Dissertations / Theses on the topic 'Plant reflectance spectra'

The thesis presents an investigation of the potential of measuring plant condition from hyperspectral reflectance data. To do this, some linear methods for embedding the high dimensional hyperspectral data and to perform regression to a plant condition space have been compared. A preprocessing step that aims at normalized illumination intensity in the hyperspectral images has been conducted and some different methods for this purpose have also been compared.A large scale experiment has been conducted where tobacco plants have been grown and treated differently with respect to watering and nutrition. The treatment of the plants has served as ground truth for the plant condition. Four sets of plants have been grown one week apart and the plants have been measured at different ages up to the age of about five weeks. The thesis concludes that there is a relationship between plant treatment and their leaves' spectral reflectance, but the treatment has to be somewhat extreme for enabling a useful treatment approximation from the spectrum. CCA has been the proposed method for calculation of the hyperspectral basis that is used to embed the hyperspectral data to the plant condition (treatment) space. A preprocessing method that uses a weighted normalization of the spectrums for illumination intensity normalization is concluded to be the most powerful of the compared methods.

This research investigates the spectral reflectance characteristics of soil and vegetation using multi-angular and single view hyperspectral data. The question of the thesis is ???How much information can be obtained from multi-angular hyperspectral remote sensing in comparison with single view angle hyperspectral remote sensing of soil and vegetation???? This question is addressed by analysing multi-angular and single view angle hyperspectral remote sensing using data from the field, airborne and space borne hyperspectral sensors. Spectral mapping, spectral indices and Functional Data Analysis (FDA) are used to analyse the data. Spectral mapping has been successfully used to distinguish features of soil and cotton with hyperspectral data. Traditionally, spectral mapping is based on collecting endmembers of pure pixels and using these as training areas for supervised classification. There are, however, limitations in the use of these algorithms when applied to multi-angular images, as the reflectance of a single ground unit will differ at each angle. Classifications using six-class endmembers identified using single angle imagery were assessed using multi-angular Compact High Resolution Imaging Spectrometer (CHRIS) imagery, as well as a set of vegetation indices. The results showed no significant difference between the angles. Low nutrient content in the soil produced lower vegetation index values, and more nutrients increased the index values. This research introduces FDA as an image processing tool for multi-angular hyperspectral imagery of soil and cotton, using basis functions for functional principal component analysis (fPCA) and functional linear modelling. FDA has advantages over conventional statistical analysis because it does not assume the errors in the data are independent and uncorrelated. Investigations showed that B-splines with 20-basis functions was the best fit for multi-angular soil spectra collected using the spectroradiometer and the satellite mounted CHRIS. Cotton spectra collected from greenhouse plants using a spectrodiometer needed 30-basis functions to fit the model, while 20-basis functions were sufficient for cotton spectra extracted from CHRIS. Functional principal component analysis (fPCA) of multi-angular soil spectra show the first fPCA explained a minimum of 92.5% of the variance of field soil spectra for different azimuth and zenith angles and 93.2% from CHRIS for the same target. For cotton, more than 93.6% of greenhouse trial and 70.6% from the CHRIS data were explained by the first fPCA. Conventional analysis of multi-angular hyperspectral data showed significant differences exist between soil spectra acquired at different azimuth and zenith angles. Forward scan direction of zenith angle provides higher spectral reflectance than backward direction. However, most multi-angular hyperspectral data analysed as functional data show no significant difference from nadir, except for small parts of the wavelength of cotton spectra using CHRIS. There is also no significant difference for soil spectra analysed as functional data collected from the field, although there was some difference for soil spectra extracted from CHRIS. Overall, the results indicate that multi-angular hyperspectral data provides only a very small amount of additional information when used for conventional analyses.

Thesis (Ph. D.)--Ohio State University, 2003.
Title from first page of PDF file. Document formatted into pages; contains xx, 245 p.; also includes graphics. Includes bibliographical references (p. 206-216).

碩士
國立中興大學
生命科學系
92
In order to understand the chlorophyll fluorescence and leaf reflectance spectral characteristics among species, 12 species with different elevation distribution and temperature adaptation were used. The experiments were made in the campus of National Chung Hsing University (78 m), Hui-Sun Forest Station (800 m), and Tatachia area (2600 m). The results indicated that the PSII efficiency estimated from chlorophyll fluorescence parameters of Pinus taiwanensis (conifer) was lesser influenced by the low temperature and high illumination than those of 2 Miscanthus (C4) species when they were measured in Tatachia. Among 2 Miscanthus species, low elevation origin M. floridulus was more influenced by low temperature than that of high elevation origin M. transomrrisonensis. In Tatachia, transplanted M. floridulus showed lower photochemical reflectance index (PRI) calculated from leaf reflectance spectra in the winter, indicating it required higher xanthophyll cycle to dissipate more excess absorbed energy due to PSII efficiency were more inhibited by low temperature. It also found that no difference of potential of PSII efficiency (Fv/Fm) between flatland and crest line grew P. taiwanensis in Hui-Sun Forest Station. However the PRI of crest line grew P. taiwanensis was lower than that of flatland grown in dry season, probably due to the difference of water condition between 2 habitats. It showed positive correlation between photosynthesis capacity (Pn) and electron transport rate (ETR) for C4 species. This regression coefficient was higher in the species with higher photosynthetic capacity, and no significant correlation could be found in Miscanthus, which showed the lowest Pn among 5 tested C4 species. When merged together of 5 C4 species to statistic analysis, the leaf with higher photosynthetic capacity showed higher portion of absorbed light energy for photochemical (P), and low portion for non-photochemical (D) dissipations. The slope between Pn and P, as well as Pn and D were decreasing with PAR increased. However, the portion of excess energy was not influenced by PAR. From November to December, which daily minimum temperature ranging from 11.6oC to 22.4oC, predawn Fv/Fm of mango (Mangifera indica, cv. Aiwen) and Podocarpus nagi decrease with low temperature, and mango was more influenced than P. nagi. On the contrary, predawn Fv/Fm of Taiwan alder (Alnus formosana) was lesser influenced by temperature. Nevertheless, predawn Fv/Fm showed a strong significant correlation with predawn PRI (PRIp) for statistical analysis when merged together of 3 species. Therefore PRIp could be used as an indicator to estimate the seasonal variation of the potential photochemical efficiency of PSII. Both Fv/Fm and Ф (actual PSII efficiency) showed significant curvilinear correlation with PRI (PRIn) when 3 species were merged together for statistical analysis which data measured at noon. However, more strong correlation between Fv/Fm and ΔPRI (PRIp - PRIn) as well as between Ф and ΔPRI were found. In addition, non photochemical quenching (NPQ) did not correlated with PRIn, but significant correlated with ΔPRI. Thus ΔPRI is suit to indicate the actual dissipation of the excess energy as well as PSII efficiency during illumination. As a conclusion, both chlorophyll fluorescence parameters and leaf reflectance spectra indexes are powerful tools for ecophysiological study.

