Review of soil salinity assessment for agriculture across multiple scales using proximal and/or remote sensors

Mapping and monitoring soil spatial variability is particularly problematic for temporally and spatially dynamic properties such as soil salinity. The tools necessary to address this classic problem only reached maturity within the past 2 decades to enable field- to regional-scale salinity assessment of the root zone, including GPS, GIS, geophysical techniques involving proximal and remote sensors, and a greater understanding of apparent soil electrical conductivity (ECa) and multi- and hyperspectral imagery. The concurrent development and application of these tools have made it possible to map soil salinity across multiple scales, which back in the 1980s was prohibitively expensive and impractical even at field scale. The combination of ECa-directed soil sampling and remote imagery has played a key role in mapping and monitoring soil salinity at large spatial extents with accuracy sufficient for applications ranging from field-scale site-specific management to statewide water allocation management to control salinity within irrigation districts. The objective of this paper is: (i) to present a review of the geophysical and remote imagery techniques used to assess soil salinity variability within the root zone from field to regional scales; (ii) to elucidate gaps in our knowledge and understanding of mapping soil salinity; and (iii) to synthesize existing knowledge to give new insight into the direction soil salinity mapping is heading to benefit policy makers, land resource managers, producers, agriculture consultants, extension specialists, and resource conservation field staff. The review covers the need and justification for mapping and monitoring salinity, basic concepts of soil salinity and its measurement, past geophysical and remote imagery research critical to salinity assessment, current approaches for mapping salinity at different scales, milestones in multi-scale salinity assessment, and future direction of field- to regional-scale salinity assessment

Hyperspectral Modeling of Relative Water Content and Nitrogen Content in Sorghum and Maize

Sorghum and maize are two of the most important cereal grains worldwide. They are important industrially, and also serve as staple crops for millions of people across the world. With climate change, increasing frequencies of droughts, and crops being planted on more marginal land, it is important to breed sorghum and maize cultivars that are tolerant to drought and low fertility soils. However, one of the largest constraints to the breeding process is the cycle time between cultivar development and release. Early evaluation of cultivars with increased the ability to maintain water status under drought and increases nitrogen contents under nitrogen stress could be the key to decreasing breeding cycle time. New tools for non-destructive, high throughput phenotyping are needed to evaluate new cultivars. These new tools can also be used for monitoring and management of crops to improve productivity. Hyperspectral imaging holds promise as one tool to improve the speed and accuracy of predicting numerous plant traits including abiotic stress tolerance characteristics. In this thesis, hyperspectral imaging projects were designed to develop and test prediction models for relative water content (RWC) and nitrogen (N) content of sorghum and maize. The first study utilized three different genotypes of sorghum in an automated hyperspectral imaging system in greenhouses at Purdue University. From this study, models were developed for relative water content and nitrogen content using the data from all three genotypes collectively as well as the data from each genotype individually. Models developed using the spectral and morphological features obtained from the hyperspectral images are predictive of both relative water content and nitrogen content. The coefficients of determination (R2) for all graphs comparing the predicted relative water content to the reference relative water content of sorghum averaged 0.90 while the same graphs for maize averaged 0.64. The coefficients of determination for all graphs comparing the predicted nitrogen content to the reference nitrogen content of sorghum averaged 0.85 while the same graphs for maize averaged 0.61. Models built only with the spectral features for sorghum were also predictive of both relative water content and nitrogen content. The coefficients of determination for all graphs comparing the predicted relative water content to the reference relative water content of sorghum averaged 0.91 while the same graphs for nitrogen content in sorghum averaged 0.85. The nitrogen content models developed using the data from the Tx7000 genotype are highly predictive of both Tx7000 and B35 but not highly predictive of Tx623. However, models developed using the data from Tx623 are highly predictive of all three genotypes. Another important finding from this study was that the water and nitrogen signals overlap and the most predictive models are developed from data where water and nitrogen vary continuously. Models to predict one factor that do not account for variation in the other factor are not very accurate. The second experiment utilized hyperspectral imaging to characterize RWC and N content of maize. Models for RWC and N content were developed using spectral and morphological features. The models developed for maize were not as predictive as the models for sorghum but they were still predictive of RWC and N content for the models developed using all six genotypes and the models developed using the data from the individual genotypes. Models built using the four half-sibling genotypes were not more predictive than the models based on all six genotypes. The final portion of this thesis explored predictions across species using both the sorghum and maize data. We found that models developed using only sorghum were not predictive of the maize reference measurements. However, when the sorghum and maize data were combined and used to generate models, both the RWC model and the N content model were highly predictive for both reference measurements

