A new application of cosmic neutron sensing for monitoring plant traits

Abstract

Continuous information on plant traits such as plant height, leaf area index (LAI), and above-ground biomass (AGB) is important in the study of plant growth and such information can help farmers achieve better yields while reducing agricultural inputs, e.g. through more efficient water use. Knowledge on plant traits is also key to further test and develop crop and land surface models. Cosmic-ray neutron sensors (CRNS) have primarily been used to determine soil moisture. Recently, Jakobi et al. (2022) found that thermal neutrons can be used to monitor aboveground biomass (ABG) and that the variations in measured thermal neutron intensity may also depend on the vegetation biomass and structure. However, different soil properties of the test sites may have influenced the results (e.g. related to differences in soil chemistry). In this follow-up study, a single agricultural field was investigated over a long measurement period (2015-2023) to avoid site-specific effects on the CRNS measurements. This new dataset contains different crop rotations with repetitions of the same crop and continuous measurements of plant height instead of sporadic biomass measurements. Based on this data, we developed regression models that take into account plant structure to predict traits (i.e. plant height and LAI) from observed thermal neutron intensity.The annual regression models for plant height provided generally high R²-values (0.86 on average), with the highest values found for potato and winter wheat. An aggregation by crop type of the different seasons resulted in a slight reduction of the R² to 0.84 for winter wheat (3 seasons), 0.68 for sugar beet (2 seasons), and 0.75 for potato (2 seasons). The slope values of these regressions were distinctly different, thus supporting the assumption that the relationship between plant traits and thermal neutron intensity depends on vegetation structure. The root mean square error (RMSE) of the plant height predicted with thermal neutrons were 12 cm for winter wheat and 14 cm for both sugar beet and potato. In addition, we tested a prediction of LAI based on thermal neutrons. For this, we used a regression model that predicts LAI based on plant height (R²: 0.78). Using this model, we were able to predict the LAI for a period of 5 years with LAI observation data with an RMSE of 1.23 m/m, which is still within the uncertainty range of radiation-based LAI methods (Fang et al., 2019). Independent validation was performed also against spatio-temporal LiDAR-based plant height and multispectral-based LAI measurements, each averaged for the CRNS footprint area. Our results demonstrate the potential of cosmic-ray neutron sensing for continuous monitoring of plant traits at the field scale. LiteratureFang, H., F. Baret, S. Plummer and G. Schaepman‐Strub (2019): An overview of global leaf area index (LAI): Methods, products, validation, and applications. Reviews of Geophysics 57(3): 739-799. DOI: 10.1002/hyp.11274Jakobi, J., J.A. Huisman, H. Fuchs, H. Vereecken and H. Bogena (2022): Potential of Thermal Neutrons to Correct Cosmic-Ray Neutron Soil Moisture Content Measurements for Dynamic Biomass Effects. Water Resour. Res. 58(8): e2022WR031972. DOI: 10.1029/2022WR031972

Plant phenotyping relevance

熱中性子センサーを用いて草丈・LAI・地上部バイオマスを連続推定する手法を開発し、回帰モデルとLiDAR・マルチスペクトル測定で検証しており、植物形質の取得手法が中心である。

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