Dissertação de Mestrado em Gestão dos Recursos Naturais
O solo e a sua biodiversidade são o motor de todos os sistemas de produção terrestres e serviços de ecossistemas. A mudança na produção agrícola de extensiva para intensiva tem um efeito negativo profundo nos solos e na sua biodiversidade. A perda de biodiversidade resulta em comunidades do solo menos complexas. Com o aumento da procura dos serviços dos ecossistemas, como o solo, existe a necessidade da melhoria da gestão dos solos e das produções agrícolas. Esta melhoria pode trazer vantagens a nível do desenvolvimento de práticas mais sustentáveis que contribuem para um desenvolvimento económico sustentável. A gestão sustentável de qualquer ecossistema requer, entre outras informações, uma compreensão completa da interação solo-planta para tentar descrever padrões naturais. As plantas produzem substância orgânica através da fotossíntese. A fotosíntese depende da absorção de luz pelos pigmentos fotossintéticos presentes na folha. Portanto, as propriedades óticas da folha são influenciadas pela concentração dos pigmentos fotossintéticos e metabolitos, do seu teor em água e da estrutura e anatomia da folha. A reflectância hiperspectral tem ganho importância comercial e científica, contudo, permanece ainda subdesenvolvida apesar do seu potencial. A deteção remota da vegetação é uma boa ferramenta, pois pode extrapolar escalas de tempo, e é cada vez mais utilizada para perceber interações planta-solo. Sabe-se que o sinal de reflectância é sensível a mudanças abióticas e bióticas, mas ainda há um longo caminho a percorrer. Consequentemente, foi realizado durante nove semanas um bioensaio com duas culturas diferentes, a Beterraba (Beta vulgaris) e o Milho (Zea mays). Foram plantadas em três diferentes tipos de gestão do solo e aplicados seis tipos de tratamentos. Os tratamentos aplicados foram: o fungo Rhizoctonia solani, o nemátode Pratylenchus penetrans, a radiação gama, os nutrientes, e o fungo R. solani com nutrientes e um controlo. Os tipos de gestão que foram aplicados foram o solo Biológico, os fertilizantes artificiais e Fertelizantes orgânicos. No total foram consideradas 3650 plantas. Os dados da refletância espetral foram obtidos com um espectrómetro de campo ASD plant-probe e clip-foliar. Um objetivo deste estudo consistiu em monitorizar a refletância espetral das folhas das duas espécies durante o período experimental. Os dados espetrais foram analisados utilizando índices de vegetação. Os efeitos do biota do solo foram analizados numa análise multivariada ANOVA com os fatores, espécie de planta, tipos de solo e tratamentos. A biomassa total de patogénicos tende a aumentar quanto mais intensiva for a prática agricola. No solo Biológico observou-se uma intensificação da cor verde da planta nas duas espécies, com o aumento da disponibilidade de nutrientes. A adição de fertelizante pode ter influenciado a resistência das plantas às doenças do solo. A menor biomassa foi encontrada no tratamento com radiação gama (estéril), sugerindo que o biota do solo influenciou o desempenho da planta. O melhor tipo de gestão do solo teve um efeito positivo no crescimento das plantas. As melhores práticas agricolas permitem uma supressão das doenças inoculadas. Foi demonstrado que o espetro da planta difere quando é induzido stresse e também consoante o tipo de gestão do solo. A melhor gestão agrícola foi considerada a Biológica.
Soil and soil biodiversity are the driving force of all the terrestrial production systems and ecosystem services. The intensification of agriculture production and shifts from extensive crop rotation have, regularly, a profound negative effect on soils and their biodiversity. Biodiversity losses result in less complex soil communities. The increasing demand of soil ecosystem implies the improvement of soil and crop management, and it’s a key opportunity for supporting sustainable economic development. The sustainable management of any ecosystem requires, amongst other information, a thorough understanding of plant-soil feedback attempting to describe natural patterns and relations between the plants and their environment. Plants produce organic substances by photosynthesis. Photosynthesis depends upon the absorption of light by pigments, as chlorophyll-a among other accessory pigments, in the leaves of the plants. Therefore, leaf optical properties are influenced by the concentration of the photosynthetic pigments, metabolites, water content, leaf structure and leaf anatomy. Hyperspectral reflectance in remote sensing has gained scientific and commercial importance but still remains underdeveloped despite its potential. Vegetation remote sensing is a great tool, as it can extrapolate to synoptic scales and time sequences can be acquired. It is increasingly used for measurements of agricultural crop condition and also for plant-soil interactions. It is known that reflectance signal is sensitive to abiotic changes, but concerning biotic changes, there are still several limitations. Therefore, was conducted a 9 weeks greenhouse bioassay with two different crops, Sugar beet (Beta vulgaris) and Corn (Zea mays), three different types of soil management and six different treatments were applied. The treatments applied were: the fungus Rhizoctonia solani, the nematode Pratylenchus penetrans, the Gamma radiation, the nutrients, the R. solani with nutrients and a control treatment. The types of management were the Biologic, the Artificial Fertilizer and the Manure. In total were 3650 plants. Spectral reflectance data were collected with an ASD Fieldspec 3 spectrometer with an ASD plant-probe and leaf-clip device attached. One of the objectives of the measurement was to monitor the differences between leaf reflectance over time. The spectral data was analyzed using vegetation indices. The effects of soil biota were analyzed in a multivariate ANOVA analysis with plant species, soil regime and soil treatment. The total biomass of the pathogens increase with a more intensive agriculture and shoot biomass in both plant species increased with disposal of the nutrient supply in the Biologic soil. The application of manure compost that is rich in nitrogen may have reduced soil-borne diseases. The lowest biomass was found in the sterilized treatments suggesting that the soil biota has influenced the plant performance. The best soil management had positive effect in growth of the plants. Disease suppression can be influenced by management practices. It was demonstrated that plant spectral signatures changes due induced stress and soil type. The best soil regime overall in this study case was considered the Biologic type.

