetation in the South Fork Iowa River Watershed During the Growing Season of 2025
Remote sensing provides complementary information on crop development and surface conditions, but radar backscatter over agricultural areas can be influenced by vegetation and soil characteristics. This study examines seasonal relationships between Sentinel-1 radar backscatter and Landsat-derived vegetation greenness over corn-growing areas in the South Fork Iowa River Watershed, Iowa, during the 2025 growing season. Landsat 8/9 was used to derive the Normalized Difference Vegetation Index (NDVI), while OPERA RTC-S1 imagery provided VV and VH backscatter and a VH/VV polarization measure. USDA Cropland Data Layer was used to identify corn growing areas, and analysis was restricted to areas with common satellite coverage. Preliminary analysis focused on the early vegetative period using Landsat observations from June 22 and Sentinel-1 observations from June 26, 2025. Around 2,400 valid spatial samples were used to compare NDVI with VV, VH, and VH/VV. It showed high corn greenness, with a median NDVI, while median VV and VH backscatter were -7.29 and -14.16 dB, respectively. Relationships between NDVI and radar response were weak: NDVI showed a very weak positive correlation with VV (r = 0.120, R2 = 0.014), essentially no linear relationship with VH (r = -0.020, R2 < 0.001), and a very weak negative relationship with VH/VV (r= -0.106, R2 = 0.011). These results indicate that vegetation greenness alone explained little of the spatial variability in radar backscatter during the early vegetative growth period, suggesting that other factors such as exposed soil conditions, surface wetness, roughness, and developing canopy structure may have contributed to the radar response. Further analysis from peak canopy development to senescence will evaluate how the radar–vegetation relationship changes throughout the growing season.