Remote Sensing Of Rice-Based Irrigated Agriculture: A Review

The ‘Green Revolution’ in rice farming of the late 1960’s denotes the beginning of the extensive breeding programs that have led to the many improved rice varieties that are now planted on more than 60% of the world’s riceland (Khush, 1987). This revolution led to increases in yield potential of 2 to 3 times that of traditional varieties (Khush, 1987). Similar trends have also been seen in the Irrigation Areas and Districts of southern New South Wales (NSW) as the local breeding program has produced many improved varieties of rice adapted to local growing conditions since the 1960’s (Brennan et al., 1994). Increases in area of rice planted, rice quality, and paddy yield resulted (Brennan et al., 1994). Increased rice area, however, has led to the development of high water tables and risk of large tracts of land becoming salt-affected in southern NSW (Humphreys et al., 1994b). These concerns have led to various environmental regulations on rice in the region, culminating in 1994 when restrictions on rice area, soil suitability, and water consumption were fully enacted (Humphreys et al., 1994b). Strict environmental restrictions in combination with large areas of land make the management of this region a difficult task. Land managers require, among other things, a way of regulating water use, assessing or predicting crop area and productivity, and making management decisions in support of environmentally and economically sustainable agriculture. In the search for more time and cost effective methods for attaining these goals, while monitoring complex management situations, many have turned to remote sensing and Geographic Information System (GIS) technologies for assistance. The spectral information and spatial density of remote sensing data lends itself well to the measurement of large areas. Since the launch of LANDSAT-1 in 1972, this technology has been used extensively in agricultural systems for crop identification and area estimation, crop yield estimation and prediction, and crop damage assessment. The incorporation of remote sensing and GIS can also help integrate management practices and develop effective management plans. However, in order to take advantage of these tools, users must have an understanding of both what remote sensing is and what sensors are now available, and how the technology is being used in applied agricultural research. Accordingly, a description of both follows: first a description of the technology, and then how it is currently being applied. The applications of remote sensing relevant to this discussion can be separated into crop type identification; crop area measurement; crop yield; crop damage; water use/ moisture availability (ma) mapping; and water use efficiency monitoring/mapping. This report focuses on satellite remote sensing for broad-scale rice-based irrigation agricultural applications. It also discusses related regional GIS analyses that may or may not include remote sensing data, and briefly addresses other sources of finer-scale remote sensing and geospatial data as they relate to agriculture. Since a complete review of the remote sensing research was not provided in the rice literature alone, some generic agricultural issues have been learned from applications not specifically dealing with rice. Remote sensing specialists may wish to skip to section 2

Remote Sensing Of Rice-Based Irrigated Agriculture: A Review

The ‘Green Revolution’ in rice farming of the late 1960’s denotes the beginning of the extensive breeding programs that have led to the many improved rice varieties that are now planted on more than 60% of the world’s riceland (Khush, 1987). This revolution led to increases in yield potential of 2 to 3 times that of traditional varieties (Khush, 1987). Similar trends have also been seen in the Irrigation Areas and Districts of southern New South Wales (NSW) as the local breeding program has produced many improved varieties of rice adapted to local growing conditions since the 1960’s (Brennan et al., 1994). Increases in area of rice planted, rice quality, and paddy yield resulted (Brennan et al., 1994). Increased rice area, however, has led to the development of high water tables and risk of large tracts of land becoming salt-affected in southern NSW (Humphreys et al., 1994b). These concerns have led to various environmental regulations on rice in the region, culminating in 1994 when restrictions on rice area, soil suitability, and water consumption were fully enacted (Humphreys et al., 1994b). Strict environmental restrictions in combination with large areas of land make the management of this region a difficult task. Land managers require, among other things, a way of regulating water use, assessing or predicting crop area and productivity, and making management decisions in support of environmentally and economically sustainable agriculture. In the search for more time and cost effective methods for attaining these goals, while monitoring complex management situations, many have turned to remote sensing and Geographic Information System (GIS) technologies for assistance. The spectral information and spatial density of remote sensing data lends itself well to the measurement of large areas. Since the launch of LANDSAT-1 in 1972, this technology has been used extensively in agricultural systems for crop identification and area estimation, crop yield estimation and prediction, and crop damage assessment. The incorporation of remote sensing and GIS can also help integrate management practices and develop effective management plans. However, in order to take advantage of these tools, users must have an understanding of both what remote sensing is and what sensors are now available, and how the technology is being used in applied agricultural research. Accordingly, a description of both follows: first a description of the technology, and then how it is currently being applied. The applications of remote sensing relevant to this discussion can be separated into crop type identification; crop area measurement; crop yield; crop damage; water use/ moisture availability (ma) mapping; and water use efficiency monitoring/mapping. This report focuses on satellite remote sensing for broad-scale rice-based irrigation agricultural applications. It also discusses related regional GIS analyses that may or may not include remote sensing data, and briefly addresses other sources of finer-scale remote sensing and geospatial data as they relate to agriculture. Since a complete review of the remote sensing research was not provided in the rice literature alone, some generic agricultural issues have been learned from applications not specifically dealing with rice. Remote sensing specialists may wish to skip to section 2

Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences

The aim of the Special Issue “Hyperspectral Imaging for Fine to Medium Scale Applications in Environmental Sciences” was to present a selection of innovative studies using hyperspectral imaging (HSI) in different thematic fields. This intention reflects the technical developments in the last three decades, which have brought the capacity of HSI to provide spectrally, spatially and temporally detailed data, favoured by e.g., hyperspectral snapshot technologies, miniaturized hyperspectral sensors and hyperspectral microscopy imaging. The present book comprises a suite of papers in various fields of environmental sciences—geology/mineral exploration, digital soil mapping, mapping and characterization of vegetation, and sensing of water bodies (including under-ice and underwater applications). In addition, there are two rather methodically/technically-oriented contributions dealing with the optimized processing of UAV data and on the design and test of a multi-channel optical receiver for ground-based applications. All in all, this compilation documents that HSI is a multi-faceted research topic and will remain so in the future

Grains Research and Development Science Highlights 2015-17

Western Australian grain production and industry value has quadrupled over the past 30 years, despite declining winter rainfall, more frost and high temperature events, acidifying soils and increasing input costs. Strong evidence links this productivity growth to R&D that has delivered genetically superior varieties, better agronomic practices and more reliable farming systems. Western Australian grain growers are innovators that rapidly adopt new technology which is increasingly sourced from a wider pool of national and global science, research and innovation. Continuing to push the productivity frontier is not only critical to grower’s profitability, it underpins the international competitiveness of our exports and value-adding opportunities for the Western Australian economy. DAFWA’s Grains R&D team aims to access and evaluate the most relevant new products and technologies under Western Australian grain growing conditions and to integrate the findings to support the rapid and appropriate adoption by Western Australian grain growing businesses

Satellite and UAV Platforms, Remote Sensing for Geographic Information Systems

The present book contains ten articles illustrating the different possible uses of UAVs and satellite remotely sensed data integration in Geographical Information Systems to model and predict changes in both the natural and the human environment. It illustrates the powerful instruments given by modern geo-statistical methods, modeling, and visualization techniques. These methods are applied to Arctic, tropical and mid-latitude environments, agriculture, forest, wetlands, and aquatic environments, as well as further engineering-related problems. The present Special Issue gives a balanced view of the present state of the field of geoinformatics