Potato ground cover and spectral data were measured in the Columbia Basin during the 1990 growing season. Three spectral were correlated with ground cover; normalized difference, near infrared-red ratio, and the first derivative of the spectral curve at 750 nm. All models were statistically significant at the 99% level. Normalized was most correlated followed by the near infrared-red ratio, and the first derivative of the spectral curve at 750 nm.
Graduation date: 1992

The measurement of plant water content is essential to assess stress and disturbance in forest plantations. Traditional techniques to assess plant water content are costly, time consuming and spatially restrictive. Remote sensing techniques offer the alternative of a non destructive and instantaneous method of assessing plant water content over large spatial scales where ground measurements would be impossible on a regular basis. The aim of this research was to assess the relationship between plant water content and reflectance data in Eucalyptus grandis forest stands in KwaZulu-Natal, South Africa. Field reflectance and first derivative reflectance data were correlated with plant water content. The first derivative reflectance performed better than the field reflectance data in estimating plant water content with high correlations in the visible and mid-infrared portions of the electromagnetic spectrum. Several reflectance indices were also tested to evaluate their effectiveness in estimating plant water content and were compared to the red edge position. The red edge position calculated from the first derivative reflectance and from the linear four-point interpolation method performed better than all the water indices tested. It was therefore concluded that the red edge position can be used in association with other water indices as a stable spectral parameter to estimate plant water content on hyperspectral data. The South African satellite SumbandilaSat is due for launch in the near future and it is essential to test the utility of this satellite in estimating plant water content, a study which has not been done before. The field reflectance data from this study was resampled to the SumbandilaSat band settings and was put into a neural network to test its potential in estimating plant water content. The integrated approach involving neural networks and the resampled field spectral data successfully predicted plant water content with a correlation coefficient of 0.74 and a root mean square error (RMSE) of 1.41 on an independent test dataset outperforming the traditional multiple regression method of estimation. The potential of the SumbandilaSat wavebands to estimate plant water content was tested using a sensitivity analysis. The results from the sensitivity analysis indicated that the xanthophyll, blue and near infrared wavebands are the three most important wavebands used by the neural network in estimating plant water content. It was therefore concluded that these three bands of the SumbandilaSat are essential for plant water estimation. In general this study showed the potential of up-scaling field spectral data to the SumbandilaSat, the second South African satellite scheduled for launch in the near future.
Thesis (M.Sc.) - University of KwaZulu-Natal, Pietermaritzburg, 2008.

Ph.D., Faculty of Science, University of the Witwatersrand, 2011
Deep-level gold mining in the Witwatersrand Basin Goldfields (WBG) of central South Africa is characterised by the production of extensive unlined tailings storage facilities (TSFs) comprising large quantities of pulverised rock and water contaminated with salts and a wide range of other inorganic pollutants (Weiersbye et al., 2006). There are more than 200 such TSFs covering a total area of more than 400 km2 (Rosner et al., 2001), and significant contaminated “footprint” areas occur after removal and reprocessing of the original TSFs (Chevrel et al., 2003). It is estimated that the Witwatersrand Basin contains six billion tons of gold and uranium tailings (Chevrel et al., 2003), 430 000 tons of uranium (Council of Geoscience, 1998; Winde, 2004a; b; c) and approximately 30 million tons of sulphur (Witkowski and Weiersbye, 1998a). An estimated 105 million tons of waste per annum is generated by the gold mining industry within the WBG (Department of Tourism, Economic and Environmental Affairs, 2002; Chamber of Mines of South Africa, 2004). A major environmental problem resulting from deep level mining in the WBG is the contaminated water that seeps from TSFs into adjacent lands and groundwater. Van As (1992) reported on the significant environmental hazards resulting from the storage of highly pulverised pyrite rock waste in TSFs (Straker et al., 2007). Adjacent lands become polluted through near-surface seepage, and this is enhanced by the movement of polluted groundwater in shallow aquifers that are commonly 1-30 m below ground (Funke, 1990; Hodgson et al., 2001; Rosner et al., 2001; Naicker et al., 2003). The impact of the mines and the TSFs extends far beyond their localities (Cogho et al., 1990). The Vaal River catchment receives a large proportion of the pollutants from WBG mining activities, with consequent acidification and salinisation of surface and ground waters. Salt discharges to the Vaal River were estimated to be 170 000 t/annum (Best, 1985), whereas discharges from the Free State gold mines south of the Vaal catchment were estimated at 350 000 t/annum of salts (Cogho et al., 1990). Concern also exists over the spread of dangerous contaminants such as uranium, chromium and mercury (Coetzee et al., 2006; Winde, 2009). Engineering solutions to these problems are hindered by the large sizes and great extent of TSFs, the high and indefinite costs involved, and the typically low hydraulic conductivity in affected aquifers, which makes the “pump and treat” option impractical. An alternative phytoremediation strategy is to establish belts or blocks of trees in strategic areas surrounding the TSFs in order to reduce the seepage of contaminated water into adjacent lands and groundwater bodies. The major reasons why trees are likely to have a greater impact on seepage water than the existing grasslands that characterise the area around most TSFs in the WBG, are that some tree species have the potential to develop very deep root systems and to continue transpiring water throughout the year. This is in contrast to seasonally dormant grasslands. In addition, some tree species are known to be tolerant to salts and other pollutants. Trees are thus potentially able to reach deep water tables, take up large quantities of water, and remove some of the pollutants in this water. It is crucial for a successful implementation of this strategy to know on what sites trees are able to access mine seepage water, and consequently maintain a high year-round rate of water use. If this access is limited, then growth and water use will be curtailed during the long winter dry season, and control of seepage will be