This study presents the SALBEC – Soil ALBEdo Calculator – a Python library and Graphical User Interface designed to predict the diurnal variation of the clear-sky albedo based on the soil surface properties. Such predictions are becoming more and more necessary with the increasing role of remote measurements. The software uses the following input parameters: the soil spectrum, soil roughness, day of the year (DOY) and sample location. It returns the diurnal albedo variation and, as a unique feature, optimal observation time in the form of tables and graphs as outputs. Models created with the SALBEC were compared with the data acquired under near clear-sky conditions. The comparison shows that the differences between the models and measured data do not exceed the variation of input parameters. The software is directed towards scientists and professionals who require precise estimations of the albedo of soils for different field observation times. Our software is issued as free and open source software (FOSS) and is publicly available at
- soil albedo
- soil roughness
- remote sensing
- GUI application
The albedo of an object is the fraction of incident solar radiation reflected from it in the range of 0.3–3 nm, and is a measure integrating the surface reflectance over all view directions (Peddle et al. 2001, Schaepman-Strub et al. 2006). Similar to other components of the Earth's surface, the albedos of bare soil surfaces provide the basic input parameters in modelling biophysical processes associated with the energy transfer between the soil, vegetation and atmosphere (Farmer, Cook 2013). With the continuous growth of available satellites and low-altitude data, the precise estimation of the diurnal variation of the albedo becomes vital (Liang et al. 2010, Qu et al. 2014). Most studies use prescribed values based on the soil maps, and thus are at low spatial resolution (He et al. 2019). In recent years, there has been an increase in the spatial and temporal resolutions of albedo observations obtained from satellite, airborne and unmanned aerial vehicle (UAV) measurements (van Leeuwen, Roujean 2002, Govaerts, Lattanzio 2007, Ortega-Farías et al. 2016, Cao et al. 2018, Canisius et al. 2019, He et al. 2019, Zhou et al. 2019, Wind et al. 2020). However, all reported experiments require the direct observation of the albedo from ground sensors during acquisition. These observations provide information on the diurnal variation of the albedo but cannot be applied to plan the optimal acquisition time.
Several theoretical models of albedo have been proposed in the last 50 years (Temps, Coulson 1977, Ineichen et al. 1990, Gueymard 1987, 1993, Lucht et al. 2000, Enriquez et al. 2012). The complexity of those models varies from straightforward constant models (Baldridge 2009, Liu, Jordan 1963), through isotropic (Nkemdirim 1972, Hay 1993) and zonal (Gueymard 2009) models, to models attempting to generalise albedo as a function of soil and surface inclination (Temps, Coulson 1977). Most of those models contain a constant parameter, which refers to the generalised surface reflectance reflected as one number. Recently Ziar et al. (2019) proposed a comprehensive and advanced model based on surface reflectance supported by several input data. This model allows one to estimate the albedo at different conditions; however, its real behaviour at the specific conditions of bare soils is yet unknown. Moreover, the model requires detailed information about the irradiance, which, in practice, limits its real applications.
The overall level of the broadband blue-sky albedos (
Recently, Cierniewski et al. (2018a) proposed a set of equations that could be used to calculate the diurnal variation of bare soil a, using 153 sets of soil surface measurements collected from Poland, Israel and France. The equations are used as the input data for
For extended periods of several days, months, seasons and years, the average diurnal a of the components are seemingly more useful than their instantaneous values (Grant et al. 2000, Cierniewski et al. 2013). Currently, the a of the Earth's surface components is often obtained from satellite and airborne observations. Cierniewski et al. (2013) proposed the observation of bare soil surfaces at the optimal time (
With the increasing cost of direct observations and increasing possibilities of computer sciences technology, there is a growing interest in advanced tools that simulate the behaviour of complex systems in virtual environments. Such solutions, to be widely used by natural scientists, must have good theoretical foundations and be accessible to users that do not have programming skills. This study aims to present a full-featured software: a Python library and graphical user interface distributed as a free and open source software (FOSS). 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, 2018, 2019) and is designed to predict the diurnal variation of the clear-sky a based on the soil surface properties:
for any soil described by its reflectance spectrum, for any place on Earth, for any daylight time of any day.
for any soil described by its reflectance spectrum,
for any place on Earth,
for any daylight time of any day.
It also provides tools to calculate the value and timing of the mean diurnal a within a certain range. The software uses input parameters that are stable in the long term and are expressed in the form of the full range of laboratory soil spectrum and soil roughness, which are subject to change owing to agricultural treatments. The described software grew from Python scripts previously prepared to calculate This software seems to be unavailable.
This software seems to be unavailable.
The rest of this paper is organised as follows: after describing the theoretical basis for calculating the a using the soil spectra and roughness parameters, we present the Soil ALBEdo Calculator (SALBEC) software design. In the case study section, we discuss the results of selected measurements made in Israel during a field campaign in 2015. Finally, possible extensions and future developments and improvements are discussed.
