A large portion of Earth¿s terrestrial surface is subject to arid climatic water stress. As in these regions the hydrological cycle and the vegetation dynamics are tightly interconnected, a coupled modeling of these two systems is needed to fully reproduce the ecosystems¿ behavior over time and to predict possible future responses to climate change. In this thesis, the performance of three parsimonious dynamic vegetation models, suitable for inclusion in an operational ecohydrological model, are tested in a semi-arid Aleppo pine forest area in the south-east of Spain. The first model considered, HORAS (Quevedo & Francés, 2008), simulates growth as a function of plant transpiration (T), evaluating environmental restraints through the transpiration-reference evapotranspiration ratio. The state variable related to vegetation is R, relative foliar biomass, which is equivalent to FAO crop coefficient (Allen et al., 1998), but not fixed in time. The HORAS model was then abandoned because of its unsatisfactory results, probably due to a poor simulation of evaporation and transpiration processes. As for the other two models, WUE-model and LUE-model, the state variable is the leaf biomass (Bl, kg dry mass m-2 vegetation cover). Both models simulate gross primary production (GPP), in the first case as a function of transpiration and water use efficiency (WUE), in the second case as a function of absorbed photosynthetically active radiation (APAR) and light use efficiency (LUE). Net primary production (NPP) is then calculated taking into account respiration. The modelling is focused particularly on simulating foliar biomass, which is obtained from NPP through an allocation equation based on the maximum leaf area index (LAI) sustainable by the system, and considering turnover. An analysis of the information offered by MODIS EVI, NDVI, and LAI products was also performed, in order to investigate vegetation dynamics in the study site and to select the best indices to be used as observational verification for models. MODIS EVI is reported in literature (Huete et al., 2002) to be highly correlated with leaf biomass. In accordance with the phenological cycle timing described for the Aleppo pine in similar climates (Muñoz et al., 2003), the EVI showed maximum values in spring and minimum values in winter. Similar results were found applying the aforementioned WUE- and LUE- models to the study area. Contrasting simulated LAI with the EVI series, the correlation coefficients rWUE = 0.45 and rLUE = 0.57 were found for the WUE-model and LUE-model respectively. Concerning NDVI, its own definition links this index to the ¿greenness¿ of the target, so that it appears highly linked to chlorophyll content and vegetation condition, but only indirectly related to LAI. Photosynthetic pigment concentrations are reported to be sensitive to water stress in Aleppo pine (Baquedano and Castillo, 2006) so, to compare the models¿ results with NDVI, the simulated LAI was corrected by plant water-stress. The resulting correlation coefficients were rWUE = 0.62 and rLUE = 0.59. Lastly, MODIS LAI and ET were found to be unreliable in the study area because very low compared to field data and to values reported in literature (e.g. Molina & del Campo, 2012) for the same species in similar climatic conditions. The performance of both WUE- and LUE- models in this semi-arid region is found to be reasonable. However, the LUE-model presents the advantages of a better performance, the possibility to be used in a wider range of climates and to have been extensively tested in literature. / Pasquato, M. (2013). Comparison of parsimonious dynamic vegetation modelling approaches for semiarid climates [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/34326

The objective of this study was to develop a method by which smallholder forest plantations can be mapped accurately in Andhra Pradesh, India using multitemporal (intra- and inter-annual) visible and near-infrared (VNIR) bands from the Sentinel-2 MultiSpectral Instruments (MSIs). Dependency on and scarcity of wood products have driven the deforestation and degradation of natural forests in Southeast Asia. At the same time, forest plantations have been established both within and outside of forests, with the latter (as contiguous blocks) being the focus of this study. The ecosystem services provided by natural forests are different from those of plantations. As such, being able to separate natural forests from plantations is important. Unfortunately, there are constraints to accurately mapping planted forests in Andhra Pradesh (and other similar landscapes in South and Southeast Asia) using remotely sensed data due to the plantations' small size (average 2 hectares), short rotation ages (often 4-7 years for timber species), and spectral similarities to croplands and natural forests. The East and West Godavari districts of Andhra Pradesh were selected as the area for a case study. Cloud-free Harmonized Landsat Sentinel-2 (HLS) S10 data was acquired over six dates, from different seasons, as follows: December 28, 2015; November 22, 2016; November 2, 2017; December 22, 2017; March 1, 2018; and June 15, 2018. Cloud-free satellite data are not available during the monsoon season (July to September) in this coastal region. In situ data on forest plantations, provided by collaborators, was supplemented with additional training data representing other land cover subclasses in the region: agriculture, water, aquaculture, mangrove, palm, forest plantation, ground, natural forest, shrub/scrub, sand, and urban, with a total sample size of 2,230. These high-quality samples were then aggregated into three land use classes: non-forest, natural forest, and forest plantations. Image classification used random forests within the Julia Decision Tree package on a thirty-band stack that was comprised of the VNIR bands and NDVI images for all dates. The median classification accuracy from the 5-fold cross validation was 94.3%. Our results, predicated on high quality training data, demonstrate that (mostly smallholder) forest plantations can be separated from natural forests even using only the Sentinel 2 VNIR bands when multitemporal data (across both years and seasons) are used. / The objective of this study was to develop a method by which smallholder forest plantations can be mapped accurately in Andhra Pradesh, India using multitemporal (intra- and inter-annual) visible (red, green, blue) and near-infrared (VNIR) bands from the European Space Agency satellite Sentinel-2. Dependency on and scarcity of wood products have driven the deforestation and degradation of natural forests in Southeast Asia. At the same time, forest plantations have been established both within and outside of forests, with the latter (as contiguous blocks) being the focus of this study. The ecosystem services provided by natural forests are different from those of plantations. As such, being able to separate natural forests from plantations is important. Unfortunately, there are constraints to accurately mapping planted forests in Andhra Pradesh (and other similar landscapes in South and Southeast Asia) using remotely sensed data due to the plantations' small size (average 2 hectares), short rotation ages (often 4-7 years for timber species), and spectral (reflectance from satellite imagery) similarities to croplands and natural forests. The East and West Godavari districts of Andhra Pradesh were selected as the area for a case study. Cloud-free Harmonized Landsat Sentinel-2 (HLS) S10 images were acquired over six dates, from different seasons, as follows: December 28, 2015; November 22, 2016; November 2, 2017; December 22, 2017; March 1, 2018; and June 15, 2018. Cloud-free satellite data are not available during the monsoon season (July to September) in this coastal region. In situ data on forest plantations, provided by collaborators, was supplemented with additional training data points (X and Y locations with land cover class) representing other land cover subclasses in the region: agriculture, water, aquaculture, mangrove, palm, forest plantation, ground, natural forest, shrub/scrub, sand, and urban, with a total of 2,230 training points. These high-quality samples were then aggregated into three land use classes: non-forest, natural forest, and forest plantations. Image classification used random forests within the Julia DecisionTree package on a thirty-band stack that was comprised of the VNIR bands and NDVI (calculation related to greenness, i.e. higher value = more vegetation) images for all dates. The median classification accuracy from the 5-fold cross validation was 94.3%. Our results, predicated on high quality training data, demonstrate that (mostly smallholder) forest plantations can be separated from natural forests even using only the Sentinel 2 VNIR bands when multitemporal data (across both years and seasons) are used.

