Variations in Leaf Area Index (LAI) can greatly alter output values and patterns of various models that deal with energy flux exchange between the land surface and the atmosphere. Customarily, such models are initiated by LAI estimated from satellite-level Vegetation Indices (VIs) including routinely produced Normalized Difference Vegetation Index (NDVI) products. However, the accuracy from LAI-VI relationships greatly varies due to many factors, including temporal and spatial variations in LAI and a selected VI. In addition, NDVI products derived from various sensors have demonstrated variations in a certain degree on describing temporal and spatial variations in LAI, especially in semi-arid areas. This thesis therefore has three objectives: 1) determine a suitable VI for quantifying LAI temporal variation; 2) improve LAI estimation by considering both temporal and spatial variations in LAI; and 3) evaluate routinely produced NDVI products on monitoring temporal and spatial variations in LAI.<p> The study site was set up in conserved semi-arid mixed grassland in St. Denis, Saskatchewan, Canada. One 600 m - long sampling transect was set up across the rolling typography, and six plots with a size of 40 × 40 m each were randomly designed and each was in a relatively homogenous area. Plant Area Index (PAI, which was validated to obtain LAI), ground hyperspectral reflectance, ground covers (grasses, forbs, standing dead, litter, and bare soil), and soil moisture data were collected over the sampling transect and plots from May through September, 2008. Satellite data used are SPOT 4/5 images and 16-day Moderate Resolution Imaging Spectroradiometer (MODIS) 250m, 1km as well as 10-day SPOT-vegetation (SPOT-VGT) NDVI products from May to October, 2007 and 2008. The results show that NDVI is the most suitable VI for quantifying temporal variation of LAI. LAI estimation is much improved by considering both temporal and spatial variations. Based on the ground reflectance data, the r2 value is increased by 0.05, 0.31, and 0.23 and an averaged relative error is decreased by 1.57, 1.62, and 0.67 in the early, maximum, and late growing season, respectively. MODIS 250m NDVI products are the most useful datasets and MODIS 1km NDVI products are superior to SPOT-VGT 1km composites for monitoring intra-annual spatiotemporal variations in LAI. The proposed LAI estimation approach can be used in other studies to obtain more accurate LAI, and thus this research will be beneficial for grassland modeling.

LiDAR (Light Detection and Ranging) directly measures canopy vertical structures, and provides an effective remote sensing solution to accurate and spatiallyexplicit mapping of forest characteristics, such as canopy height and Leaf Area Index. However, many factors, such as large data volume and high costs for data acquisition, precludes the operational and practical use of most currently available LiDARs for frequent and large-scale mapping. At the same time, a growing need is arising for realtime remote sensing platforms, e.g., to provide timely information for urgent applications. This study aims to develop an airborne profiling LiDAR system, featured with on-the-fly data processing, for near real- or real- time forest inventory. The development of such a system involves implementing the on-board data processing and analysis as well as building useful regression-based models to relate LiDAR measurements with forest biophysical parameters. This work established a paradigm for an on-the-fly airborne profiling LiDAR system to inventory regional forest resources in real- or near real- time. The system was developed based on an existing portable airborne laser system (PALS) that has been previously assembled at NASA by Dr. Ross Nelson. Key issues in automating PALS as an on-the-fly system were addressed, including the design of an archetype for the system workflow, the development of efficient and robust algorithms for automatic data processing and analysis, the development of effective regression models to predict forest biophysical parameters from LiDAR measurements, and the implementation of an integrated software package to incorporate all the above development. This work exploited the untouched potential of airborne laser profilers for realtime forest inventory, and therefore, documented an initial step toward developing airborne-laser-based, on-the-fly, real-time, forest inventory systems. Results from this work demonstrated the utility and effectiveness of airborne scanning or profiling laser systems for remotely measuring various forest structural attributes at a range of scales, i.e., from individual tree, plot, stand and up to regional levels. The system not only provides a regional assessment tool, one that can be used to repeatedly, remotely measure hundreds or thousands of square kilometers with little/no analyst interaction or interpretation, but also serves as a paradigm for future efforts in building more advanced airborne laser systems such as real-time laser scanners.

