# Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
###### Abstract
Planet imagery undergoes varying levels of processing before it is released for analytical purpose. Ortho Scene-Analytics (Level 3B) surface reflectance imagery is an orthorectified, radiometrically calibrated, and atmospherically corrected product that captures bottom of atmosphere reflectance characteristics [21]. The blue, green, red, and NIR images from these products can be used to derive a variety of vegetation indices (VIs) at 3 m spatial resolution. For research purposes, users can sign up with the National Aeronautics and Space Administration (NASA) Commercial Smallsat Data Acquisition (CSDA) program to access these commercial products free of charge. Through the NASA-CSDA program Planet, data are available to U.S. government federal civil agencies and National Science Foundation funded researchers. Students and researchers from any university can access these data through Planet's Education and Research Program. Canopy-level reflectance data collected with the hyperspectral instrument can be helpful in developing reflectance characteristics of single- and mixed-species cover crops. Unlike multispectral sensors, hyperspectral sensors record reflected energy in narrow and contiguous channels. This allows more information to be collected and the reflectance characteristics of the target to be developed. Depending on the instrument type and capacity, data collected range from ultraviolet (UV, -250 nm) to short-wave infrared (SWIR, -2500 nm). Usually, wavelengths from visible (VIS) to near infrared (NIR) range (400-700 nm) are used for vegetation estimation based on the absorption of light by leaf pigmentation. Mesophyll cell reflects most of the incident light in NIR (700-1300 nm) range while middle-infrared (MIR, 1300-2500 nm) region of the light spectrum is mostly absorbed by soil and leaf water contents [22]. Hence these absorption and reflectance features of specific portion of the light spectrum are used to develop different vegetation indices. Most vegetation indices developed are closely related to biomass, leaf area index, and photosynthetically active radiation absorption and reported widely for vegetation estimation [22]. Furthermore, because hyperspectral reflectance data are rich in spectral information, they can be used to separate crops into taxonomic units such as species and even in varieties [23; 24]. An exploratory analysis of ongoing cover crop research will be presented in this paper. Although biomass estimation at landscape level has been evaluated by several other researchers [13; 14; 15; 16; 17; 18], mixed-species cover crop biomass estimation using plot-level dataset is lacking in the literature. The objective is to evaluate the feasibility of mixed-speciescover crop biomass estimation using high-resolution satellite imagery. Specifically, we hypothesize that (1) cover crop biomass production increases with mixed species; (2) Planet imagery collected during later growth stages is more strongly correlated with cover crop biomass; and (3) biomass estimation using remotely sensed data with a machine learning approach is not affected by mixed-species cover crops. The reasoning behind the third hypothesis is that more information will be captured throughout the growing season by different wavebands, accounting for species variability. Planet imagery will be used for this purpose. One-time scanned hyperspectral reflectance data will also be used to compare the model output. Although four different cover crop study sites were combined, the comparison of the hyperspectral data is performed with the aim of developing future research directions, rather than drawing any conclusions per se.