Annual variation of bare arable soil areas on a global scale

Wydział Nauk Geograficznych i GeologicznychArable land around the world has a 12% share of the global land area. This thesis was created as a part of a project aimed at estimation of shortwave radiation reflected from those surfaces according to various scenarios based on the farming methods. This thesis was created as a part of a project aimed at estimation of shortwave radiation reflected from those surfaces according to various scenarios based on the farming methods. Its key element was the estimation of the bare soil area, defined for its spectral properties, as the area of arable land not covered by vegetation on more than 15% on its surface. In conventional agriculture, during the period immediately following the planting of crops, the soil stays bare until the newly planted crops reach defined above share of surface cover. This work focuses on estimating the periods of bare soil that occur after the planting of 13 major crops at the global scale; those selected crops are wheat, maize, barley, sorghum, soybeans, millet, cotton, rapeseed, groundnuts, potato, cassava, rye, and sugar beet. The supplementary objective of the study was to determine which soil groupings, and in what proportions, were bare during those periods. Arable land, divided into extensive agricultural regions located on six continents, was analyzed. The estimation of bare soil acreage was performed based on publicly available spatial datasets including the distribution of arable land in the world, crop calendars containing planting dates and the geographic distribution of crops. The arable land in the world was first divided into agricultural regions inspired by the division proposed by United States Department of Agriculture. For each region, average daily temperatures were used to predict plant growth stages. For each crop within a region, the planting date was used as the beginning of the bare soil period, which ended when it reached a stage where at least 15% of the surface was covered by vegetation. The aggregated periods concerning every crop within any given region resulted in an annual variation of bare soil area. The acreages of soil grouping used in agriculture for any region were then extracted based on the location of arable land and the region’s boundaries.The global annual variation of bare soil area shows that the maximum level occurs around the 140th day of the year (DOY) (middle of May), influenced primarily by the planting of crops occurring in the northern hemisphere. Up to 1.5 million km2of soil surface stays bare at that time. Centered on that maximum is a period of bare soil lasting for almost four months, between the 92nd DOY and the 200th DOY (early April and end of July), when two lesser maxima were observed, of around 900,000 and 700,000 km2, respectively. The equivalent of that period, resulting from planting in the southern hemisphere, starts around the 330th DOY (middle of November) and lasts for about a month, reaching almost 400,000 km2. The other distinguishable episode of bare soil in the southern hemisphere was noted between the 15th and the 25th DOY (second half of January) when its area reached 100,000 km2. Asia is the super region with by far the largest area of arable land and consequently, it sports the highest acreage of bare soil. During the aforementioned maximum in the northern hemisphere occurring around the 140th DOY, the Asian super region contributes around 700,000 km2 of bare soil, which is almost half of the bare soil area for the whole northern hemisphere at that time, with Lithosols, Cambisols, and Gleysols being the major soil groupings that stay bare. In Europe, two distinct periods of bare soil were found; during the first, starting around the 40th DOY (middle of February) and lasting until the 150th DOY (end of May), the steady increase of the bare soil area lasts until the 140th DOY (middle of May) when it reaches almost 500,000 km2, after which a rapid decline was observed. The second, manifesting two and a half months later, lasts between around the 230th and the 290th DOY (middle of August to middle of October), and exceeds 100,000 km2. Chernozems, Cambisols, and Luvisols are dominant soil groupings on arable land in Europe. Similar trends, related to the European bare soil areas, were found in the North American super region, where a period of maximum bare soil area occurs in late spring, and a second period, characterized by a much smaller area, follows the main one three months later. The maxima coincide with the aforementioned ones in Asia and Europe, reaching 300,000 km2 of bare soil around the 140th DOY. Similar to