The diurnal change in
This equation computes the overall a level of the soils with a given roughness at
This equation calculates
This equation calculates the a of the studied soil surfaces (
This equation enables the adjustment of the entire range of the relationship between a of these soils and
The process of converting the input parameters into a soil albedo model is divided into three steps (A–C), as shown in Figure 2. As observed by Cierniewski et al. (2018), the dependency between
First, we calculate the values of
Although the model
Modelling the diurnal a variation using the time at a given location as a direct input is impossible. It requires the prior calculation of a hash table,
Estimation of |
Through experiments, we found that
SALBEC (Fig. 4) is divided into two distinct parts that are distributed together. The first part is the Python library designed to be imported into the Python environment, and the second part is its graphic user interface (GUI) front-end, which can be started directly from the operating system. The GUI is designed to provide the SALBEC functionality to non-programmers but is separated from the core library. Both environments, the Python library and the GUI, have the same functionality, except some GUI-only features that support processing in the visual environment. The Python library can be easily integrated and extended into more complex data processing scripts, while the GUI has a workflow path limited by its functionality. The core library contains three modules (Fig. 4): Soil Database (
The soil database module is a supporting module designed to store and manage soil spectra. Soil spectra can be imported as text or spreadsheet files and can be imported separately during the run-time. However, considering that individual imports for each calculation could be an error-prone task, a separate module was created to import and manage the data. The database is designed as a list of soils, where each entity is stored as a single serialised binary object. Each object contains the spectrum as a wavelength–reflectance pair, geo-location of the soil sample (if available) and pre-computed spectrum derivatives necessary for Eq. (1). Soil names are a convenient way to access spectral data with intelligible names. The module is designed as a single class that facilitates basic management tasks – adding, removing, modifying and renaming the individual entities – over the database. The soil database is independent of the other modules and can easily be expanded into a more universal, domain-agnostic data management tool. The software is distributed with the reflectance spectra for the 33 most extensive agricultural regions globally, averaged based on the georeferenced topsoil samples acquired from national, continental and global soil spectral libraries. These include the European LUCAS (Stevens et al. 2013) and the Global Soil Spectral Library (Rossel 2009, Rossel et al. 2016). The data set and the estimation of optimal observation time for the above-mentioned regions are extensively discussed in the study by Cierniewski and Jasiewicz (2020).
The soil surface albedo module is a step towards calculating the diurnal a at specific locations and times. The module
The third module,
The complete results of the albedo class include a
The GUI is designed in the ‘ribbon’ style to provide quick and intuitive access to all the parameters accepted by the Python modules described above. The application is just one window (Fig. 6) divided into the results (Fig. 6A) and the input ‘ribbon’ bars (Fig. 6B1–B3). The input bar is visually divided into three separate parts.
Pop-up dialogue windows are reduced to the minimum, except for the Soil Manager, which is a separate, GUI-only sub-module. This separate sub-module is divided into two tabs – the collection manager and the spectrum manager (Fig. 6C1 and C2). These do not cover some functions of the underlying core library layer and are designated to manage the soil spectra visually. The additional benefit of the soil manager is the soil spectra browser and visual exporter. Each spectrum must receive a human-readable name during the import to the soil database. The collection manager offers an easy tool to limit the number of named spectra displayed in the soil surface.
The software presented in this study allows the modelling of the a variation of bare soil surfaces with differing roughness. The modelling is simplified to a clear-sky condition and refers to the optimal setup when soil surfaces can be viewed from a satellite. Eqs (1)–(4) used in this study are models fitted to the measurements acquired during several field campaigns in three countries (Poland, France and Israel; see Cierniewski et al. 2015, 2018b, 2019 for details). To demonstrate how the software refers to the real situation, we used the data acquired in Israel during the field campaign in 2015 when near clear-sky conditions were observed.
These data were collected at two locations: N30°59′16″, E34°42′15″ (
Figure 7 shows that there are differences between the real measurements and the results obtained by their adequate models. The shape of the measured and modelled curves is similar, implying that the differences are systematic. Models concerning the measured data are either under- or overestimated. Such results suggest that the error does not stem from the uncertainty of measurements. As the impact of atmospheric conditions was negligible, we presume that the mentioned differences result from the imprecision of the two roughness measures –
At that moment, we could not compare our results with those of other similar tools, since such tools did not exist to our knowledge. The only known software pertains to urban areas (Chimklai et al. 2004) and not cultivated soils, but the results reported by its authors show that differences between the modelled and measured values are even higher than those reported in this study. Eq. (4), which is the core of the software described in our study, works with almost the same error with all types of soils and accepted ranges of surface roughness. Future observations and more data will facilitate the formation of new, potentially more detailed models tailored to specific conditions. The general approach proposed by Ziar et al. (2019) is very promising; however, at the present moment a comparison with our proposal would be disputable. Ziar's model is based on different assumptions and requires several, not always, available data, but our model is intended to work using solely data obtained from the laboratory or public databases.
Two factors potentially affect the computational performance of the SALBEC library. First is the procedure of model fitting accomplished by the
In this paper, we present SALBEC, a fully featured software including Python script and a GUI, dedicated to calculating the bare-soil albedo under clear-sky conditions, which considers the spectral properties and roughness of the soil surface. The software can be used by scientists and professionals who need to consider the albedo in their models and measurements. A unique feature of this software, prediction of the optimal observation time (Cierniewski, Jasiewicz 2020), can be used to plan observation campaigns for satellite scanning and low-altitude airborne measurements or UAV acquisitions. Inclusion of an easy-to-use GUI opens the software to all interested groups, including those without programming skills.
The future development of the software depends on the availability of new measurements, possibly including a wide variety of climate zones and different varieties of topsoil. However, we are sceptical about the possible extension of this particular model to include weather changes during the day. Such changes are challenging to define without direct observations and require the local control of many factors. These factors will also introduce several free parameters to the model, which generally increase the model complexity and, in practice, increase the error of the estimation (Hastie et al. 2009).
Although the equations used in the SALBEC software predict the albedo of air-dried bare soils in clear-sky conditions, these equations were also the basis for determining seasonal shortwave radiation of bare arable land according to soil surface roughness and real state of the atmosphere in European Union (Cierniewski et al. 2019), as well as in Poland and Israel (Cierniewski et al. 2021). The shortwave radiation reaching the examined soil surfaces was obtained from satellite data of the Spinning Enhanced Visible and Infrared Imager instrument (SEVIRI) on board the Meteosat Second Generation.
SALBEC software, including scripts, GUI, user manual, example and basic data can be freely downloaded from