This study examines the effectiveness of using the Normalized Difference Vegetation Index (NDVI) derived from 1326 different Landsat Thematic Mapper and Enhanced Thematic Mapper images in finding isolated housing starts within the Commonwealth of Virginia's forests. Individual NDVI images were stacked by year for the years 1995-2011 and the yearly maximum for each pixel was extracted, resulting in a 17-year image stack of all yearly maxima (a 98.7% data reduction). Using location data from housing starts and well permits, known previously forested housing starts were isolated from all other forest disturbance types. Samples from housing starts and other forest disturbances, as well as from undisturbed forest, were used to derive vegetation index thresholds enabling separation of disturbed from undisturbed forest. Disturbances, once identified, were separated accurately (overall accuracy = 85.4 percent, F-statistic = 0.86) into housing starts and other forest disturbances using a classification tree and only two variables from the Disturbance Detection and Diagnostics (D3) algorithm: the maximum NDVI in the available recovery period and the slope between the NDVI value at the time of the disturbance and the maximum NDVI in the available recovery period. Landsat time series stacks thus show promise for identifying even the small changes associated with exurban development. / Master of Science / The objective of this study was to determine whether low-density development in previously forested areas can be identified using a time series of maximum annual vegetation greenness derived from the Landsat earth observing satellite missions. The study area was the Commonwealth of Virginia, USA. This study used 1326 different Landsat satellite images from the years 1995 through 2011. Each image contained over 34 million pixels, which were converted to a value between 0 and 1 that indicated how vegetated they were (a higher value being more vegetated). When houses are constructed trees are removed, thus lowering (at least temporarily) the overall greenness in a given area. Using location data from housing starts and well permits, known previously forested housing starts were isolated from all other forest disturbance types. Samples from housing starts and other forest disturbances, as well as from undisturbed forest, were used to develop greenness thresholds enabling separation of disturbed from undisturbed forest. Disturbances, once identified, were separated accurately (overall accuracy = 85.4 percent) into housing starts and other (non-housing) disturbances using a classification tree and the highest greenness a pixel attained in the available years after being disturbed (indicating how much vegetation returned in the recovery period) as well as the slope from the year of the identified disturbance to the year that had the highest value in the available recovery period.

A presente pesquisa trata de uma análise da Estação Ecológica Serra Geral do Tocantins (EESGT) localizada entre os Estados do Tocantins e da Bahia, tendo como principal objetivo investigar a ação do fogo, antes e depois da sua criação, ou seja, entre os anos de 1998 e 2015. A metodologia utilizada foi o Sensoriamento Remoto com o qual aplicou-se diferentes técnicas que permitiram observar a ação do fogo com base nos Aspectos Regionais, NDVI, Focos de Calor e Cicatrizes do Fogo (queimadas e incêndios florestais) e a Recorrência destas áreas. Desta maneira, utilizou-se um conjunto de imagens de satélites Landsat Sensor -TM e Ressourcesat - 1, dos anos de 1998, 2001, 2007, 2012 e 2015, obtidas no período seco entre junho e outubro. Com os softwares QGis e ArcGIS® foi aplicado o Índice de Diferença de Vegetação Normalizado (NDVI) que permitiu a identificação de áreas atingidas pelo fogo, as quais foram aferidas ou confirmadas com a sobreposição dos focos de calor e observação visual de uma composição colorida (RGB) de imagem de satélite. No mesmo sentido, foi realizado uma classificação supervisionada de imagens de satélite, o que permitiu a identificação e o delineamento das cicatrizes de fogo e consequentemente, a ação do fogo na área de pesquisa. Entre os resultados obtidos, pode-se observar que no ano de 1998, a ação do fogo correspondeu a cerca de 50% de área, no ano de 2001 cerca de 46%, no ano de 2007 cerca de 68%, no ano de 2012 cerca de 54% e 2015 cerca de 41%. De maneira geral, pode-se observar uma redução da ação do fogo na área da EESGT nos últimos anos, o que pode estar associado“os fatores à diferentes formas” de manejo do fogo, o que precisa ser aprofundado com o andamento de novas pesquisas. Também foi possível observar, que anterior a criação da unidade, até o ano de 2001, os valores correspondentes a ação do fogo (50 e 46%) não reduziram, após a criação da unidade, no ano de 2007(68%) aumentaram. Porém, nos últimos anos ocorre uma redução da ação de fogo anos de 2012 e 2015 (54 e 41%), sendo um sinal positivo quanto a conservação do Cerrado e manutenção da função da unidade. / The present research deals with an analysis of the Estação Ecológica Serra Geral do Tocantins, located between the states of Tocantins and Bahia, with the main objective of investigating the fire action, before and after its creation, that is, between 1998 and 2015. The methodology used was the Remote Sensing with which different techniques were applied to observe the fire action based on the Regional Aspects, NDVI, Heat Sources and Fire Scars (burnings and forest fires) and Recurrence of these areas. A set of Landsat Sensor-TM and Ressourcesat-1 satellite images from 1998, 2001, 2007, 2012 and 2015 were obtained in the dry period between June and October. As the QGis and ArcGIS® software were applied Standardized Vegetation Difference Index (NDVI) that allowed the identification of areas affected by fire, which were measured or confirmed with the overlap of heat fires and visual observation of a color composition (RGB) satellite imagery. n the same direction a supervised classification of satellite images was carried out, which allowed the identification and the design of the fire scars and consequently the fire action in the research area. Among the results obtained, it can be observed that in 1998 the fire action corresponded to about 50% of area, in 2001 about 46%, in 2007 about 68%, in the year of 2012 about 54% and 2015 about 41%. In general, it can be observed a reduction of the fire action in the area of the EESGT in the last years, which may be associated the “factors to the different forms” of fire management, which needs to be deepened with the progress of new researches. It was also possible to observe that before the creation of the unit, until the year 2001, the values corresponding to the action of the fire (50 and 46%) did not reduce, after the creation of the unit, in the year of 2007 (68%) yes they increased. However, in recent years there has been a reduction in the fire action for the years 2012 and 2015 (54 and 41%), which is a positive sign for the conservation of the Cerrado and maintenance of the unit's function.

