## 3 Materials and Methods  
### Airborne Remote Sensing of Study Area and Fields  
#### 3.1.1 Manned Aircraft Remote Sensing  
At Stoneville, Mississippi, USDA-ARS scientists began to remotely sense crop fields from manned aircraft in the early 2000s. A video-based system installed on an Air Tractor 402B spraying airplane (Air Tractor, Olney, TX, USA) was developed for aerial remote sensing flyovers of crop fields, especially for detection of crop stress caused by multiple factors, within the USDA research farms in this location [18; 19]. A thermal imaging system installed on the Air Tractor 402 spray plane was further developed for aerial remote sensing flyovers of crop fields, especially for determination of field surface and canopy temperature for irrigation management [20]. Since 2009, airborne remote sensing has been fully developed and applied in this area. The Air Tractor 402B airplane was retrofitted with digital, multispectral, and thermal cameras that were triggered automatedly based on GPS navigation of the flights. The applications have been accomplished in field monitoring for assessing crop damage caused by herbicides sprayed from aircraft and tractors [21] and farm mapping for field decision support based on remote sensing big data [22]. The system developed on Air Tractor 402B consists of two GoPro Hero 3+ cameras (GoPro, San Mateo, CA, USA). One is for RGB image acquisition, and the other was  
Figure 1: Google/Google Earth maps of the demonstration sites at three locations in lower northern Mississippi. modified by replacing the red lens with a 700 nm high-pass filtered lens to sense NIR signals for NIR images. The original lenses of GoPro cameras are wide-angled, however, for this study, the lenses were all replaced with 2.97 mm f/4.0 low distortion ones. Each lens has a 95\({}^{\circ}\) of field of view (FOV), which can offer 55 cm/pixel ground spatial resolution of the images at a flight altitude of 900 m with an image size of 3680 \(\times\) 2760. This resolution is superior to the imagery of high-resolution satellites in multispectral bands. Additionally, a GPS receiving unit was synchronized with the operation of the imaging system onboard the airplane to provide GPS data to geotag the images along the flying track, which is required for image mosaic. To ensure the camera was pointed nadir during flight, the camera was shimmed backward 10\({}^{\circ}\) to compensate for the in-flight difference in vertical angle. The lens was calibrated for optimal focus and exposure for aerial operation. This system was also synchronized to a FLIR Vue Pro 640 13 mm thermal camera which works at the spectral band over 7.5-3.5 \(\upmu\)m, with an image size of 640 \(\times\) 512 pixels and 45\({}^{\circ}\) (horizontal) \(\times\) 37\({}^{\circ}\) (vertical) FOV. It can offer an average of 118 cm/pixel (1.18 m/pixel) ground spatial resolution of the images at a flight altitude of 900 m.  
With thermal image data, \"hot\" spots over the crop fields can be identified to show the distribution of crop stresses, such as water deficiency. With RGB and multispectral image data, the crop growth vigor over the field can be visualized, which can be expressed as a vegetation index. A vegetation index is a combination of remotely sensed earth surface radiance or reflectance at two or more wavelengths to highlight a particular property of vegetation. Various vegetation indices have been published for crop studies in fields [23; 24]. Among them, the normalized difference vegetation index (NDVI) has been most widely used in data processing of various remote sensing platforms and sensors to characterize land surface vegetation, and was used for this study as well. NDVI originated for satellite remote sensing. It has been expanded to be used for airborne and other remote sensors with red and NIR band data.