László Pásztor

Spectral band selection for the characterization of salinity status of soils

Abstract Reflectance spectra of salt-affected surface soil samples have been measured at 10 nm spectral resolution between 495 nm and 2395 nm. The samples were collected in California (41) and in Hungary (49) to compare the effect of different salinization and alkalization processes on reflectance properties. The data set consisted of 272 spectra having 2–12 cases (repetitions) for each sample. Sixteen classes were defined based on chemical soil properties, accounting for changes in pH, electrical conductivity (EC), and exchangeable sodium percentage (ESP), of which 12 were represented in the sample. The reflectance data set was statistically analyzed using a modified stepwise principal component analysis (MSPCA) approach to select 1, 2, 3, … bands for classification of salinity status. Recognition of the above-described classification was tested by discriminant function analysis (DFA). Its results can be applied in further studies for weighing spectral bands according to their sensitivity to the chosen classification as well as in defining broad, but still potentially sufficient bands. Recognition accuracy of salinity status was 91%, 90%, and 88% with 10 nm, 20 nm, and 40 nm bands, respectively, for the entire data set. Comparison with PCA using all bands showed only slight differences. The California soil samples had more distinct spectral characteristics than the Hungarian ones. Key spectral ranges were identified in the visible (550–770 nm), near-infrared (900–1030 nm, 1270–1520 nm), and middle infrared (1940–2150 nm, 2150–2310 nm, 2330–2400 nm) portion of the spectrum at 20 nm, 40 nm, and 80 nm spectral resolution. Two of these (1270–1520 nm and 1940–2150 nm) cannot be used with satellite data due to water vapor absorption in the atmosphere. In addition, six broad bands in these ranges were identified, leading to considerably higher overall accuracy than currently available Landsat MSS, TM, and SPOT XS, in terms of spectral recognition of salinity status.

Maps of heavy metals in the soils of the European Union and proposed priority areas for detailed assessment

Soil contamination is one of the greatest concerns among the threats to soil resources in Europe and globally. Despite of its importance there was only very course scale (1/5000km(2)) data available on soil heavy metal concentrations prior to the LUCAS topsoil survey, which had a sampling density of 200km(2). Based on the results of the LUCAS sampling and auxiliary information detailed and up-to-date maps of heavy metals (As, Cd, Cr, Cu, Hg, Pb, Zn, Sb, Co and Ni) in the topsoil of the European Union were produced. Using the maps of heavy metal concentration in topsoil we made a spatial prediction of areas where local assessment is suggested to monitor and eventually control the potential threat from heavy metals. Most of the examined elements remain under the corresponding threshold values in the majority of the land of the EU. However, one or more of the elements exceed the applied threshold concentration on 1.2Mkm(2), which is 28.3% of the total surface area of the EU. While natural backgrounds might be the reason for high concentrations on large proportion of the affected soils, historical and recent industrial and mining areas show elevated concentrations (predominantly of As, Cd, Pb and Hg) too, indicating the magnitude of anthropogenic effect on soil quality in Europe.

Compilation of 1:50,000 scale digital soil maps for Hungary based on the digital Kreybig soil information system

After several years of digital processing of legacy soil data collected by the Kreybig soil survey, the nationwide development of the digital Kreybig soil information system (DKSIS) made possible the compilation of soil property and function maps for the territory of Hungary at a scale of approximately 1:25,000–1:50,000. The Kreybig legacy data are spatially most detailed nationwide dataset related to soils which covers the whole area of the country. It simultaneously contains two types of geometric datasets: approximately 100,000 soil mapping units (SMUs) and 250,000 sampling plots. SMUs are characterized by several complex soil physical and chemical categories and detailed soil properties which are provided for soil profiles whose description in the digital environment is supported by a specific relational database. Primary digital soil maps can be compiled based on the polygons-type entities, while suitable spatial inference of profile-related variables makes the composition of secondary, regionalized digital soil maps possible, too. In our paper, we present example for both types.

Coupling the 4M crop model with national geo-databases for assessing the effects of climate change on agro-ecological characteristics of Hungary

The 4M crop model was used to investigate the prospective effects of climate change on the agro-ecological characteristics of Hungary. The model was coupled with a detailed meteorological database and spatial soil information systems covering the whole territory of Hungary. Plant-specific model parameters were determined by inverse modeling. Future meteorological data were produced from the present meteorological data by combining a climate change scenario and a stochastic weather generator. Using the available and the generated data, the present and the prospective agro-ecological characteristics of Hungary were determined. According to the simulation results, average yields will decrease considerably (~30%) due to climate change. The rate of nitrate leaching will prospectively decrease as well. The fluctuations of both the yields and the annual nitrate leaching rates will most likely increase approaching the end of the twenty-first century. On the basis of the simulation results, the role of autumn crops is likely to become more significant in Hungary. The achieved results can be generalized for more extended regions based on the concept of spatial (geographical) analogy.

