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UAV and Satellite Observation of Crop Stresses

Field images from Texas A&M
What We Do

This project evaluates the precision and suitability of rotorcraft-type small unmanned aircraft system (UAS), fixed-wing aircraft (Cessna), and satellite (accessed through the GEOGLAM and Harvest networks) for observing crop stresses and predicting yield of rice (and corn) via integration into a crop simulation model.                            


Texas, Louisiana

How Satellites Make This Work

This project collects ground, UAS, and airborne data over cotton and sorghum fields in Texas. These ground and airborne measurements are compared with high-spatial resolution satellite data. Spatial resolution of orthomosaic images are 1cm, 10cm, and 10/30m (UAS, Aircraft, and Satellite (Sentinel 2 and Landsat 8, respectively). With the extraction of crop phenotypic data from remotely sensed imagery, there is cross-validation of crop phenotypic data with ground data. All remote sensing data is compared to the ground data. Calibrated satellite data is then used to predict yield using EPIC/APEX models. 

, Texas A&M AgriLife Research
Team Members

Juan A. Landivar, Texas A&M AgriLife Research

Jaehak Jeong, Texas A&M AgriLife Research

Jinha Jung, Texas A&M AgriLife Research