Soil Organic Carbon mapping of partially vegetated agricultural fields with imaging spectroscopy

Soil Organic Carbon (SOC) is one of the key soil properties, but the large spatial variation makes continuous mapping a complex task. Imaging spectroscopy has proven to be an useful technique for mapping of soil properties, but the applicability decreases rapidly when fields are partially covered with vegetation. In this paper we show that with only a few percent fractional maize cover the accuracy of a Partial Least Square Regression (PLSR) based SOC prediction model drops dramatically. However, this problem can be solved with the use of spectral unmixing techniques. First, the fractional maize cover is determined with linear spectral unmixing, taking the illumination and observation angles into account. In a next step the influence of maize is filtered out from the spectral signal by a new procedure termed Residual Spectral Unmixing (RSU). The residual soil spectra resulting from this procedure are used for mapping of SOC using PLSR, which could be done with accuracies comparable to studies performed on bare soil surfaces (Root Mean Standard Error of Calibration = 1.34 g/kg and Root Mean Standard Error of Prediction = 1.65 g/kg). With the presented RSU approach it is possible to filter out the influence of maize from the mixed spectra, and the residual soil spectra contain enough information for mapping of the SOC distribution within agricultural fields. This can improve the applicability of airborne imaging spectroscopy for soil studies in temperate climates, since the use of the RSU approach can extend the flight-window which is often constrained by the presence of vegetation.     

Countergradient Fluxes of Conserved Variables in the Clear convective and Stratocumulus-topped Boundary Layer: The role of the Entrainment Flux

Large-eddy simulations of a clear convective boundary layer (CBL)and a stratocumulus-topped boundary layer are studied. Bottom-upand a top-down scalars were included in the simulations, and theprinciple of linear superposition of variables was applied toreconstruct the fields of any arbitrary conserved variable.This approach allows a systematic analysis of countergradient fluxesas a function of the flux ratio, which is defined as the ratio betweenthe entrainment flux and the surface flux of the conserved quantity.In general, the turbulent flux of an arbitrary conserved quantityis counter to the mean vertical gradient if the heights where thevertical flux and the mean vertical gradient change sign do notcoincide. The regime where the flux is countergradient is thereforebounded by the so-called zero-flux and zero-gradient heights. Becausethe vertical flux changes sign only if the entrainment flux has anopposite sign to the surface flux, countergradient fluxes arepredominantly found for negative flux ratios. In the CBL the fluxratio for the virtual potential temperature is, to a good approximation,constant, and equal to -0.2. Only if the moisture contribution to thevirtual potential temperature is negligibly small will the flux ratio forthe potential temperature be equal to this value. Otherwise, theflux ratio for the potential temperature can have any arbitrary(negative) value, and, as a consequence, the fluxes for thepotential temperature and the virtual potential temperature willbe countergradient at different heights. As a practical application ofthe results, vertical profiles of the countergradient correction termfor different entrainment-to-surface-flux ratios are discussed.