Elaboration and applications of spatial soil information systems and digital soil mapping at Research Institute for Soil Science and Agricultural Chemistry of the Hungarian Academy of Sciences

Hungary has long traditions in soil survey and mapping. Large amount of soil information is available in various dimensions and generally presented in maps, serving different purposes as to spatial and/or thematic aspects. Increasing the proportion of soil-related data has been digitally processed and organized into various spatial soil information systems (SSISs). The most current countrywide ones have been elaborated by and available at Research Institute for Soil Science and Agricultural Chemistry of the Hungarian Academy of Sciences (RISSAC HAS). The existing maps, data and systems served the society for many years; however, the available data are no longer fully satisfactory for the recent needs of policy making. There were numerous initiatives for the digital processing, completion, improvement and integration of the existing soil datasets. In our paper, we briefly present the national SSISs developed and maintained by our institute and some examples are given regarding how their functionality was extended by digital soil mapping for the solution of specific soil-related demands.

Mapping geogenic radon potential by regression kriging.

Radon ((222)Rn) gas is produced in the radioactive decay chain of uranium ((238)U) which is an element that is naturally present in soils. Radon is transported mainly by diffusion and convection mechanisms through the soil depending mainly on the physical and meteorological parameters of the soil and can enter and accumulate in buildings. Health risks originating from indoor radon concentration can be attributed to natural factors and is characterized by geogenic radon potential (GRP). Identification of areas with high health risks require spatial modeling, that is, mapping of radon risk. In addition to geology and meteorology, physical soil properties play a significant role in the determination of GRP. In order to compile a reliable GRP map for a model area in Central-Hungary, spatial auxiliary information representing GRP forming environmental factors were taken into account to support the spatial inference of the locally measured GRP values. Since the number of measured sites was limited, efficient spatial prediction methodologies were searched for to construct a reliable map for a larger area. Regression kriging (RK) was applied for the interpolation using spatially exhaustive auxiliary data on soil, geology, topography, land use and climate. RK divides the spatial inference into two parts. Firstly, the deterministic component of the target variable is determined by a regression model. The residuals of the multiple linear regression analysis represent the spatially varying but dependent stochastic component, which are interpolated by kriging. The final map is the sum of the two component predictions. Overall accuracy of the map was tested by Leave-One-Out Cross-Validation. Furthermore the spatial reliability of the resultant map is also estimated by the calculation of the 90% prediction interval of the local prediction values. The applicability of the applied method as well as that of the map is discussed briefly.

Mapping of topsoil texture in Hungary using classification trees

ABSTRACT Spatial information about physical soil properties is in great demand, being basic input data in numerous applications. Soil texture can be characterized by different approaches, such as particle size distribution, plasticity index or soil texture classification. In accordance with the increasing demands for spatial soil texture information, our aim was to compile a topsoil texture class map for Hungary with an appropriate spatial resolution, using the United States Department of Agriculture soil texture classes. The ‘Classification and Regression Trees’ method was applied because it is widely used in Digital Soil Mapping, and has numerous advantages. Primary soil data were provided by the Hungarian Soil Information and Monitoring System. A digital elevation model and its derived components, geological and land cover map, and appropriate remotely sensed products together with the soil map featuring overall physical properties provided by the Digital Kreybig Soil Information System were used as auxiliary environmental co-variables. The resulting map can be used as direct input data in meteorological and hydrological modelling as well as in spatial planning.

Variations for the Implementation of SCORPAN’s “S”

Development of DSM can be notably attributed to frequent limitations in the availability of proper soil information; consequently, it has been typically used in cases featured by limited soil data. Since SCORPAN equation includes other or previously measured properties of soil, the usage of legacy soil data supports the applicability of DSM and improves the accuracy of DSM products as well. Nevertheless, the occurrent abundance of available soil information poses new demands on and at the same time opens new possibilities in the application of DSM methods. A great amount of soil information has been collected in Hungary in the frame of subsequent surveys and assessments. The majority of these legacy soil data were integrated in various spatial soil information systems. Our paper presents three approaches for the application of Hungary’s most extended legacy soil data source in goal-oriented digital soil mapping.

Stochastic modelling of N-leaching using GIS and multivariate statistical methods

After the growing season, a part of the nitrogen remains in forms sensitive to changes of the conditions, such as nitrate. In years with above-average precipitation a significant amount of nitrate can leave the rooting zone. Integration of knowledge related to environmental conditions of a certain area with the soil, water, and crop management practices helps to prevent the simultaneity of the unfavourable processes leading to nitrate leaching, thus water resources may be protected from nitrate pollution of agricultural origin. In our work we present a stochastic approach for the evaluation of the vulnerability of soils for nitrate leaching. The method was applied for mapping N-leaching hazard in Hungary at a scale of 1:1M.

Compilation of Functional Soil Maps for the Support of Spatial Planning and Land Management in Hungary

The tasks of national spatial planning (i.e., delineation of areas with natural constraints or areas with excellent productivity; support of irrigation strategies; flood, drought, and climate change impact risk assessment) increasingly demand advanced or new kinds of spatial soil information, which cannot be fully satisfied by legacy soil maps or formerly elaborated databases. Due to the lack of recent, extended, nationwide mapping, the data of previous surveys should be exploited thoroughly. Digital soil mapping integrates geographic information systems (GIS), geostatistical, and data mining tools and makes possible the elaboration of target-specific soil maps with improved and/or specific thematic, spatial, and temporal accuracy as opposed to former, more general soil maps. For the satisfaction of the recent demands, soil conditions of Hungary have been digitally mapped based on various available recent and legacy soil datasets, and spatially exhaustive, environmental, and auxiliary information. The produced digital soil property maps have been miscellaneously utilized in various regional planning activities.