Made available in DSpace on 2017-07-21T14:19:37Z (GMT). No. of bitstreams: 1 CLAUDIO KAPP JUNIOR.pdf: 1815678 bytes, checksum: d0a947fefc0e6b1b5a0242eb975f3cb7 (MD5) Previous issue date: 2013-03-18 / No-till systems with diversified crop rotations have stood out of the most effective strategies to improve the sustainability of farming in tropical and subtropical regions. Wheat (Triticum aestivum L.) is one of the most important crops used in this rotation during the autumn-winter season. Nitrogen (N) is uptake in larger amounts by plants, it is essential for the structure and functions in the cell, for all enzymatic reactions and is part of the chlorophyll molecules. Nitrogen fertilizers represent a significant part of the costs of production and due to the dynamics of N in soil, losses of N occur and cause economic and environmental damages. In the same agricultural area may exist changing demands for this nutrient. The attributes of the plant commonly used as indicators of N are NO3- content in stem, leaf chlorophyll content, the intensity of the green color and the N foliar content, dry biomass, and the extraction of N by plants. Lower levels of N can cause chlorophyll deficiency that is recognized by whitish or pale foliar coloration, and this changing in plant color can be identified using remote sensing techniques. This study aimed to evaluate the correlations between spectral reflectance data obtained by commercial ground sensors (Clorofilog 1030, GreenSeeker, and Crop Circle ACS-470) and attributes of wheat crop in response to N rates in top dressing under a no-till system. The efficiency of the sensors was evaluated in two ways: (i) by classical statistical methods, and (ii) through the application of Artificial Neural Networks, a machine learning technique. For the use of Artificial Neural Networks, this study compared the performance of the algorithms Resilient Propagation and Backpropagation. Because wheat plants exhibited adequate nutritional status, even without N application in top dressing, Clorofilog 1030 readings were not sensitive to variations of N rates. Thus, this sensor also did not correlate significantly with the N foliar content, dry biomass, and the extraction of N by wheat plants. The indices obtained by reflectance sensors Crop Circle and GreenSeeker had close correlation with the rates of N in top dressing, dry biomass, and the extraction of N by wheat plants. The Crop Circle and GreenSeeker sensors showed weaker correlation with the N content in leaves, and especially with the wheat grain yield. In this way, it was evident that grain yield has not followed the dry biomass production when high wheat grain yields were obtained. The correlation coefficients obtained by the Resilient Propagation and Backpropagation algorithms were similar to those found by statistical analysis. The Artificial Neural Networks technique had satisfactory behavior similar to classical statistical methods. / O sistema plantio direto com rotação diversificada de culturas tem sido apontado como uma das melhores estratégias para aumentar a sustentabilidade da agricultura em regiões tropicais e subtropicais. Uma das culturas de maior importância nessa rotação durante a estação de outono–inverno é o trigo (Triticum aestivum L.). O nitrogênio (N) é um dos nutrientes extraídos em maior quantidade pelas plantas, sendo essencial para a estrutura e funções nas células, para todas as reações enzimáticas e faz parte das moléculas de clorofila. Os fertilizantes nitrogenados representam parte significativa dos custos da produção agrícola e, em razão da dinâmica do N no solo, perdas consideráveis de N podem ocorrer e causar prejuízos econômicos e ambientais. Em uma mesma área agrícola podem existir demandas variáveis por este nutriente. Os atributos da planta mais utilizados como indicadores de N são o teor de NO3- no colmo, o teor de clorofila, a intensidade da cor verde e o teor de N na folha bandeira, a produção de matéria seca da parte aérea e a extração de N pelas plantas. Níveis baixos de N podem ocasionar deficiência de clorofila que é reconhecida pela coloração pálida ou mesmo esbranquiçada da folha, e esta variação de coloração da planta pode ser identificada por meio de técnicas de sensoriamento remoto. Este trabalho teve o objetivo de estudar as correlações entre dados de refletância espectral obtidos por sensores terrestres comerciais (Clorofilog 1030, GreenSeeker e Crop Circle ACS-470) e atributos de desempenho da cultura do trigo em resposta à doses de N aplicadas em cobertura no sistema plantio direto. A eficiência dos sensores foi avaliada de duas maneiras: (i) por meio de métodos estatísticos clássicos e (ii) por meio da aplicação de Redes Neurais Artificiais com uso da técnica de aprendizado de máquina, software MatLab. Para a utilização de Redes Neurais Artificiais, este trabalho comparou o desempenho dos algoritmos Backpropagation e Resilient Propagation. Os resultados mostraram que as leituras do Clorofilog 1030 não foram sensíveis às variações das doses de N aplicadas em cobertura na cultura do trigo, pois as plantas de trigo apresentaram bom estado nutricional, mesmo sem aplicação de N em cobertura. Logo, este sensor também não teve correlação significativa com o teor de N na folha bandeira, a produção de matéria seca da parte aérea e a extração de N pelas plantas de trigo. Os índices obtidos pelos sensores de refletância Crop Circle e GreenSeeker tiveram estreita correlação com as doses de N aplicadas em cobertura, a produção de matéria seca da parte aérea e a extração de N pelas plantas de trigo. Os sensores Crop Circle e GreenSeeker apresentaram correlação mais fraca com o teor de N no tecido foliar e, principalmente, com a produtividade de grãos de trigo. Isso aconteceu porque ficou bem evidenciado que a produtividade de grãos não acompanhou os ganhos de matéria seca da parte aérea do trigo, quando os rendimentos de grãos de trigo foram elevados. Os coeficientes de correlação obtidos pelos algoritmos Backpropagation e Resilient Propagation foram semelhantes aos encontrados pelas análises estatísticas. A técnica de Redes Neurais Artificiais teve comportamento satisfatório e similar aos métodos estatísticos clássicos.

