Evaluation of the Effects of Surface Roughness on the Relationship Between Soil BRF Data and Broadband Albedo

Abstract

This paper reports a preliminary study on correctness of the broadband albedo (αb) of cultivated soils that could be calculated by their narrowband bidirectional reflectance factors (BRF) measured from many directions with a prototype goniometric device in a laboratory on their samples with similar roughness, as these soils revealed in the field. The correctness was tested on examples of Phaeozem, Luvisol, and Albeluvisol with roughness formed by a plow, disk harrow, pulverizing harrow, and smoothing harrow. The αb values of the soils calculated using the equation proposed here were not affected by the root mean square error higher than 0.01 as compared to the data measured in the field if the BRF data were obtained on the soil samples with a sufficiently low roughness. It was found that the roughness of those soil samples should be sufficiently low to ensure that the values of their height standard deviation along the length of the minor axis of a sensor field of view area are greater than 15.Index Terms-Bidirectional reflectance factor (BRF), field and laboratory measurements, hyperspectral directional reflectance, soil surface roughness.

Imaging Spectroscopy for Soil Mapping and Monitoring

“…The roughness of soils, regarded here as irregularities of their surfaces resulting from the existence of soil particles, aggregates, rock fragments and micro-relief configuration, significantly affects soil spectral reflectance. Although this impact was noticed and examined many years ago (Bowers and Hanks 1965;Brennan and Bandeen 1970;Stoner and Baumgardner 1981;Cierniewski 1987;Cierniewski and Courault 1993), it is still underappreciated (Cierniewski et al 2015).…”

“…1 3 Soil roughness also clearly affects albedo. Analysing broadband soil albedo variation during the day, it was found that soil roughness affected not only the overall level of that variation (Cierniewski et al 2015), but also the intensity of the albedo, which increased from θ s at the local noon to about 75°-80°. Rough, deeply ploughed soil surfaces showed almost no rise in albedo values for θ s lower than 75°, while the same soil, but smoothed, exhibited a gradual albedo increase at these angles.…”

Retrieval of Sediment Filling Factor in a Salt Panne from Multi-View Hyperspectral Imagery

“…In most past studies, inversion of the large set of free parameters in the Hapke IMSA model used either laboratory observations and validation data [6,[51][52][53], astronomical observational data from ground-based systems, or probes [15,16,18,19,21] for which direct corroborating ground truth is difficult to obtain, or combinations of these two types of data [2,54,55]. Even when the input data have been Earth remote sensing imagery, typically the Hapke model has been fit to the remote sensing imagery data, and it is an error in the reconstruction of the original spectral reflectance of the remote sensing imagery from the optimized Hapke parameters that has served as validation rather than direct comparison to physical measurements on the ground [7].…”

SALBEC – A Python Library and GUI Application to Calculate the Diurnal Variation of the Soil Albedo

“…1) with the changing solar zenith angle (θ s ), reaching their minimum at the local solar noon and approaching 1 at sunrise and sunset (Monteith, Szeicz 1961, Wang et al 2004, Oguntunde et al 2006. Cierniewski et al (2015) found that the surface roughness affects not only the overall a level of soils but also the a growth relative to θ s between the local solar noon and θ s » 75°. The a of rough soil surfaces, such as deeply ploughed soils, are almost constant in this range, while those of the same soils when smoothed (for example, by a smoothing harrow) clearly increase.…”

“…Python was selected upon its growing participation in both remote sensing (Bunting et al 2014) and soil science (Boudoire et al 2020). The software follows the concepts described above (Cierniewski et al 2015(Cierniewski et al , 2018(Cierniewski et al , 2019 and is designed to predict the diurnal variation of the clear-sky a based on the soil surface properties: 1. for any soil described by its reflectance spectrum, 2. for any place on Earth, 3. for any daylight time of any day.…”