Influence of arable land location incorporating its roughness on blue-sky albedo variation. (February 2023) - Cierniewski, Jerzy; Ceglarek, Jakub
Highlights: Location impacts albedo of arable land. Increasing distance from the equator rises mean diurnal albedo variation. Smoothed surfaces show higher albedo variation than rough ones. SALBEC python plugin used in this research is available online. Abstract: This paper quantifies the albedo variation in global bare, arable land, as one of the components of the Earth's land surface, that occurs over relatively short periods of the year but over a large total area. The study focused on the impact of the location of the land's dominant soil units, incorporating their roughness in their diurnal albedo ( α ) variation on the shortest (SDOY) and longest (LDOY) days of the year, and on their mean diurnal albedo ( α d ) values throughout the year under clear-sky conditions. Specialized software was used to generate the aforementioned soil albedo values on the basis of soil reflectance spectra stored in a soil database. It was assumed that the air-dried soil surfaces being tested would present extremely different roughness states: very high, corresponding to those shaped by a plow (Pd), and very low, corresponding to a smoothing harrow (Hs). It was found that the farther the surfaces were from the equator, the higher the minimum α values would be on the SDOY, and the lower they would be on the LDOY. It was determined that the relative difference (AV α d ) between the highest and lowest values of α d for the studied surfaces located within the latitude range of ±15° would be close to 0%. For latitudes within ±20°–±30°, the AV α d values would be 6 and 5% for surfaces shaped by Pd and Hs, respectively. For those at about 65°, AV α d values would even reach 63% and 54%, respectively. The α values of soil surfaces generated from many thousands of reflectance spectra collected in soil databases around the world, and taking into account their roughness, may be used in the future to model Earth's climate changes.