Made available in DSpace on 2017-07-24T19:38:08Z (GMT). No. of bitstreams: 1 CLAUDIO KAPP JUNIOR.pdf: 1815678 bytes, checksum: d0a947fefc0e6b1b5a0242eb975f3cb7 (MD5) Previous issue date: 2013-03-08 / No-till systems with diversified crop rotations have stood out of the most effective strategies to improve the sustainability of farming in tropical and subtropical regions. Wheat (Triticum aestivum L.) is one of the most important crops used in this rotation during the autumn-winter season. Nitrogen (N) is uptake in larger amounts by plants, it is essential for the structure and functions in the cell, for all enzymatic reactions and is part of the chlorophyll molecules. Nitrogen fertilizers represent a significant part of the costs of production and due to the dynamics of N in soil, losses of N occur and cause economic and environmental damages. In the same agricultural area may exist changing demands for this nutrient. The attributes of the plant commonly used as indicators of N are NO3- content in stem, leaf chlorophyll content, the intensity of the green color and the N foliar content, dry biomass, and the extraction of N by plants. Lower levels of N can cause chlorophyll deficiency that is recognized by whitish or pale foliar coloration, and this changing in plant color can be identified using remote sensing techniques. This study aimed to evaluate the correlations between spectral reflectance data obtained by commercial ground sensors (Clorofilog 1030, GreenSeeker, and Crop Circle ACS-470) and attributes of wheat crop in response to N rates in top dressing under a no-till system. The efficiency of the sensors was evaluated in two ways: (i) by classical statistical methods, and (ii) through the application of Artificial Neural Networks, a machine learning technique. For the use of Artificial Neural Networks, this study compared the performance of the algorithms Resilient Propagation and Backpropagation. Because wheat plants exhibited adequate nutritional status, even without N application in top dressing, Clorofilog 1030 readings were not sensitive to variations of N rates. Thus, this sensor also did not correlate significantly with the N foliar content, dry biomass, and the extraction of N by wheat plants. The indices obtained by reflectance sensors Crop Circle and GreenSeeker had close correlation with the rates of N in top dressing, dry biomass, and the extraction of N by wheat plants. The Crop Circle and GreenSeeker sensors showed weaker correlation with the N content in leaves, and especially with the wheat grain yield. In this way, it was evident that grain yield has not followed the dry biomass production when high wheat grain yields were obtained. The correlation coefficients obtained by the Resilient Propagation and Backpropagation algorithms were similar to those found by statistical analysis. The Artificial Neural Networks technique had satisfactory behavior similar to classical statistical methods. / O sistema plantio direto com rotação diversificada de culturas tem sido apontado como uma das melhores estratégias para aumentar a sustentabilidade da agricultura em regiões tropicais e subtropicais. Uma das culturas de maior importância nessa rotação durante a estação de outono–inverno é o trigo (Triticum aestivum L.). O nitrogênio (N) é um dos nutrientes extraídos em maior quantidade pelas plantas, sendo essencial para a estrutura e funções nas células, para todas as reações enzimáticas e faz parte das moléculas de clorofila. Os fertilizantes nitrogenados representam parte significativa dos custos da produção agrícola e, em razão da dinâmica do N no solo, perdas consideráveis de N podem ocorrer e causar prejuízos econômicos e ambientais. Em uma mesma área agrícola podem existir demandas variáveis por este nutriente. Os atributos da planta mais utilizados como indicadores de N são o teor de NO3- no colmo, o teor de clorofila, a intensidade da cor verde e o teor de N na folha bandeira, a produção de matéria seca da parte aérea e a extração de N pelas plantas. Níveis baixos de N podem ocasionar deficiência de clorofila que é reconhecida pela coloração pálida ou mesmo esbranquiçada da folha, e esta variação de coloração da planta pode ser identificada por meio de técnicas de sensoriamento remoto. Este trabalho teve o objetivo de estudar as correlações entre dados de refletância espectral obtidos por sensores terrestres comerciais (Clorofilog 1030, GreenSeeker e Crop Circle ACS-470) e atributos de desempenho da cultura do trigo em resposta à doses de N aplicadas em cobertura no sistema plantio direto. A eficiência dos sensores foi avaliada de duas maneiras: (i) por meio de métodos estatísticos clássicos e (ii) por meio da aplicação de Redes Neurais Artificiais com uso da técnica de aprendizado de máquina, software MatLab. Para a utilização de Redes Neurais Artificiais, este trabalho comparou o desempenho dos algoritmos Backpropagation e Resilient Propagation. Os resultados mostraram que as leituras do Clorofilog 1030 não foram sensíveis às variações das doses de N aplicadas em cobertura na cultura do trigo, pois as plantas de trigo apresentaram bom estado nutricional, mesmo sem aplicação de N em cobertura. Logo, este sensor também não teve correlação significativa com o teor de N na folha bandeira, a produção de matéria seca da parte aérea e a extração de N pelas plantas de trigo. Os índices obtidos pelos sensores de refletância Crop Circle e GreenSeeker tiveram estreita correlação com as doses de N aplicadas em cobertura, a produção de matéria seca da parte aérea e a extração de N pelas plantas de trigo. Os sensores Crop Circle e GreenSeeker apresentaram correlação mais fraca com o teor de N no tecido foliar e, principalmente, com a produtividade de grãos de trigo. Isso aconteceu porque ficou bem evidenciado que a produtividade de grãos não acompanhou os ganhos de matéria seca da parte aérea do trigo, quando os rendimentos de grãos de trigo foram elevados. Os coeficientes de correlação obtidos pelos algoritmos Backpropagation e Resilient Propagation foram semelhantes aos encontrados pelas análises estatísticas. A técnica de Redes Neurais Artificiais teve comportamento satisfatório e similar aos métodos estatísticos clássicos.

En Viticulture de Précision, l’imagerie multi-spectrale est principalement utilisée pour des dispositifs de télédétection. Ce manuscrit s’intéresse à son utilisation en proxidétection, pour la caractérisation du feuillage. Il présente un dispositif expérimental terrestre mobile composé d’un GPS, d’une caméra multi-spectrale acquérant des images visible et proche infrarouge, et d’un Greenseeker RT-100 mesurant l’indice Normalized Difference Vegetation Index (NDVI). Ce système observe le feuillage de la vigne dans le plan de palissage, en lumière naturelle. La parcelle étudiée comporte trois cépages (Pinot Noir, Chardonnay et Meunier) plantés en carré latin. En 2013, six jeux de données ont été acquis à différents stades phénologiques.Pour accéder aux propriétés spectrales de la végétation, il est nécessaire de calibrer les images en réflectance. Cela requiert l’utilisation d’une mire de MacBeth comme référence radiométrique. Lorsque la mire est cachée par les feuilles, les paramètres de calibration sont estimés par une interpolation linéaire en fonction des images les plus proches sur lesquelles la mire est visible. La cohérence de la méthode d’estimation employée est vérifiée par une validation croisée (LOOCV).La comparaison du NDVI fournie par le Greenseeker avec celui déterminé via les images corrigées permet de valider les données générées par le dispositif. La polyvalence du système est évaluée via les images où plusieurs indices de végétation sont déterminés. Ils permettant des suivis de croissance de la végétation originaux offrant des potentialités de phénotypage ou une caractérisation de l’état sanitaire de la végétation illustrant la polyvalence et le gain en précision de cette technique. / Mutispectral imaging systems are widely used in remote sensing for Precision Viticulture. In this work, this technique was applied in the proximal sensing context to characterize vine foliage. A mobile terrestrial experimental system is presented, composed of a GPS receiver, a multi-spectral camera acquiring visible and near infrared images, and a Greenseeker RT-100 which measures the Normalized Difference Vegetative Index (NDVI). This optical system observes vine foliage in the trellis plan, in natural sunlight. The experimental field is planted with Chardonnay, Pinot Noir and Meunier cultivars in a latin squared pattern. In 2013, six datasets were acquired at various phenological stages.Spectral properties of the vegetation are accessible on images when they are calibrated in reflectance. This step requires the use of a MacBeth colorchart as a radiometric reference. When the chart is hidden by leaves, the calibration parameters are estimated by simple linear interpolation using the results from resembling images, which have a visible chart. The performance of this method is verified with a cross-validation technique (LOOCV).To validate the data provided by the experimental system, the NDVI given by the Greenseeker was compared to those computed from the calibrated images. The assessment of the versatility of the system is done with the images where several indices were determined. It allows an innovative follow-up of the vegetative growth, and offering phenotyping applications. Moreover, the characterization of the sanitary state of the foliage prove that this technique is versatile and accurate.