considerably below potential. A primary aim of this study was to develop methodologies to discriminate between water-stressed and non-water-stressed trees currently growing in three gold mining districts (Welkom, Vaal River, West Wits) within the WBG. This information was required to assess what site types are likely to support adequate tree growth and permit high rates of water use and seepage control. The tree species selected were those most widely occurring in these areas, and include the non-native species Eucalyptus sideroxylon A. Cunningham ex Woolls and Eucalyptus camaldulensis Dehnhardt, as well as the indigenous species Searsia lancea L.f. Various remote sensing technologies including leaf-level spectroscopy, satellite and airborne remote sensing images were evaluated for their usefulness in detecting levels of winter-time water stress. Four commonly used ground-truthing techniques (predawn leaf water potential, leaf chlorophyll fluorescence, leaf chlorophyll and carotenoid pigment content, and leaf water content) were used for localised measurements of plant water stress and for ground-truthing of remotely sensed data on 75 sample sites and 15 sample sites. This study provided a unique opportunity to test and compare the use of stress reflectance models derived from different remote sensing data acquired at different spatial and spectral resolutions (i.e. multispectral and hyperspectral) for the same geographical location. The use of remote sensing to examine the spectral responses of vegetation to plant stress has been widely described in the scientific literature. A collation of published spectral reflectance indices provided the basis for investigating the use of hand-held remote sensing technology to detect plant water stress, and was used as a stepping stone to further develop spectral plant water stress relationships for specific tree species in this study. Seventy seven spectral reflectance indices and specific individual spectral wavelengths useful for detecting plant water stress, plant pigment content, the presence of stress related pigments in vegetation, and changes in leaf cellular structure, were investigated using hand-held spectroscopy. Ground-based measurements of plant water stress were taken on 75 sample trees. In this study, the measurement of predawn leaf water potential has been identified as a key methodology for linking remotely sensed assessments of plant water stress to actual plant water stress; a reading of -0.8 MPa was used to separate stressed trees from unstressed trees in the landscape (Cleary and Zaerr, 1984). The results of the predawn leaf water potential measurements ranged from -0.56 to -0.68 MPa at unstressed sites, and from -0.93 to -1.78 MPa at stressed sites. A novel approach of using spectral reflectance indices derived from previous studies was used to identify specific indices which are applicable to South Africa and to the three species investigated in the WGB. Maximal multiple linear regression models were derived for all possible combinations of plant water stress measurements and the 77 spectral reflectance indices extracted from leaf-level spectral reflectance data, and included the interactions of district and species. The results of the multiple linear regression models indicated that the (695/690) index, DATT index (850-710)/(850-680), near infra-red index (710/760) and the water band (900/970) index performed well and accounted for more than 50% of the variance in the data. The stepwise regression model derived between chlorophyll b content and the DATT index was selected as the “best” model, having the highest adjusted R2 of 69.3%. This was shown to be the most robust model in this application, which could be used at different locations for different species to predict chlorophyll content at the leaf-level. Satellite earth observation data were acquired from two data sources for this investigation; the Hyperion hyperspectral sensor (United States Geological Survey Earth Resources Observation Systems) and the Proba Chris pseudo-hyperspectral sensor (European Space Agency). The Hyperion sensor was selected to obtain high spatial and spectral resolution data, whereas the Proba Chris sensor provided high spatial and medium spectral resolution earth observation data. Twelve vegetation indices designed to capture changes in canopy water status, plant pigment content and changes in plant cellular structure, were selected and derived from the satellite remote sensing imagery. Ground-based measurements of plant water stress undertaken during late July 2004 were used for ground-truthing the Hyperion image, while measurements undertaken during July 2005 and August 2005 were used for ground-truthing the Proba Chris images. Predawn leaf water potential measurements undertaken for the three species, ranged from -0.42 to -0.78 MPa at unstressed sites, and -0.95 to -4.66 MPa at stressed sites. Predawn leaf water potentials measured for E. camaldulensis trees sampled in species trials in Vaal River were significantly different between stressed and non stressed trees (t = 3.39, 8df, P = 0.009). In contrast, E. camaldulensis trees sampled near a pan within the Welkom mining district, which had greater access to water but were exposed to higher concentrations of salts and inorganic contaminants, displayed differences in total chlorophyll content (t = -2.20, 8df, P = 0.059), carotenoid content (t = -5.68, 8df, P < 0.001) and predawn leaf water potential (t = 4.25, 8df, P = 0.011) when compared to trees sampled on farmland. E. sideroxylon trees sampled close to a farm dam in the West Wits mining district displayed differences in predawn leaf water potential (t = 69.32, 8df, P < 0.001) and carotenoid content (t = -2.13, 8df, P = 0.066) when compared to stressed trees further upslope away from the water source. Multiple linear regressions revealed that the predawn leaf water potential greenness normalised difference vegetation index model, and the predawn leaf water potential water band index model were the “best” surrogate measures of plant water stress when using broad band multispectral satellite and narrow-band hyperspectral satellite data respectively. It was concluded from these investigations that vegetation indices designed to capture changes in plant water content/plant water status and spectral changes in the red edge region of the spectrum, performed well when applied to high spectral resolution remote sensing data. The greenness normalised difference vegetation index was considered to be a fairly robust index, which was highly correlated to chlorophyll fluorescence and predawn leaf water potential. It is recommended that this index has the potential to be used to map spatial patterns of winter-time plant stress for different genera/species and in different geographical locations. Airborne remote sensing surveys were conducted to investigate the application of high spatial resolution remote sensing data to detect plant water stress. Multispectral airborne