## 2 Materials and Methods  
This study relied upon 15 flight lines collected in late summer of 2020 with the Advanced Visible Infrared Imaging Spectrometer-Next Generation (AVIRIS-ng) instrument. AVIRIS-ng measures radiance from 380 to 2510 nm at 5 nm intervals [28]. All flight lines were downloaded from the AVIRIS-ng data portal ([[https://avirisng.jpl.nasa.gov/dataportal](https://avirisng.jpl.nasa.gov/dataportal)]([https://avirisng.jpl.nasa.gov/dataportal](https://avirisng.jpl.nasa.gov/dataportal)), accessed on 1 June 2022) as orthorectified radiance. Each line was converted to surface reflectance using the Imaging Spectrometer Optimal FITting algorithm (ISOFIT) [29]. ISOFIT version 2.9.2 was used, as cloned from [[https://github.com/isofit/isofit](https://github.com/isofit/isofit)]([https://github.com/isofit/isofit](https://github.com/isofit/isofit)) (accessed on 1 June 2022). For all ISOFIT runs, the empirical line method flag was turned on (ELM = 1), with segmentation size = 200. After atmospheric correction, reflectance data in wavelength ranges 376-421, 1123-1173, 1323-1498, 1774-2024, and 2450-2500 nm (AVIRIS-ng bands 1-10, 150-160, 190-225, 280-330, 415-425) were excluded from statistical analyses due to atmospheric contamination, resulting in the retention of 306 of 425 bands. Several 600 \(\times\) 800-pixel subsets were extracted from each flightline and mosaiced into a single composite image cube of 64,000,000 pixel spectra. All lines were flown in California between 24 July and 24 September 2020 (Figure 1). Flight altitude for all lines was approximately 10,300 ft (3140 m), resulting in a ground sampling distance of roughly 3 m. The lines sampled broad crop and soil diversity within one of the most productive agricultural landscapes on Earth [30]. Flights spanned a broad diversity of soils [31] hosting orchards, vineyards, and row crops, as well as some native and suburban vegetation. Nearly all agriculture in the study area is irrigated and planted/harvested asynchronously, resulting in a wide range of soil exposure, moisture content, growth stage, and canopy closure within each line. This study focuses on the question of sensor interoperability. Specifically, we seek to isolate the subset of interoperability which is fundamentally _spectral_ from platform- or acquisition-specific factors such as view and illumination geometry, ground sampling distance, second-to-minute scale atmospheric variability, or sensor radiometric calibration. Here, we control for these factors by directly simulating Planet data by convolving the AVIRIS-ng surface reflectance with the spectral response of the SuperDove sensor obtained from: [[https://developers.planet.com/docs/apis/data/sensors/](https://developers.planet.com/docs/apis/data/sensors/)]([https://developers.planet.com/docs/apis/data/sensors/](https://developers.planet.com/docs/apis/data/sensors/)) (accessed on 1 June 2022). A total of 6 indices were chosen on the basis of their popularity, as recently reviewed by [26]. The indices used in this study are: Difference Vegetation Index (DVI, [32]), Simple Ratio (SR [10], scaled by 0.1), Normalized Difference Vegetation Index (NDVI [11]), Near-Infrared Reflectance of Vegetation (NIRv [15]), Enhanced Vegetation Index (EVI [13]), and two-band Enhanced Vegetation Index (EVI2 [14]). Formulas for each index are given in Table 1.  
Similarity metrics were computed in Python. Pearson correlation coefficients (\(\rho\)) were computed using NumPy v1.21.2 (numpy.corrcoef). Mutual information (MI) was computed