# Mixed-Species Cover Crop Biomass Estimation Using Planet Imagery
###### Abstract  
Cover crop biomass is helpful for weed and pest control, soil erosion control, nutrient recycling, and overall soil health and crop productivity improvement. These benefits may vary based on cover crop species and their biomass. There is growing interest in the agricultural sector of using remotely sensed imagery to estimate cover crop biomass. Four small plot study sites located at the United States Department of Agriculture Agricultural Research Service, Crop Production Systems Research Unit farm, Stoneville, MS with different cereals, legumes, and their mixture as fall-seeded cover crops were selected for this analysis. A randomized complete block design with four replications was used at all four study sites. Cover crop biomass and canopy-level hyperspectral data were collected at the end of April, just before cover crop termination. High-resolution (3 m) PlanetScope imagery (Dove satellite constellation with PS2.SD and PSB.SD sensors) was collected throughout the cover crop season from November to April in the 2021 and 2022 study cycles. Results showed that mixed cover crop increased biomass production up to 24% higher compared to single species rye. Reflectance bands (blue, green, red and near infrared) and vegetation indices derived from imagery collected during March were more strongly correlated with biomass (\(r\) = 0-0.74) compared to imagery from November (\(r\) = 0.01-0.41) and April (\(r\) = 0.03-0.57), suggesting that the timing of imagery acquisition is important for biomass estimation. The highest correlation was observed with the near-infrared band (\(r\) = 0.74) during March. The R\({}^{2}\) for biomass prediction with the random forest model improved from 0.25 to 0.61 when cover crop species/mix information was added along with Planet imagery bands and vegetation indices as biomass predictors. More study with multiple timepoint biomass, hyperspectral, and imagery collection is needed to choose appropriate bands and estimate the biomass of mix cover crop species. Remote sensing can be used to efficiently document the adoption and benefits of CC [13], thus reducing the time and the resources needed for extensive field sampling and measurements. Relatively more work has been performed on classifying cover crops at the landscape scale using coarse resolution satellite imagery [14; 15; 16; 17] than on estimating cover crop biomass using fine-scale imagery. Using relatively finer resolution imagery [18] reported that monoculture grass biomass was strongly correlated with SPOT-5 derived normalized difference vegetation index (NDVI) values. Similarly, [19] showed that Sentinel-2 imagery can help assess cover crop growth. However, a within-field biomass estimation requires more precise reflectance characteristics of cover crops. Additionally, work on cover crop biomass estimation using remote sensing approach is more challenging due to the adoption of mix cover crop species by growers, which demands a more detailed reflectance characterization of the mixed species. Hence, estimating biomass yield and their benefit with remote sensing requires reflectance characteristics and identification of appropriate spectral bands and vegetation indices for each CC species and their mix. Planet Lab (Planet Labs, Inc., San Francisco, CA, USA, [[https://www.planet.com](https://www.planet.com)]([https://www.planet.com](https://www.planet.com)), accessed on 5 November 2022) provides free daily imagery for the research purpose in 3 m spatial resolution [20] that can be used for plot-level analysis of cover crop studies. The PlanetScope (PS) satellite constellation was launched in phases, with groups of individual small satellites (called smallsats or cubesats) named Dove classic, Dove-R and SuperDove. The three generations of instruments on these satellites are called PS2, PS2.SD and PSB.SD, with blue, green, red and near infrared (NIR) sensor capabilities. The third generation of instrument, PSB.SD, was launched in April 2019, with four additional visible bands (coastal blue, green, yellow and red-edge).