Comparing satellites and vegetation indices for cover crop biomass estimation

Abstract

Cost-share programs based on measures of participation rather than performance are available to farmers who plant cover crops. However, cover crops only provide significant ecological benefits like reduced nutrient loss when adequate biomass is established. The purpose of this study was to determine whether satellite imagery can effectively estimate cover crop biomass in fields with diverse species composition, and whether increased spatial resolution and satellite imaging frequency can increase biomass estimation accuracy. Aboveground biomass samples of 1 m² were collected for 86 sites within 26 agricultural fields containing unique cover crop species composition to assess biomass production. In-field sensors were used to measure normalized difference vegetation index (NDVI) and groundcover percentage. Three satellites (Landsat-8 [30 m resolution], Sentinel-2 [10 m resolution], and PlanetScope [3 m resolution]) were used to calculate eight vegetation indices (VIs) for comparison with cover crop biomass. Multiple linear regression, correlation coefficients, and root mean square error (RMSE) were used to perform hierarchical clustering to rank VIs calculated from each satellite for biomass estimation accuracy. Satellites predicted cover crop biomass at the field level very accurately (r² up to 0.79), demonstrating the potential of large-scale biomass estimation at relatively low cost compared to in-field sampling. All satellite-VI pairs estimated biomass more accurately than the in-field sensors. Performance of VIs varied by satellite, but each satellite had at least one VI that performed very well for both site-level and field-averaged data. When using PlanetScope or Landsat-8 imagery, the perpendicular vegetation index provided the most accurate cover crop biomass estimation on a per-site basis and ratio vegetation index performed best using Sentinel-2 imagery. PlanetScope was the only satellite to provide useable imagery for every site due to increased revisit period; however, its increased spatial resolution did not increase estimation accuracy overall compared to Landsat-8 or Sentinel-2.

Plant phenotyping relevance

衛星画像と植生指数を用いたカバークロップ地上部バイオマス推定を中心に、複数衛星・センサーの精度比較と検証を行っているため、植物形質の計測手法研究に該当する。

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