## 3 Radiometric and Geometric Calibration and Corrections
### Geometric Corrections  
All PS products undergo some level of geometric correction by Planet, which varies by processing level (Table 2). Spatial resolutions for all products are around 3 m (Table 1), but post-correction positional accuracies are officially reported as less than 10 m root mean square error at the 90 th percentile [13], which can mean considerable image-to-image pixel shifts between images and in comparison to other spatially referenced data. The analytical orthoscene products (Table 2) undergo the most rigorous geometric corrections. However, it should be noted that post spacing for ground control points and digital elevation models is between 30 and 90 m [13; 17], or 10 to 30 times the product pixel size. Independent investigations measuring horizontal offsets noted positional errors as small as 4.8 m and as large as 19 m [7; 18; 19]. In sum, positional errors in PS imagery can be considerable and often require additional geometric corrections be performed if images are to be related to other spatial data or mosaicked with other images [19; 20]. However, the majority of studies we reviewed did not report any additional geometric correction, raising questions about the spatial integrity and uncertainty of data used in those applications. In situations where only a single scene or adjacent (along-track) scenes are being used [19], additional corrections may not be needed as long as no other spatial data are being used that would require co-registration. Other authors determined the up-to-10 m errors to be insignificant in cases where reflectance values were being summarized across large areas such as meadows [21] or crop fields [22]. Similarly, a study comparing large, homogenous snow patches through time disregarded the errors, noting that the 2-3 pixel misalignment from the error would only cause misclassifications along the edges [23]. In cases where accurate geo-registration is needed, the user community has developed several solutions to better geolocate Planet scenes and minimize the propagation of geometric errors into analyses. Image co-registration has proven viable in cases where it is not necessary for each pixel to be assigned a unique x, y coordinate in a planimetric map projection [7; 24]. [PERSON] and [PERSON] [7] developed an automated scene co-registration technique for Planet images that shifts pixels based on optimizing spatial autocorrelation between temporally adjacent scenes. The authors do not provide a quantitative measure of the success of this adjustment method but do note qualitatively that the features of interest appear \"well aligned\" across the scenes upon completion. This type of co-registration technique produces better results when applied in areas with discrete or contrasting land cover changes (e.g., agricultural field boundaries). The technique may be less useful in highly heterogeneous environments such as urban areas or where land cover changes are more gradual (e.g., grasslands or ecotones). When precise planimetric pixel positions are required, images need to be rectified to a standard map projection and evaluated for positional accuracy. The latter step often involves selecting ground control points, which can be an obstacle for high-resolution PS imagery. [PERSON] et al. [24] present a workflow for co-registering Planet images to a geocorrected reference image using a Python implementation of AROSICS (Automatic and Robust Open-Source Image Co-Registration Software for Multi-Sensor Satellite Data [25]). The method relies on a Fourier shift theorem to perform intensity-based registration. The authors do not provide details on how the initial base image was geocorrected but ultimately measured geometric displacement between their first image and subsequent images at less than 6 m. This magnitude of error is still approximately two PS pixels, which may not be sufficient for applications requiring precise geolocation.