Desertification is a serious problem in environments where it occurs, namely in dry lands (arid, semi-arid and dry sub-humid areas). This type of degradation affects about one quarter of the land surface, with implications for environmental, economic, political, social and cultural order. The areas in Brazil susceptible to this process are located in the northeast region which is characterized by low rainfall index, high temperatures, severe water deficit, shallow and rocky soils and xerophytic vegetation. São Raimundo Nonato, which is the object of this study, is located in the semi-arid region of Piaui and is a susceptible area to desertification. This has led to the study of degradation / desertification in this area in order to assess the risk of physical deterioration and effective degradation. The risk of physical deterioration was evaluated using the following indicators: climate, rainfall erosivity, erodibility of soils and slopeness. The effective degradation was assessed by considering the indicators discussed above combined with the NDVI of the years 1987 and 2007. The results indicate that 8.3%, 81% and 10.7% of the area are at risk of a low, moderate and high physical deterioration. The effective degradation, taking into account the NDVI for 1987, indicates that 70% and 30% of the area have respectively moderate and high degradation. For the year 2007, the data indicate that 71% and 29% of the area have respectively moderate and high effective degradation. These data reveal a dynamic ecological equilibrium with a subtle trend of improvement in terms of environmental degradation, that is , in the process of desertification in the studied area, since there is a reduction of the class of high effective degradation. The decline and economic stagnation in the area were found during the analysis of major crops and effective livestock . These data revealed a decline in the planted area, productivity and effective livestock, both in number of heads and / or unit of animals. The decline of these indicators corroborates the statement of improvement of environmental conditions in the studied area. / A desertificação constitui um grave problema nos ambientes em que ocorre, qual seja as Terras Secas (áridas, semiáridas e subúmidas secas). Esse tipo de degradação afeta cerca de 1/4 da superfície terrestre, com implicações de ordem ambiental, econômica, política, social e cultural. No Brasil as áreas suscetíveis a esse processo localizam-se na região Nordeste caracterizada por baixos índices pluviométricos, elevadas temperaturas médias, acentuado déficit hídrico, solos rasos e pedregosos e vegetação xerofítica. O Núcleo de São Raimundo Nonato, objeto deste estudo, localizado no semi-árido piauiense constitui área suscetível à desertificação. Esta constatação conduziu ao estudo da degradação/desertificação desta área com o objetivo de avaliar o risco de degradação física e a degradação efetiva. O risco de degradação física foi avaliado a partir dos seguintes indicadores: índice climático, erosividade das chuvas, erodibilidade dos solos e a declividade. A degradação efetiva foi avaliada considerando os indicadores anteriormente citados combinados ao NDVI dos anos de 1987 e 2007. Os resultados indicam que 8,3%, 81% e 10,7% da área apresentam risco de degradação física baixo, moderado e alto. A degradação efetiva, considerando o NDVI para 1987, indica que 70% e 30% da área apresenta respectivamente degradação moderada e alta. Para o ano de 2007, os dados indicam que 71% e 29% da área apresenta respectivamente degradação efetiva moderada e alta. Esses dados revelam uma situação de equilíbrio ecológico dinâmico com uma sutil tendência de melhoria nas condições de degradação ambiental, ou seja, no processo de desertificação da área de estudo, posto a redução da classe de alta degradação efetiva. O declínio e a estagnação econômica da área foram constatados quando da analise das principais culturas e do efetivo de rebanhos. Esses dados revelaram redução da área plantada, da produtividade e do efetivo dos rebanhos em número de cabeças e de unidades animais. A queda desses indicadores corrobora a afirmativa de melhoria das condições ambientais da área de estudo.

This work was written as a cumulative doctoral thesis based on reviewed publications. Climate projections are mainly based on the results of numeric simulations from global or regional climate models. Up to now processes between atmosphere and land surface are only rudimentarily known. This causes one of the major uncertainties in existing models. In order to reduce parameterisation uncertainties and to find a reasonable description of sub grid heterogeneities, the determination and evaluation of parameterisation schemes for modelling require as many datasets from different spatial scales as possible. This work contributes to this topic by implying different datasets from different platforms. Its objective was to analyse the spatial heterogeneity of land surface parameters and energy flux densities obtained from both satellite observations with different spatial and temporal resolutions and in-situ measurements. The investigations were carried out for two target areas in Germany. First, satellite data for the years 2002 and 2003 were analysed and validated from the LITFASS-area (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study). Second, the data from the experimental field sites of the FLUXNET cluster around Tharandt from the years 2006 and 2007 were used to determine the NDVI (Normalised Difference Vegetation Index for identifying vegetated areas and their "condition"). The core of the study was the determination of land surface characteristics and hence radiant and energy flux densities (net radiation, soil heat flux, sensible and latent heat flux) using the three optical satellite sensors ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spektroradiometer) and AVHRR 3 (Advanced Very High Resolution Radiometer) with different spatial (30 m – 1 km) and temporal (1 day – 16 days) resolution. Different sensor characteristics and different data sets for land use classifications can both lead to deviations of the resultant energy fluxes between the sensors. Thus, sensor differences were quantified, sensor adaptation methods were implemented and a quality analysis for land use classifications was performed. The result is then a single parameterisation scheme that allows for the determination of the energy fluxes from all three different sensors. The main focus was the derivation of the latent heat flux (L.E) using the Penman-Monteith (P-M) approach. Satellite data provide measurements of spectral reflectance and surface temperatures. The P-M approach requires further surface parameters not offered by satellite data. These parameters include the NDVI, Leaf Area Index (LAI), wind speed, relative humidity, vegetation height and roughness length, for example. They were derived indirectly from the given satellite- or in-situ measurements. If no data were available so called default values from literature were taken. The quality of these parameters strongly influenced the exactness of the radiant- and energy fluxes. Sensitivity studies showed that NDVI is one of the most important parameters for determination of evaporation. In contrast it could be shown, that the parameters as vegetation height and measurement height have only minor influence on L.E, which justifies the use of default values for these parameters. Due to the key role of NDVI a field study was carried out investigating the spatial variability and sensitivity of NDVI above five different land use types (winter wheat, corn, grass, beech and spruce). Methods to determine this parameter not only from space (spectral), but also from in-situ tower measurements (broadband) and spectrometer data (spectral) were compared. The best agreement between the methods was found for winter wheat and grass measurements in 2006. For these land use types the results differed by less than 10 % and 15 %, respectively. Larger differences were obtained for the forest measurements. The correlation between the daily MODIS-NDVI data and the in-situ NDVI inferred from the spectrometer and the broadband measurements were r=0.67 and r=0.51, respectively. Subsequently, spatial variability of land surface parameters and fluxes were analysed. The several spatial resolutions of the satellite sensors can be used to describe