imagery was acquired by Land Resource International (PTY) Ltd, South Africa. Ground-based measurements of plant water stress were carried out during July and August 2005.Four individual spectral bands and two vegetation spectral reflectance indices, which are sensitive to changes in plant pigment content, were derived from the processed multispectral images viz. red, green, blue and near-infrared spectral bands and the normalised difference vegetation index (NDVI) and greenness normalised difference vegetation index (GNDVI).The results of the multispectral airborne study revealed that carotenoid content together with the green spectral waveband resulted in the “best” surrogate measure of plant water stress when using broad-band multispectral airborne data. Airborne remote sensing surveys were conducted by Bar-Kal Systems Engineering Ltd, Israel, to investigate the application of hyperspectral airborne imagery to detect plant water stress. Six vegetation spectral reflectance indices designed to capture changes in plant pigment and plant water status/content, were derived from the processed hyperspectral images. When using airborne hyperspectral data, predawn leaf water potential with the normalized difference water index was selected as the most appropriate model. It was concluded, upon evaluation of the multiple linear regression models, that the airborne hyperspectral data produced several more regression models with higher adjusted R2 values (Ra2 range 6.2 - 76.2%) when compared to the airborne multispectral data (Ra2 range 6 - 50.1). Exploration of relationships between vegetation indices derived from leaf-level, satellite and airborne spectral reflectance data and ground-based measurements used as “surrogate” measures of plant water stress, revealed that several prominent and recurring spectral reflectance indices could be applied to identify species-specific plant water stress within the Welkom, Vaal River and West Wits mining districts. The models recommended for mapping and detecting spatial patterns of plant water stress when using different sources of remote sensing data are as follows: the chlorophyll b DATT spectral reflectance model when derived from leaf-level spectral reflectance data, can be applied across all three mining districts the predawn leaf water potential GNDVI spectral reflectance model and predawn leaf water potential water band index spectral reflectance model when utilising satellite multispectral and hyperspectral remote sensing data carotenoid content green band spectral reflectance model can be used for airborne multispectral resolution data predawn leaf water potential NDVI spectral reflectance model is best suited for airborne high spatial and hyperspectral resolution data. These results indicate that measurements of predawn leaf water potential and plant pigment content have been identified as key methodologies for ground-truthing of remotely sensed data and can be used as surrogate measures of plant water stress. Some preliminary research was undertaken to evaluate if wood anatomy characteristics could be used as a non-destructive and rapid low-cost survey approach for identifying trees which are experiencing long-term plant stress. Seventy two wood core samples were extracted and analysed. Predawn leaf water potential measurements were used to classify stressed and unstressed trees. Relative differences in radial vessel diameter, vessel frequency and wood density were examined. Comparison of the radial vessel diameter and vessel frequency measurements revealed significant differences in three of the five comparative sampling sites (p <0.05). The results of the density analyses were significantly different for all five comparative sampling sites (p < 0.01). In general, trees experiencing higher plant water stress displayed smaller vessel diameters, compared to less stressed or healthy trees. Sites which were influenced by high levels of contaminated water also displayed smaller vessel diameters, indicating that the uptake of contaminants could affect the wood anatomy of plants. Trees considered to be experiencing higher plant water stress displayed higher vessel frequency. This preliminary study showed that plant stress does influence the wood anatomical characteristics (radial vessel diameter, vessel frequency and wood density) in E. camaldulensis, E. sideroxylon and S. lancea in the three mining districts. Spatial patterns of trees, mapped in the three gold mining districts, Welkom (27º57´S, 26º34´E) in the Free State Province, Vaal River (26º55´S, 26º40´E) located in the North West Province, and West Wits (26º25´S, 27º21´E) located in Gauteng, which were not experiencing winter-time water stress were correlated to site characteristics such as average soil depth, percent clay in the topsoil, groundwater chloride and sulphate concentrations, total dissolved solids, electrical conductivity and groundwater water level. The spectral reflectance model derived between predawn leaf water potential and the green normalised difference vegetation index using broad-band multispectral Proba Chris satellite data was used to map spatial patterns of unstressed trees across the three mining districts. Very high resolution (75 cm) multispectral airborne images acquired by LRI in 2005 were used to demarcate and classify vegetation using the maximum likelihood supervised classification technique. Interpolated surfaces of groundwater chloride and sulphate concentrations, total dissolved solids, electrical conductivity, pH and groundwater table levels were created using the kriging geostatistical interpolation technique for each mining district. Random sample analyses between stressed and unstressed trees were extracted in order to determine whether site characteristics were significantly different (using t-tests). Site characteristic surfaces which were significantly different from stressed areas were spatially linked to trees which were not experiencing winter-time plant water stress for each tree species investigated in each mining district. This spatial correlation was used to make recommendations and prioritise sites for the establishment of future block plantings. Analysis of the site characteristic data and the geophysical surveys undertaken in the three mining districts which provided detailed information on groundwater saturation and an indication of the salinity conditions, confirmed the presence of relatively shallow and saline groundwater sources. This would imply that tree roots could access the relatively shallow groundwater even during the dry winter season and assist in containing contaminated groundwater seeping into surrounding lands. Keywords : airborne imagery, ground-based measurements of plant water stress, hyperspectral, leaf-level spectroscopy, multispectral, satellite imagery, spatial patterns of unstressed trees, spectral reflectance indices