Europe, the second period sports a much smaller bare soil area, short of 30,000 km2. The dominant soil groupings in agricultural use in North America are Kastanozems, Luvisols, and Chernozems. Africa is a super region whose area is divided between both northern and southern hemispheres, which shows in the annual variation of its bare soil area. Three distinct periods were found there, the major one around the middle of a year lasted for about two and a half months, between the 167th and the 230th DOY (middle of June to middle of August) with the bare soil area being up to almost 400,000 km2. The other peak occurs about a month and a half earlier, between the 95th and the 115th DOY (roughly the month of April) and is characterized by a bare soil area exceeding 120,000 km2. The last notable episode of bare soil in Africa manifests itself between the 317th DOY and the 10th day of the following year (middle of November to the middle of January), with the area of soil uncovered by vegetation reaching almost 100,000 km2. Luvisols together with Arenosols, followed by Vertisols, are the most extensively farmed soil groupings in Africa. The majority of arable land in the southern hemisphere is found in the South American super region, which is reflected in the annual variation of bare soil area, which is similar to that of the whole southern hemisphere. The maximum lasts for around two weeks, between the 330th and the 345th DOY (end of November to the middle of December), when almost 500,000 km2 of arable soil is bare. A secondary peak was observed between the 15th and the 30th DOY (second half of January), sporting around 100,000 km2 of bare soil area. Ferrasols is the most commonly farmed soil grouping in the region, followed by Phaozems and Luvisols. In Oceania, the maximum area of bare soil slightly exceeds 25,000 km2 for about two weeks in the first half of June, followed by a rapid decline. A secondary period is characterized by a longer duration but the smaller area, lasting between the 313th and the 14th DOY (middle of November to middle of January) with about 5,000 km2 of arable land which is not covered by vegetation at that time. Luvisols are the dominant soil grouping under cultivation in Oceania, followed by Planosols, Solonetz, and Vertisols. The obtained variations of bare soil areas together with the corresponding share of soil groupings for all regions were used in other work in order to estimate the amount of shortwave radiation reflected from those surfaces according to various scenarios based on the farming methods.Grunty orne stanowią około 12% powierzchni lądów na całym świecie. Niniejsza praca powstała w ramach projektu dążącego do oszacowania ilości promieniowania krótkofalowego odbijanego od tych powierzchni. Kluczowym jej elementem było oszacowanie areału odkrytej gleby, definiowanej ze względu na jej właściwości spektralne, jako powierzchni gruntów ornych niepokrytych roślinnością w stopniu większym niż 15%. W przypadku rolnictwa konwencjonalnego, w okresie bezpośrednio po sianiu lub sadzeniu roślin gleba pozostaje odkryta, dopóki nowo zasiane lub zasadzone rośliny nie osiągną fazy wzrostu powodującej pokrycie powierzchni w wyżej zdefiniowanym stopniu. Praca ta koncentruje się na oszacowaniu okresów kiedy gleba pozostaje odkryta, które występują po sianiu lub sadzeniu 13 głównych upraw w skali globalnej; te wybrane uprawy to pszenica, kukurydza, jęczmień, sorgo, soja, proso, bawełna, rzepak, orzeszki ziemne, ziemniaki, maniok, żyto i burak cukrowy. Celem badania było ustalenie, które główne grupy glebowe (major soil groupings wg definicji FAO–UNESCO) oraz w jakich areałach pozostają odkryte. Przeanalizowane zostały grunty orne podzielone na regiony rolnicze położone na sześciu kontynentach. Oszacowanie areału odkrytej gleby przeprowadzono przy użyciu publicznie dostępnych zbiorów danych przestrzennych, w tym rozmieszczenia gruntów ornych na świecie, geograficznego rozmieszczenia upraw oraz kalendarzy upraw zawierających daty sadzenia. Używane zbiory danych zostały w pierwszej kolejności podzielone na regiony rolnicze zainspirowane podziałem zaproponowanym przez Departament Rolnictwa Stanów Zjednoczonych. Dla każdego z tych regionów zastosowano średnie dzienne temperatury w celu oszacowania etapów wzrostu roślin. Dla każdej uprawy w regionie data sadzenia została wykorzystana jako początek okresu występowania odkrytej gleby, który kończy się, gdy osiągnie etap, w którym gleba zostaje pokryte roślinnością. Zagregowane okresy dotyczące każdej uprawy w danym regionie posłużyły do ustalenia rocznej zmienności powierzchni odkrytej gleby. Areały głównych grup glebowych wykorzystywanych w rolnictwie dla każdego z