subscale heterogeneity from one scale to the other and to study the effects of spatial averaging. Therefore land use dependent parameters and fluxes were investigated to find typical distribution patterns of land surface properties and energy fluxes. Implying the distribution patterns found here for albedo and NDVI from ETM+ data in models has high potential to calculate representative energy flux distributions on a coarser scale. The distribution patterns were expressed as probability density functions (PDFs). First results of applying PDFs of albedo, NDVI, relative humidity, and wind speed to the L.E computation are encouraging, and they show the high potential of this method. Summing up, the method of satellite based surface parameter- and energy flux determination has been shown to work reliably on different temporal and spatial scales. The data are useful for detailed analyses of spatial variability of a landscape and for the description of sub grid heterogeneity, as it is needed in model applications. Their usability as input parameters for modelling on different scales is the second important result of this work. The derived vegetation parameters, e.g. LAI and plant cover, possess realistic values and were used as model input for the Lokalmodell of the German Weather Service. This significantly improved the model results for L.E. Additionally, thermal parameter fields, e.g. surface temperature from ETM+ with 30 m spatial resolution, were used as input for SVAT-modelling (Soil-Vegetation-Atmosphere-Transfer scheme). Thus, more realistic L.E results were obtained, providing highly resolved areal information. / Die vorliegende Arbeit wurde auf der Grundlage begutachteter Publikationen als kumulative Dissertation verfasst. Klimaprognosen basieren im Allgemeinen auf den Ergebnissen numerischer Simulationen mit globalen oder regionalen Klimamodellen. Eine der entscheidenden Unsicherheiten bestehender Modelle liegt in dem noch unzureichenden Verständnis von Wechselwirkungsprozessen zwischen der Atmosphäre und Landoberflächen und dem daraus folgenden Fehlen entsprechender Parametrisierungen. Um das Problem einer unsicheren Modell-Parametrisierung aufzugreifen und zum Beispiel subskalige Heterogenität in einer Art und Weise zu beschreiben, dass sie für Modelle nutzbar wird, werden für die Bestimmung und Evaluierung von Modell-Parametrisierungsansätzen so viele Datensätze wie möglich benötigt. Die Arbeit trägt zu diesem Thema durch die Verwendung verschiedener Datensätze unterschiedlicher Plattformen bei. Ziel der Studie war es, aus Satellitendaten verschiedener räumlicher und zeitlicher Auflösung sowie aus in-situ Daten die räumliche Heterogenität von Landoberflächenparametern und Energieflussdichten zu bestimmen. Die Untersuchungen wurden für zwei Zielgebiete in Deutschland durchgeführt. Für das LITFASS-Gebiet (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study) wurden Satellitendaten der Jahre 2002 und 2003 untersucht und validiert. Zusätzlich wurde im Rahmen dieser Arbeit eine NDVI-Studie (Normalisierter Differenzen Vegetations Index: Maß zur Detektierung von Vegetationflächen, deren Vitalität und Dichte) auf den Testflächen des FLUXNET Clusters um Tharandt in den Jahren 2006 und 2007 realisiert. Die Grundlage der Arbeit bildete die Bestimmung von Landoberflächeneigenschaften und daraus resultierenden Energieflüssen, auf Basis dreier optischer Sensoren (ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spectroradiometer) und AVHRR 3 (Advanced Very High Resolution Radiometer)) mit unterschiedlichen räumlichen (30 m – 1 km) und zeitlichen (1 – 16 Tage) Auflösungen. Unterschiedliche Sensorcharakteristiken, sowie die Verwendung verschiedener, zum Teil ungenauer Datensätze zur Landnutzungsklassifikation führen zu Abweichungen in den Ergebnissen der einzelnen Sensoren. Durch die Quantifizierung der Sensorunterschiede, die Anpassung der Ergebnisse der Sensoren aneinander und eine Qualitätsanalyse von verschiedenen Landnutzungsklassifikationen, wurde eine Basis für eine vergleichbare Parametrisierung der Oberflächenparameter und damit auch für die daraus berechneten Energieflüsse geschaffen. Der Schwerpunkt lag dabei auf der Bestimmung des latenten Wärmestromes (L.E) mit Hilfe des Penman-Monteith Ansatzes (P-M). Satellitendaten liefern Messwerte der spektralen Reflexion und der Oberflächentemperatur. Die P-M Gleichung erfordert weitere Oberflächenparameter wie zum Beispiel den NDVI, den Blattflächenindex (LAI), die Windgeschwindigkeit, die relative Luftfeuchte, die Vegetationshöhe oder die Rauhigkeitslänge, die jedoch aus den Satellitendaten nicht bestimmt werden können. Sie müssen indirekt aus den oben genannten Messgrößen der Satelliten oder aus in-situ Messungen abgeleitet werden. Stehen auch aus diesen Quellen keine Daten zur Verfügung, können sogenannte Standard- (Default-) Werte aus der Literatur verwendet werden. Die Qualität dieser Parameter hat einen großen Einfluss auf die Bestimmung der Strahlungs- und Energieflüsse. Sensitivitätsstudien im Rahmen der Arbeit zeigen die Bedeutung des NDVI als einen der wichtigsten Parameter in der Verdunstungsbestimmung nach P-M. Im Gegensatz dazu wurde deutlich, dass z. B. die Vegetationshöhe und die Messhöhe einen relativ kleinen Einfluss auf L.E haben, so dass für diese Parameter die Verwendung von Standardwerten gerechtfertigt ist. Aufgrund der Schlüsselrolle, welche der NDVI in der Bestimmung der Verdunstung einnimmt, wurden im Rahmen einer Feldstudie Untersuchungen des NDVI über fünf verschiedenen Landnutzungstypen (Winterweizen, Mais, Gras, Buche und Fichte) hinsichtlich seiner räumlichen Variabilität und Sensitivität, unternommen. Dabei wurden verschiedene Bestimmungsmethoden getestet, in welchen der NDVI nicht nur aus Satellitendaten (spektral), sondern auch aus in-situ Turmmessungen (breitbandig) und Spekrometermessungen (spektral) ermittelt wird. Die besten Übereinstimmungen der Ergebnisse wurden dabei für Winterweizen und Gras für das Jahr 2006 gefunden. Für diese Landnutzungstypen betrugen die Maximaldifferenzen aus den drei Methoden jeweils 10 beziehungsweise 15 %. Deutlichere Differenzen ließen sich für die Forstflächen verzeichnen. Die Korrelation zwischen Satelliten- und Spektrometermessung betrug r=0.67. Für Satelliten- und Turmmessungen ergab sich ein Wert von r=0.5. Basierend auf den beschriebenen Vorarbeiten wurde die räumliche Variabilität von Landoberflächenparametern und Flüssen untersucht. Die unterschiedlichen räumlichen Auflösungen der Satelliten können genutzt werden, um zum einen die subskalige Heterogenität zu beschreiben, aber auch, um den Effekt räumlicher Mittelungsverfahren zu testen. Dafür wurden Parameter und Energieflüsse in Abhängigkeit der Landnutzungsklasse untersucht, um typische Verteilungsmuster dieser Größen zu finden. Die Verwendung der Verteilungsmuster (in Form von Wahrscheinlichkeitsdichteverteilungen – PDFs), die für die Albedo und den NDVI aus ETM+ Daten gefunden wurden, bietet ein hohes Potential als Modellinput, um repräsentative PDFs der Energieflüsse auf gröberen Skalen zu erhalten. Die ersten Ergebnisse in der Verwendung der PDFs von Albedo, NDVI, relativer Luftfeuchtigkeit und Windgeschwindigkeit für die Bestimmung von L.E waren sehr ermutigend und zeigten das hohe Potential der Methode. Zusammenfassend lässt sich feststellen, dass die Methode der Ableitung von Oberflächenparametern und Energieflüssen aus Satellitendaten zuverlässige Daten auf verschiedenen zeitlichen und räumlichen Skalen liefert. Die Daten sind für eine detaillierte Analyse der räumlichen Variabilität der Landschaft und für die Beschreibung der subskaligen Heterogenität, wie sie oft in Modellanwendungen benötigt wird, geeignet. Ihre Nutzbarkeit als Inputparameter in Modellen auf verschiedenen Skalen ist das zweite wichtige Ergebnis der Arbeit. Aus Satellitendaten abgeleitete Vegetationsparameter wie der LAI oder die Pflanzenbedeckung liefern realistische Ergebnisse, die zum Beispiel als Modellinput in das Lokalmodell des Deutschen Wetterdienstes implementiert werden konnten und die Modellergebnisse von L.E signifikant verbessert haben. Aber auch thermale Parameter, wie beispielsweise die Oberflächentemperatur aus ETM+ Daten in 30 m Auflösung, wurden als Eingabeparameter eines Soil-Vegetation-Atmosphere-Transfer-Modells (SVAT) verwendet. Dadurch erhält man realistischere Ergebnisse für L.E, die hochaufgelöste Flächeninformationen bieten.