Badanie roślinności, w tym monitoring pokrywy roślinnej, jest istotnym problemem badawczym w skali globalnej, regionalnej oraz lokalnej. W badaniu roślinności duże znaczenie ma pozyskiwanie parametrów biofizycznych roślin, co pozwala na określenie stanu roślin, a także prognozowanie plonów. Jednocześnie dane teledetekcyjne umożliwiają wiarygodne pozyskiwanie informacji na dużym terenie w znacznie krótszym czasie, niż w przypadku tradycyjnych metod badawczych. W monitoringu roślinności z wykorzystaniem danych teledetekcyjnych stosowane są dwie metody: statystyczna i modelowanie z wykorzystaniem modeli transferu promieniowania (RTM). Podstawowym celem niniejszych badań jest ocena skuteczności modeli transferu promieniowania do określania stanu szaty roślinnej łąk występujących na terenie Polski. W badaniach przetestowano dwa modele transferu promieniowania służące do modelowania odbicia promieniowania od pojedynczych liści i szaty roślinnej traktowanej jako jednolita pokrywa roślinna, z punktu widzenia poprawności symulowania krzywych odbicia spektralnego. Badania odbywały się na obszarach łąkowych, które, ze względu na duże zróżnicowanie wewnętrzne, są trudne do modelowania. Poza tym zbadano i oceniono istotność czynników wpływających na skuteczność symulowania odbicia spektralnego od zróżnicowanej roślinności łąkowej. Podjęto także próbę oceny przydatności danych symulowanych do obliczania teledetekcyjnych wskaźników roślinności i relacji tak obliczonych wskaźników do ich wartości uzyskanych na podstawie danych z bezpośrednich pomiarów terenowych. Badania miały za zadanie określić, jak dalece modele transferu promieniowania mogą być wykorzystane do pozyskiwania danych o cechach roślinności łąkowej o zróżnicowanej strukturze, występujących na terenie Polski. Modele transferu promieniowania (Radiative Transfer Model – RTM) są modelami fizyczno-matematycznymi, które na podstawie praw fizycznych opisują, co dzieje się z promieniowaniem w atmosferze i kontakcie z pokrywą roślinną. Modele są używane od kilkudziesięciu lat do symulowania odbicia od pokrywy roślinnej, a po wykonaniu inwersji modelu, do badania parametrów biofizycznych. Danymi wejściowymi są zmienne biofizyczne i biometryczne roślin, np. zawartość barwników, LAI, zawartość wody, masy suchej, natomiast wyjściowymi – krzywe odbicia spektralnego. W modelowaniu z użyciem RTM zbiorowiska roślinne lub pojedyncze rośliny opisane są jako homogeniczne warstwy. Modele RTM mogą być używane do symulowania pomiarów spektrometrycznych, a także, po przeprowadzeniu inwersji, do pozyskiwania parametrów biofizycznych z krzywych odbicia spektralnego. Do modelowania odbicia od liści używany jest często model PROSPECT, który został zastosowany w niniejszym badaniach (Jacquemoud i in., 1996). Na ogół jest używany do jednolitych powierzchni, takich jak zboża. Wśród modeli opisujących pokrywę roślinności najbardziej popularny jest SAIL i jego modyfikacje. Model SAIL (Scattering by Arbitrarily Inclined Leaves) jest jednowymiarowy, pod względem obliczeniowym mało wymagający, z niewielką liczbą parametrów wejściowych (Verhoef, 1984; 1985). Na ogół modele opisujące odbicie na poziomie pokrywy roślinnej łączone są z modelami opisującymi pojedyncze liście. Jednym z najczęstszych połączeń jest kombinacja modelu PROSPECT z SAIL nazywana PROSAIL (Jacquemoud i in., 2009). Dotychczas na terenie Polski modele transferu promieniowania nie były wykorzystywane do badań roślinności łąkowej. Na ogół modele RTM są używane do badania roślinności homogenicznej ze względu na konieczność prostego opisania roślinności. Znaczna część badań z użyciem modeli RTM wykorzystuje jedynie podstawowe badania terenowe wykonywane w trakcie zobrazowania. Użycie do niniejszych badań wyłącznie pomiarów terenowych powoduje bardziej wiarygodną ocenę skuteczności modeli. Zastosowanie w pracy dwóch różnych modeli pozwoliło na określenie ich skuteczności w zależności od poziomu (modelowanie odbicia od liści i od pokrywy roślinnej). Badania zostały przeprowadzone na zróżnicowanych terenach łąkowych o niejednolitej strukturze. Ponadto, żeby obiektywnie ocenić poprawność działania modeli znaczna większość danych wejściowych została obliczona z pomiarów terenowych. Badania przeprowadzono na dwóch obszarach: na obszarze Pogórza Karpackiego (Pogórze Gorlickie) oraz na terenie Równin Środkowopolskich: Mazowsza Północnego w mikroregionie Ziemi Zakroczymsko-Serockiej; Doliny Środkowej Wisły w mikroregionach: Nadzalewowych Tarasów Dęblińsko-Markoskich i Rynny Karczewskiej oraz Mazowsza Środkowego w mikroregionie Równiny Mszczonowskiej (Olędzki, 2007). Przedmiotem badań są zbiorowiska trawiaste – ekstensywnie użytkowane łąki. Łącznie wykonano pomiary na 57 poligonach pomiarowych. W zależności od sposobu użytkowania, a tym samym struktury, wyróżniono łąki uprawiane o dużej biomasie, uprawiane o zredukowanej biomasie (skoszone) oraz nieuprawiane. W modelowaniu odbicia promieniowania od roślinności na poziomie pojedynczych liści wykorzystano model PROSPECT-5. Parametry wejściowe to: parametr opisujący strukturę liści – zwartość warstw liści, zawartość chlorofilu oraz karotenoidów, zawartość materii suchej i masa wody w liściach (Feret i in., 2008). Pomiary terenowe do modelu PROSPECT odbywały się na Pogórzu Karpackim i na Mazowszu na 22 poligonach badawczych w lipcu 2009 oraz w lipcu i sierpniu 2010 roku. Na podstawie pomiarów terenowych obliczono parametry wejściowe do modelu i utworzono dwa zestawy danych. W pierwszym zestawie (PROSPECT-1) utworzono krzywe odbicia spektralnego na podstawie danych obliczonych bezpośrednio z pomiarów terenowych. Ze względu na duże rozbieżności w wielkości odbicia wygenerowano drugi zestaw krzywych (PROSPECT-2), w którym empirycznie dopasowano parametr określający zawartość chlorofilu, karotenoidów oraz zawartość wody. W modelowaniu odbicia od pokrywy roślinnej zastosowano model SAIL, który ma następujące parametry wejściowe opisujące rośliny i glebę: dane spektrometryczne, dane biofizyczne roślin, zmienna opisującą glebę, informacje o geometrii odbicia promieniowania, a także dodatkowe informacje. Pomiary terenowe do modelu PROSAIL odbywały się wyłącznie na terenie Mazowsza na 50 poligonach pomiarowych w lipcu i sierpniu 2010 roku. Podobnie jak w przypadku PROSPECT opracowano dwa zestawy danych wejściowych do modelu PROSAIL: PROSAIL-1 (wartości bezpośrednio obliczone z pomiarów terenowych) i PROSAIL-2 (empirycznie dopasowano te same parametry, co w przypadku PROSPECT-2). Po połączeniu modelu PROSPECT i SAIL wprowadzono dane wejściowe do modelu i uzyskano krzywe odbicia spektralnego. Ostatnim etapem opracowania była weryfikacja uzyskanych krzywych z dwóch modeli przez porównanie z wartościami odbicia zmierzonymi w terenie. Obliczono pierwiastek błędu średniokwadratowego – Root Mean Square Error (RMSE) w przypadku każdej symulowanej krzywej w zakresie 0,4-2,5 µm oraz w czterech jego fragmentach: 0,4-0,6 µm, 0,4-0,8 µm i zakresów w podczerwieni bliskiej (0,8-1,5 µm) oraz średniej (1,5-2,5 µm). Określono wielkości średniego błędu modelowania w zależności od: rodzaju łąk, ilości biomasy świeżej, wartości parametru LAI oraz