Comparing vegetation index data collected by UAV

Multispectral monitoring involves comparing vegetation indices. To make meaningful comparisons (at one point in time between different plots, or at two or more points in time at one place) your data must be calibrated, and the sensor used must compensate for differences in light during flight. Otherwise, you can misinterpret your vegetation stress maps. The sensor must record the downwelling light from the sun and light reflected from the ground. The sky-facing sensor compensates for any changes in ambient light (clouds) during flight, and those of your crop.  Also, the UAV pilot must take a bit of time to image a special target of known radiometric reflectance to calibrate the light available at the time of survey. If this is done, then you can compare your vegetation index data (over space or time) directly because your data has an absolute calibration, and the sensor compensated for changes in ambient light during flight. If not, your data values are relative. Relative to what you ask? Relative (i.e. meaningful) only to the conditions in which you collected the data. Changes in cloud cover, sun angle (time of day and year), all change the amount and spectral composition of reflected light. 

If your data are not calibrated, a cloud shadow over part of your field could make the NDVI data there appear less healthy, or for all your field if its overcast. If your data is calibrated (and compensated for ambient changes in light during flight) then the increase or decrease in NDVI observed in space and/or time is attributed to the spectral properties of your crop and soil. When done properly, studies have shown multispectral data can be collected effectively in a wide range of ambient conditions. This is important because NextGen does not have to wait for a perfectly clear day – we can image your field at the right time. At NextGen, our vegetation index maps are consistent regardless of the weather conditions, or time of year. Your crop health maps will be directly comparable.