This work was written as a cumulative doctoral thesis based on reviewed publications. Climate projections are mainly based on the results of numeric simulations from global or regional climate models. Up to now processes between atmosphere and land surface are only rudimentarily known. This causes one of the major uncertainties in existing models. In order to reduce parameterisation uncertainties and to find a reasonable description of sub grid heterogeneities, the determination and evaluation of parameterisation schemes for modelling require as many datasets from different spatial scales as possible. This work contributes to this topic by implying different datasets from different platforms. Its objective was to analyse the spatial heterogeneity of land surface parameters and energy flux densities obtained from both satellite observations with different spatial and temporal resolutions and in-situ measurements. The investigations were carried out for two target areas in Germany. First, satellite data for the years 2002 and 2003 were analysed and validated from the LITFASS-area (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study). Second, the data from the experimental field sites of the FLUXNET cluster around Tharandt from the years 2006 and 2007 were used to determine the NDVI (Normalised Difference Vegetation Index for identifying vegetated areas and their "condition"). The core of the study was the determination of land surface characteristics and hence radiant and energy flux densities (net radiation, soil heat flux, sensible and latent heat flux) using the three optical satellite sensors ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spektroradiometer) and AVHRR 3 (Advanced Very High Resolution Radiometer) with different spatial (30 m – 1 km) and temporal (1 day – 16 days) resolution. Different sensor characteristics and different data sets for land use classifications can both lead to deviations of the resultant energy fluxes between the sensors. Thus, sensor differences were quantified, sensor adaptation methods were implemented and a quality analysis for land use classifications was performed. The result is then a single parameterisation scheme that allows for the determination of the energy fluxes from all three different sensors. The main focus was the derivation of the latent heat flux (L.E) using the Penman-Monteith (P-M) approach. Satellite data provide measurements of spectral reflectance and surface temperatures. The P-M approach requires further surface parameters not offered by satellite data. These parameters include the NDVI, Leaf Area Index (LAI), wind speed, relative humidity, vegetation height and roughness length, for example. They were derived indirectly from the given satellite- or in-situ measurements. If no data were available so called default values from literature were taken. The quality of these parameters strongly influenced the exactness of the radiant- and energy fluxes. Sensitivity studies showed that NDVI is one of the most important parameters for determination of evaporation. In contrast it could be shown, that the parameters as vegetation height and measurement height have only minor influence on L.E, which justifies the use of default values for these parameters. Due to the key role of NDVI a field study was carried out investigating the spatial variability and sensitivity of NDVI above five different land use types (winter wheat, corn, grass, beech and spruce). Methods to determine this parameter not only from space (spectral), but also from in-situ tower measurements (broadband) and spectrometer data (spectral) were compared. The best agreement between the methods was found for winter wheat and grass measurements in 2006. For these land use types the results differed by less than 10 % and 15 %, respectively. Larger differences were obtained for the forest measurements. The correlation between the daily MODIS-NDVI data and the in-situ NDVI inferred from the spectrometer and the broadband measurements were r=0.67 and r=0.51, respectively. Subsequently, spatial variability of land surface parameters and fluxes were analysed. The several spatial resolutions of the satellite sensors can be used to describe subscale heterogeneity from one scale to the other and to study the effects of spatial averaging. Therefore land use dependent parameters and fluxes were investigated to find typical distribution patterns of land surface properties and energy fluxes. Implying the distribution patterns found here for albedo and NDVI from ETM+ data in models has high potential to calculate representative energy flux distributions on a coarser scale. The distribution patterns were expressed as probability density functions (PDFs). First results of applying PDFs of albedo, NDVI, relative humidity, and wind speed to the L.E computation are encouraging, and they show the high potential of this method. Summing up, the method of satellite based surface parameter- and energy flux determination has been shown to work reliably on different temporal and spatial scales. The data are useful for detailed analyses of spatial variability of a landscape and for the description of sub grid heterogeneity, as it is needed in model applications. Their usability as input parameters for modelling on different scales is the second important result of this work. The derived vegetation parameters, e.g. LAI and plant cover, possess realistic values and were used as model input for the Lokalmodell of the German Weather Service. This significantly improved the model results for L.E. Additionally, thermal parameter fields, e.g. surface temperature from ETM+ with 30 m spatial resolution, were used as input for SVAT-modelling (Soil-Vegetation-Atmosphere-Transfer scheme). Thus, more realistic L.E results were obtained, providing highly resolved areal information. / Die vorliegende Arbeit wurde auf der Grundlage begutachteter Publikationen als kumulative Dissertation verfasst. Klimaprognosen basieren im Allgemeinen auf den Ergebnissen numerischer Simulationen mit globalen oder regionalen Klimamodellen. Eine der entscheidenden Unsicherheiten bestehender Modelle liegt in dem noch unzureichenden Verständnis von Wechselwirkungsprozessen zwischen der Atmosphäre und Landoberflächen und dem daraus folgenden Fehlen entsprechender Parametrisierungen. Um das