procentowej zawartości wody. Określono istotność statystyczną różnic. Policzono wartości teledetekcyjnych wskaźników roślinności na charakterystykach spektralnych pobranych w terenie i modelowanych (na podstawie zestawów danych PROSPECT-2 i PROSAIL-2). Porównując modelowanie wykonane na poziomie liści i na poziomie pokrywy roślinnej z wykorzystaniem dwóch modeli, można stwierdzić, że w całym zakresie widma od 0,4 do 2,5 µm lepsze wyniki uzyskuje się z użyciem modelu PROSPECT. Wartości pierwiastka błędu średniokwadratowego w symulacjach wykonywanych z pierwszym zestawem danych (PROSPECT-1 i PROSAIL-1) wzrastają wraz z długością fali. Największe błędy występują w zakresie środkowej podczerwieni, a mniejsze wartości błędu uzyskiwane są w zakresie bliskiej podczerwieni. Znacznie mniejsze błędy w porównaniu z zakresem podczerwonym, występują w zakresach 0,4-0,6 µm i 0,4-0,8 µm. W przedziale od 0,4 do 0,8 µm stwierdza się najmniejsze różnice w modelowaniu wskaźników z wykorzystaniem z jednej strony zestawów danych niezmodyfikowanych (PROSPECT-1 i PROSAIL-1) i z drugiej – zmodyfikowanych (PROSPECT-2 i PROSAIL-2). W przypadku zestawu danych PROSPECT-2 i PROSAIL-2 największe wartości pierwiastka błędu średniokwadratowego w modelowaniu krzywych odbicia spektralnego występują w zakresie bliskiej podczerwieni (0,8-1,5 µm), a najmniejsze w zakresie 0,4-0,6 µm. Niezależnie od modelu w każdym z zakresów widma, w przypadku stosowania zestawu danych zmodyfikowanych (PROSPECT-2 i PROSAIL-2) wielkość pierwiastka błędu średniokwadratowego jest znacznie mniejsza niż w przypadku danych niezmodyfikowanych (PROSPECT-1 i PROSAIL-1). Zestawiono otrzymane wyniki dla pierwszych zestawów danych (PROSPECT-1 i PROSAIL-1). Przeanalizowano skuteczność modelowania w zależności od rodzaju łąki. W zakresach 0,4-0,6 i 0,4-0,8 µm niezależnie od rodzaju uzyskano zbliżone uśrednione wartości pierwiastka błędu średniokwadratowego. W zakresie podczerwieni najmniejsze błędy występują w charakterystykach spektralnych na terenie łąk nieuprawianych, a największe na obszarach łąk uprawianych o zredukowanej biomasie. Jedynie w modelowaniu z użyciem algorytmu PROSAIL w zakresie środkowej podczerwieni (1,5-2,5 µm) różnice były istotne statystycznie. Stwierdzono, że aspekty środowiskowe mają niewielki wpływ na rezultaty modelowania. Poprawność modelowania zależy także od wartości parametrów biofizycznych. Niezależnie od modelu, stosując zestawy danych niemodyfikowanych, stwierdzono tę samą zależność dotyczącą biomasy świeżej. W przypadku klas łąk o większej zawartości biomasy błąd jest mniejszy. Wspomniane różnice są istotne statystycznie. W zakresie podczerwieni mniejsze błędy występują na terenie łąk o większej wartości wskaźnika powierzchni projekcyjnej liści, ale różnice są istotne statystycznie tylko w przypadku modelowania według algorytmu PROSAIL. W przypadku obu modeli – im większa jest zawartość wody, tym większa wartość błędu. W obu modelach różnice są istotne statystycznie. Oceniając skuteczność modelowania odbicia spektralnego z użyciem zestawów danych zmodyfikowanych (PROSPECT-2 i PROSAIL-2), należy stwierdzić, że w porównaniu z zestawami danych niemodyfikowanych różnice między poszczególnymi aspektami modelowania są znacznie mniejsze. Takie same rezultaty, na danych modyfikowanych stosując oba modele, uzyskuje się w przypadku podziału poligonów na trzy rodzaje łąk. Różnice w wartościach pierwiastków błędów średniokwadratowych między poszczególnymi rodzajami łąk są bardzo niewielkie. Oceniając wpływ zawartości poszczególnych substancji w roślinach (biomasy świeżej, LAI i wody) na skuteczność modelowania odpowiedzi spektralnych z wykorzystaniem zmodyfikowanych zestawów danych można stwierdzić, że różnice w modelowaniu między poszczególnymi klasami badanych parametrów są znacznie mniejsze przy tych zestawach danych niż przy zestawach danych niezmodyfikowanych. Zawartość parametrów biofizycznych ma niewielki wpływ w podczerwieni. Większość teledetekcyjnych wskaźników roślinności modelowanych za pomocą obu algorytmów ma wartości zbliżone do wzorcowych. Mniejsze różnice między modelowaną wartością wskaźnika a obliczoną na podstawie pomiarów terenowych uzyskuje się z użyciem modelu PROSPECT. Wyjątkiem jest wskaźnik określający zwartość ligniny (NDLI). Zbliżoną skuteczność niezależnie od modelu uzyskuje się w przypadku wskaźnika zawartości celulozy (CAI). Oba modele dają bardzo dobre wyniki odnośnie do wskaźnika kanału wody (WBI), dość dobre są też wyniki dotyczące znormalizowanego różnicowego wskaźnika zieleni (NDVI). Przeprowadzone badania umożliwiły ocenę zastosowania modeli transferu promieniowania do modelowania odbicia od złożonych środowisk roślinnych. Na podstawie przeprowadzonych badań można stwierdzić, że modele transferu promieniowania mogą być stosowane do symulowania współczynnika odbicia spektralnego w przypadku niejednorodnej roślinności trawiastej. Jednak, aby możliwe było dalsze używanie modeli, konieczne jest wprowadzenie poprawek do danych wejściowych, co znacznie wydłuża proces modelowania. Łąki są na tyle skomplikowanym środowiskiem, że niektóre z parametrów muszą być ustalane lub dopasowywane, a nie mierzone w terenie. Na modelowanie znikomy wpływ ma rodzaj łąki i stopień jej złożoności. Na skuteczność symulacji współczynnika odbicia znacznie bardziej wpływają wartości poszczególnych parametrów biofizycznych. W dalszych badaniach warto wykonać inwersję modelu PROSAIL na podstawie opracowanych danych, aby uzyskać wybrane zmienne biofizyczne z charakterystyk spektralnych. Jest to ułatwione przez opracowanie zakresów danych wejściowych, jakie powinny być stosowane. Można także podjąć próbę modelowania korzystając z danych pozyskanych z pułapu lotniczego i satelitarnego. Na podstawie przeprowadzonych badań ustalono, jakie zakresy parametrów wejściowych do modeli PROSPECT i PROSAIL są optymalne dla tak zróżnicowanego środowiska, jakim są łąki na obszarze Polski. Feret J.-B., Frençois C., Asner G. P., Gitelson A. A., Martin R. E., Bidel L. P. R., Ustin S., le Maire G., Jacquemoud S., 2008, PROSPECT-4 and 5: Advances in the leaf optical properties model separating photosynthetic pigments. Remote Sensing of Environment, nr 112, str. 3030-3043. Jacquemoud S., Ustin S. L., Verdebout J., Schmuck G., Anderoli G., Hosgood B., 1996, Estimating Leaf Biochemistry Using the PROSPECT Leaf Optical Properties Model. Remote Sensing of Environment, nr 56, str. 194-202. Jacquemoud S., Verhoef W., Baret F., Bacour C., Zarco-Tejada P. J., Asner G. P., François H., Ustin S. L., 2009, PROSPECT+SAIL models: A review of use for vegetation characterization. Remote Sensing of Environment, nr 113, str.556–566. Olędzki J. R., 2007, Regiony geograficzne Polski, Teledetekcja Środowiska, nr 38, str. 1-337. Verhoef W., 1984, Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model. Remote Sensing of Environment, nr 16, str. 125-141. Verhoef W., 1985, Earth observation modeling based on layer scattering matrices. Remote Sensing of Environment, nr 17, str. 165-178.