regionów zostały następnie obliczone na podstawie lokalizacji gruntów ornych i granic regionu. Analizując wszystkie grunty orne na świecie, maksymalny poziom odkrycia występuje około 140 dnia roku (day of year - DOY);(połowa maja), i jest spowodowany przede wszystkim przez sianie oraz sadzenie roślin uprawnych na półkuli północnej. W tym czasie do 1,5 mln km2 powierzchni gruntów ornych nie jest pokryta przez rośliny. Wyżej opisane maksimum występuje podczas okres odsłoniętej gleby trwającego przez prawie cztery miesiące, między 92 DOY a 200 DOY (początek kwietnia a koniec lipca), kiedy zaobserwowano dwa pomniejsze maksima, odpowiednio około 900 000 i 700 000 km2. Odpowiednik tego okresu, wynikający z siania oraz sadzenia na półkuli południowej, zaczyna się około 330 DOY (połowa listopada) i trwa około miesiąca, osiągając prawie 400 000 km2. Inny wyraźnie widoczny okres odkrytej gleby na półkuli południowej odnotowano między 15 a 25 DOY (druga połowa stycznia), kiedy jego powierzchnia osiągnęła 100 000 km2. Azja to kontynent o zdecydowanie największym areale odkrytej gleby wynikający ze zdecydowanie największej powierzchni gruntów ornych. Podczas wspomnianego maksimum na półkuli północnej, występującego około 140 DOY, azjatycki region odpowiada za około 700 000 km2 odkrytej gleby, a więc prawie połowę powierzchni odkrytej gleby dla całej półkuli północnej w tym czasie, z Lithosols, Cambisols i Gleysols jako głównymi grupami gleb, które pozostają odkryte. W Europie znaleziono dwa odrębne okresy odkrytej gleby; podczas pierwszego, rozpoczynającego się około 40 DOY (połowa lutego) i trwającego do 150 DOY (koniec maja), stały wzrost powierzchni odkrytej gleby trwa do 140 DOY (połowa maja), kiedy osiąga ona prawie 500 000 km2, po czym następuje gwałtowny spadek tego areału. Drugi, zaczynający się dwa i pół miesiąca później, trwa od około 230 do 290 DOY (od połowy sierpnia do połowy października) i przekracza 100 000 km2. Chernozems, Cambisols i Luvisols są dominującymi grupami glebowymi na gruntach ornych w Europie. Podobne tendencje jak w przypadku odsłoniętych gleb na kontynencie europejski zanotowano w Ameryce Północnej, w przypadku której okres największej powierzchni odkrytej gleby występuje późną wiosną, a drugi okres, obejmującym znacznie mniejszy areał, następuje trzy miesiące później. Maksymalne wartości występują w podobnym okresie jak w wyżej wymienionych Azji i Europie, osiągając 300 000 km2 odkrytej gleby około 140 DOY. Podobnie jak w Europie, drugi okres charakteryzuje się znacznie mniejszą powierzchnię odkrytej gleby, poniżej 30 000 km2. Dominującymi grupami glebowymi uprawianymi w Ameryki Północnej są Kastanozems, Luvisols i Chernozems. Afryka jest kontynentem zajmującym półkulą północną, jak i południową, co jest odzwierciedlone w rocznym przebiegu areału odkrytej gleby. Wyróżniono tam trzy osobne okresy, największy z nich występuje w połowie roku i trwa około dwóch i pół miesiąca, między 167 a 230 DOY (od połowy czerwca do połowy sierpnia), podczas którego powierzchnia odkrytej gleby osiąga prawie 400 000 km2. Drugi szczyt występuje około półtora miesiąca wcześniej, między 95 a 115 DOY (w kwietniu) i charakteryzuje się areałem odkrytej gleby przekraczającym 120 000 km2. Ostatni znaczący okresy odkrytej gleby w Afryce ustalono między 317 DOY a 10 dniem następnego roku (od połowy listopada do połowy stycznia), przy czym odkryty areał gleby sięga prawie 100 000 km2. Luvisols wraz z Arenosols oraz Vertisols, są najbardziej ekstensywnie uprawianymi grupami glebowymi w Afryce. Roczna zmienność powierzchni odsłoniętej gleby na kontynencie Ameryki Południowej ma podobny przebieg jak w przypadku całej półkuli południowej. Maksimum areału odsłoniętej gleby trwa przez około dwa tygodnie, między 330 a 345 DOY (koniec listopada do połowy grudnia), kiedy prawie 500 000 km2 gruntów ornych pozostaje odkrytych. Drugi szczyt zaobserwowano między 15 a 30 DOY (druga połowa stycznia), w którego trakcie około 100 000 km2 gruntów ornych jest odsłoniętych. Ferrasols są najczęściej uprawianą grupą glebową na kontynencie, a następnie Phaozems i Luvisols. W Oceanii maksymalny areał odkrytej gleby nieznacznie przekracza 25 000 km2 przez okres około dwóch tygodni w pierwszej połowie czerwca, po czym następuje jego gwałtowny spadek. Drugi okres charakteryzuje się dłuższym czasem trwania, ale mniejszym areałem, utrzymującym się od 313 do 14 DOY (od połowy listopada do połowy stycznia) z około 5000 km2 gruntów ornych, które nie są w tym czasie pokryte roślinnością. Luvisols są dominującą grupą glebową pod uprawą w Oceanii, a następnie Planosols, Solonetz i Vertisols.project 2014/13/B/ST10/02111, financed by the Polish National Science Cente