Problem einer unsicheren Modell-Parametrisierung aufzugreifen und zum Beispiel subskalige Heterogenität in einer Art und Weise zu beschreiben, dass sie für Modelle nutzbar wird, werden für die Bestimmung und Evaluierung von Modell-Parametrisierungsansätzen so viele Datensätze wie möglich benötigt. Die Arbeit trägt zu diesem Thema durch die Verwendung verschiedener Datensätze unterschiedlicher Plattformen bei. Ziel der Studie war es, aus Satellitendaten verschiedener räumlicher und zeitlicher Auflösung sowie aus in-situ Daten die räumliche Heterogenität von Landoberflächenparametern und Energieflussdichten zu bestimmen. Die Untersuchungen wurden für zwei Zielgebiete in Deutschland durchgeführt. Für das LITFASS-Gebiet (Lindenberg Inhomogeneous Terrain - Fluxes between Atmosphere and Surface: a longterm Study) wurden Satellitendaten der Jahre 2002 und 2003 untersucht und validiert. Zusätzlich wurde im Rahmen dieser Arbeit eine NDVI-Studie (Normalisierter Differenzen Vegetations Index: Maß zur Detektierung von Vegetationflächen, deren Vitalität und Dichte) auf den Testflächen des FLUXNET Clusters um Tharandt in den Jahren 2006 und 2007 realisiert. Die Grundlage der Arbeit bildete die Bestimmung von Landoberflächeneigenschaften und daraus resultierenden Energieflüssen, auf Basis dreier optischer Sensoren (ETM+ (Enhanced Thematic Mapper), MODIS (Moderate Resolution Imaging Spectroradiometer) und AVHRR 3 (Advanced Very High Resolution Radiometer)) mit unterschiedlichen räumlichen (30 m – 1 km) und zeitlichen (1 – 16 Tage) Auflösungen. Unterschiedliche Sensorcharakteristiken, sowie die Verwendung verschiedener, zum Teil ungenauer Datensätze zur Landnutzungsklassifikation führen zu Abweichungen in den Ergebnissen der einzelnen Sensoren. Durch die Quantifizierung der Sensorunterschiede, die Anpassung der Ergebnisse der Sensoren aneinander und eine Qualitätsanalyse von verschiedenen Landnutzungsklassifikationen, wurde eine Basis für eine vergleichbare Parametrisierung der Oberflächenparameter und damit auch für die daraus berechneten Energieflüsse geschaffen. Der Schwerpunkt lag dabei auf der Bestimmung des latenten Wärmestromes (L.E) mit Hilfe des Penman-Monteith Ansatzes (P-M). Satellitendaten liefern Messwerte der spektralen Reflexion und der Oberflächentemperatur. Die P-M Gleichung erfordert weitere Oberflächenparameter wie zum Beispiel den NDVI, den Blattflächenindex (LAI), die Windgeschwindigkeit, die relative Luftfeuchte, die Vegetationshöhe oder die Rauhigkeitslänge, die jedoch aus den Satellitendaten nicht bestimmt werden können. Sie müssen indirekt aus den oben genannten Messgrößen der Satelliten oder aus in-situ Messungen abgeleitet werden. Stehen auch aus diesen Quellen keine Daten zur Verfügung, können sogenannte Standard- (Default-) Werte aus der Literatur verwendet werden. Die Qualität dieser Parameter hat einen großen Einfluss auf die Bestimmung der Strahlungs- und Energieflüsse. Sensitivitätsstudien im Rahmen der Arbeit zeigen die Bedeutung des NDVI als einen der wichtigsten Parameter in der Verdunstungsbestimmung nach P-M. Im Gegensatz dazu wurde deutlich, dass z. B. die Vegetationshöhe und die Messhöhe einen relativ kleinen Einfluss auf L.E haben, so dass für diese Parameter die Verwendung von Standardwerten gerechtfertigt ist. Aufgrund der Schlüsselrolle, welche der NDVI in der Bestimmung der Verdunstung einnimmt, wurden im Rahmen einer Feldstudie Untersuchungen des NDVI über fünf verschiedenen Landnutzungstypen (Winterweizen, Mais, Gras, Buche und Fichte) hinsichtlich seiner räumlichen Variabilität und Sensitivität, unternommen. Dabei wurden verschiedene Bestimmungsmethoden getestet, in welchen der NDVI nicht nur aus Satellitendaten (spektral), sondern auch aus in-situ Turmmessungen (breitbandig) und Spekrometermessungen (spektral) ermittelt wird. Die besten Übereinstimmungen der Ergebnisse wurden dabei für Winterweizen und Gras für das Jahr 2006 gefunden. Für diese Landnutzungstypen betrugen die Maximaldifferenzen aus den drei Methoden jeweils 10 beziehungsweise 15 %. Deutlichere Differenzen ließen sich für die Forstflächen verzeichnen. Die Korrelation zwischen Satelliten- und Spektrometermessung betrug r=0.67. Für Satelliten- und Turmmessungen ergab sich ein Wert von r=0.5. Basierend auf den beschriebenen Vorarbeiten wurde die räumliche Variabilität von Landoberflächenparametern und Flüssen untersucht. Die unterschiedlichen räumlichen Auflösungen der Satelliten können genutzt werden, um zum einen die subskalige Heterogenität zu beschreiben, aber auch, um den Effekt räumlicher Mittelungsverfahren zu testen. Dafür wurden Parameter und Energieflüsse in Abhängigkeit der Landnutzungsklasse untersucht, um typische Verteilungsmuster dieser Größen zu finden. Die Verwendung der Verteilungsmuster (in Form von Wahrscheinlichkeitsdichteverteilungen – PDFs), die für die Albedo und den NDVI aus ETM+ Daten gefunden wurden, bietet ein hohes Potential als Modellinput, um repräsentative PDFs der Energieflüsse auf gröberen Skalen zu erhalten. Die ersten Ergebnisse in der Verwendung der PDFs von Albedo, NDVI, relativer Luftfeuchtigkeit und Windgeschwindigkeit für die Bestimmung von L.E waren sehr ermutigend und zeigten das hohe Potential der Methode. Zusammenfassend lässt sich feststellen, dass die Methode der Ableitung von Oberflächenparametern und Energieflüssen aus Satellitendaten zuverlässige Daten auf verschiedenen zeitlichen und räumlichen Skalen liefert. Die Daten sind für eine detaillierte Analyse der räumlichen Variabilität der Landschaft und für die Beschreibung der subskaligen Heterogenität, wie sie oft in Modellanwendungen benötigt wird, geeignet. Ihre Nutzbarkeit als Inputparameter in Modellen auf verschiedenen Skalen ist das zweite wichtige Ergebnis der Arbeit. Aus Satellitendaten abgeleitete Vegetationsparameter wie der LAI oder die Pflanzenbedeckung liefern realistische Ergebnisse, die zum Beispiel als Modellinput in das Lokalmodell des Deutschen Wetterdienstes implementiert werden konnten und die Modellergebnisse von L.E signifikant verbessert haben. Aber auch thermale Parameter, wie beispielsweise die Oberflächentemperatur aus ETM+ Daten in 30 m Auflösung, wurden als Eingabeparameter eines Soil-Vegetation-Atmosphere-Transfer-Modells (SVAT) verwendet. Dadurch erhält man realistischere Ergebnisse für L.E, die hochaufgelöste Flächeninformationen bieten.