Vegetation analysis is an important problem in regional and global scale. Because of pollution of environment and changes in the ecosystems plant monitoring is very important. Remote sensing data can be easily used to plant monitoring. That kind of method is much faster and more reliable than traditional approaches. Spectrometry analyses the interaction between radiation and object and it uses measurement of radiation intensity as a function of wavelength. Each object emits and absorbs different quantity of radiation, so it is possible to recognize the object and check its characteristics analyzing the spectrum. The subject of the researches is Polish meadows. The human usage of the meadows determines its proper functioning. Grasslands, which consist of meadows and pastures, cover 10% of Poland. Meadows are mostly extensively used, the crops from meadows, hay and green forage, are very low. The meadows in Poland are floristically and morphologically very diverse. Many factors influence on this ecosystem and that is why the monitoring is very important. The aim of the researches is to study the possibility of use of the Radiative Transfer Models in modeling the state of the heterogeneous vegetation cover of seminatural meadows in Poland. To canopy analysis two approaches are used: statistic and modeling. In statistic approach, biophysical parameters calculated from the image are correlated with reflectance or transmittance from field measurements. In second approach physically based model is used to represent the photon transport inside leaves and canopy. The Radiative Transfer Models are based on the laws of optics. Developing the model results in better understanding of the interaction of light in canopy and leaves. The Radiative Transfer Models are often applied to vegetation modeling. The Radiative Transfer Models are physically based models which describe the interactions of radiation in atmosphere and vegetation. Adjusted models can be used to fast and precise analysis of biophysical parameters of the canopy. The canopy can be described as homogeneous layer consist of leaves and spaces. The Radiative Transfer Models are algorithms which vary by input and output parameters, the level of the analysis, kinds of plants and other modifications. Models are used on two levels: single leaf and whole canopy. The first model, which is used in this research, is PROSPECT, which describes the multidirectional reflectance and diffusion on leaf level. It is often employed with other models that describe whole canopy. The input parameters in the model are: chlorophyll and carotenoid content, Equivalent Water Thickness and dry matter content and also leaf structure parameter that describe the leaf structure and complexity. Second model, which is used in the study, is the canopy reflectance model SAIL (Scattering by Arbitrarily Inclined Leaves). It simulates the top of the canopy bidirectional reflectance and it describes the canopy structure in a fairly simple way. In this analysis the 4-SAIL model will be used. This version has few input parameters that describe plants and soil: spectrometric data – reflectance and transmittance from leaves (the output parameters form PROSPECT model), biophysical canopy parameters (Leaf Area Index, brown pigment content, mean leaf inclination angle), soil brightness parameter, reflectance geometry (Solar zenith angle, observer zenith angle, relative azimuth angle), ratio of diffuse to total incident radiation and two hot spot size parameters. The SAIL model is often combined with the model on leaf level – the PROSAIL model. The PROSPECT and SAIL are very rarely used to meadows, because this kind of ecosystem is normally rather heterogeneous and modeling is quite difficult. In this study two Radiative Transfer Models (PROSPECT-5 and 4SAIL) were used form modeling the reflectance on leaf and canopy level. In order to acquire the input data to both models model and reference spectrums the field measurements were done. The input parameters were recalculated using fields measurements and put into the models: PROSPECT and PROSAIL. Only one leaf structure parameter was fitted for each polygon individually. The field measurements were done in 2009 and 2010on 57 test polygons, that were located in Carpathian Foothills (near Gorlice) and on the Środkowopolskie Plains. For each model two datasets were calculated: first (PROSECT-1 and PROSAIL-1), where the input parameters were calculated directly from field measurements, and second (PROSPECT-2 and PROSAIL-2), where water, chlorophyll and carotenoid content were modified. Then, the spectral reflectance obtained from model was compared with field measurements. Based on the calculated Root Mean Square Error the simulation was verified. The RMSE values were calculated for whole range from 0,4-2,5 µm and for specific ranges. The correctness of simulated spectra were analyzed dependent on the type of meadows (cultivated meadows with reduced amount of biomass, cultivated meadows with high amount of biomass and not cultivated meadows) and the value of three different biophysical parameters (Leaf Area index, fresh biomass content and water content). Better results were obtained using PROSPECT model than PROSAIL. In the visible light more accurate values were calculated using PROSAIL and in infrared using PROSPECT. Generally bigger errors were noticed in the infrared, especially middle infrared. In the case of the second datasets (PROSPECT-2 and PROSAIL-2) the largest root mean square error was noticed in the near-infrared and the smallest in the range 0.4-0.6 µm. For both models in each range the RMSE was smaller for datasets PROSPECT-2 and PROSAIL-2 compared to the PROSPECT-1 and PROSAIL-1. The effectiveness of simulation reflectance was not influenced by different kind of meadows. The only differences were noticed in infrared, but were not statistically significant. Apart from that, better results were obtained on meadows with higher biomass value, bigger Leaf Area Index and lower water content for both models. The differences in infrared were statistically significant, especially for the PROSPECT-1 and PROSAIL-1 datasets. Most modeled vegetation indices have vales similar to the field measurements. Better results were notice for PROSPECT model. The smallest differences were for Water band index and also Normalized Difference Vegetation Index. Generally, the PROSPECT and PROSAIL radiative transfer models can be used to simulate the spectral reflectance of vegetation on heterogeneous meadows. The models can be used to estimate the biophysical parameters, but it is necessary to correct the values of input variables (especially water content). Meadows are very complex environment and some of the parameters should be adjusted. The type of the meadow is irrelevant to the correctness of the simulation, while the effectiveness of simulation is much more influenced by the values of biophysical parameters.