Field experiments were conducted in Kentucky and Louisiana in 2008 and 2009 (split-plot in a randomized complete block design with four replications) to investigate it is possible to simulate with manual defoliation the effect of soybean rust (SBR) (Phakopsora pachyrhizi Syd. and P. Syd) injury on a healthy soybean [Glycine max, (L.) Merr.] canopy, understand how defoliation affects the growth dynamics and canopy light interception, and if defoliation affectsleaf senescence and nitrogen remobilization during the seed-filling period. Two manual defoliation treatments based on changes in effective leaf area index (ELAI) (calculated as the reduction in leaf area equivalent to SBR-induced premature leaf abscission, loss in green leaf area, and reduction in photosynthetic capacity of diseased leaves) in infected canopies in Brazil were used to simulate SBR infection at growth stage R2 (full flowering) and R5 (beginning of seed-fill). Both defoliation treatments reduced yield in all experiments and the reduction was larger for the treatments at growth stage R2. The yield losses were equivalent to that observed in infected soybean canopies in Brazil. This suggests that a system of manual defoliation to simulate changes in effective leaf area duration shows promise as a tool to simulate the impact of SBR on soybean yield. The radiation use efficiency and crop growth rate from growth stage R2 to R5 were not influenced by defoliation. Defoliation started at growth stage R2 reduced seed number per unit area, while defoliation started at growth stage R5 reduced seed size due to shortening the seed-fill duration and a lower seed growth rate. There is no evidence that manual defoliation affected leaf senescence or nitrogen redistribution to the seed. This study found that the reduction of light interception by SBR was the main reason for the reductions in soybean growth and yield.

Estimativas do rendimento da soja são informações importantes que podem auxiliar as instituições na tomada de decisão quanto à políticas de comercialização. São consideradas também, na liberação de recursos para o financiamento da produção e seguro agrícola. Modelos matemáticos, que se baseiam nas relações clima planta, denominados modelos agrometeorológicos, podem estimar o rendimento médio de grãos, através de dados meteorológicos e biofísicos da cultura, como exemplo: o IAF (Índice de área foliar) da soja pode ser associado ao Kc (Coeficiente de cultura) para estimar o estádio fenológico, e esta informação, ser associada à parametrização dos modelos. O objetivo geral deste trabalho foi avaliar as possibilidades de se utilizar imagens do sensor MODIS, para estimar parâmetros biofísicos da soja, aplicáveis à modelagem do rendimento de grãos. A área de estudo abrangeu a porção norte do estado do Rio Grande do Sul. Foram obtidas as imagens do IAF, MOD15A2, e dos índices de vegetação NDVI (Índice de vegetação por diferença normalizada) e NDWI (Índice de umidade por diferença normalizada), do produto MOD13Q1. Foram analisadas duas safras: 2003/04 e 2004/05. O período de safra foi de 15 de outubro até 30 de abril. Foram geradas máscaras de cultivo para as safras, e aplicadas sobre as imagens. Inicialmente, foram comparadas as médias do IAF, MOD15A2, às outras estimativas do IAF obtidas a partir de funções de relação com o NDVI, e analisados os diagramas de dispersão para cinco datas. Então foi estimado o Kc_1 com as médias do IAF, MOD15A2, conforme Martorano (2007), e comparadas ao Kc_2 ajustado conforme Matzenauer, 2002. Foi calculado o balanço hídrico meteorológico da cultura a fim de se obter o Índice de Satisfação das Necessidades de Água das plantas (ISNA), sendo ISNA_1 considerando o Kc_1, e o ISNA_2, o Kc_2. Foram obtidos os índices de correlação entre o ISNA_1 e 2 e os índices NDVI e NDWI, para o ciclo completo da cultura e para períodos de baixo ISNA. Como resultado, o IAF, MOD15A2 apresentou coerência com a evolução do IAF durante o ciclo, porém, em média, apresentou valores inferiores aos observados por Fontana et al. (1992) e Martorano (2007). Já como estimador do Kc, apresentou coeficientes de variação inferiores ao observado no Kc_2. Observou-se também que no IAF, MOD15A2 ocorriam valores superestimados do Kc entre a semeadura e o máximo desenvolvimento, e após, subestimados, o que potencializou períodos de déficit hídrico acentuado durante a floração e enchimento de grãos, em ambas as safras. Já, avaliando os índices de correlação, o ISNA_1, apresentou correlações de maior significância com os índices de vegetação do que o ISNA_2. Com isso podemos supor que, mesmo o IAF, MOD15A2 não tendo apresentado valores compatíveis com os da soja, demonstrou maior significância nas correlações, o que indica que imagens MODIS, podem gerar estimativas adequadas tanto do IAF, quanto do Kc, e também, representar adequadamente as condições hídricas. É recomendável avaliar estimativas do IAF da soja, a partir de suas relações com o NDVI, a fim de se obter resultados compatíveis com a soja. / Estimates of soybean yield are useful information that can assist institutions in decisions related by commercial policies. It is considered also in financing of production and agricultural insurance. Mathematical models, which are based in clime plant relationship known as Agrometeorological Models, can provide an estimate for grain yield through meteorological and biophysical data correlated with the culture, as an example: the LAI (Leaf Area Index) of soybean can be related with the Kc (Culture's coefficient) to estimate the phenological stage, and this information, applied to model's parameters. The main objective of this work was evaluated of possibilities of MODIS's images, in estimation of biophysical parameters, which are applicable in yield's modeling for soybean. The studying area was a portion of northern of State of Rio Grande do Sul. Were obtained images from LAI, MOD15A2, and vegetation indexes, NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) from MOD13Q1 product. Were analyzed two harvests: 2003/04 and 2004/05. The length of time for harvest was 15th October to 30th April. Soybean's areas was detected and masks were applied on the images. Firstly, were compared the means between LAI, MOD15A2 and two different methodologies of LAI's estimation based on relationship with NDVI. The scatter plots were discussed between LAI, MOD15A2 and other methodologies, for five key-dates. The culture's coefficient Kc_1 was estimated through LAI, MOD15A2, according to Martorano (2007), and compare with Kc_2, which were obtained from fitted culture's coefficient by Matzenauer (2002). The water balance were calculated aiming the index for Plant's water satisfaction needs (ISNA), which, ISNA_1 has considered the Kc_1, and ISNA_2, the Kc_2. Correlation indexes were obtained between ISNA_1 and 2, and vegetation indexes NDVI and NDWI, for the whole culture's cycle and specific periods of low ISNA. As results, LAI, MOD15A2, has presented coherence with soybean's cycle progress, however, as a rule, it presents lower values, comparing to Fontana et al. (1992) and Martorano (2007). Even now as a Kc's estimator, Kc_1 has presented lower variation's coefficient compared to Kc_2. Was observed also overrated in Kc_1 between the sowing to maximum development, and after, underestimated, what provokes periods of accented hydric deficiency during flowering and grain forming stages, in both harvests. Now, as to correlation's indexes, we observed more significance correlations between both vegetation's indexes and ISNA_1. This way, these results has indicated that remote sensing images can provide accurate estimates of IAF as much as Kc, and also indicate the hydric conditions of plants. It is recommended to improve the LAI's mean values on the images, exploring the relationship with NDVI, looking for adequate values for the case of soybean.

A evapotranspiração (ET) é um fenômeno natural que influencia diretamente mudanças no clima local e global, possuindo grande importância hidrológica e meteorológica. Este trabalho teve como objetivo estimar a ET através do método Penman-Monteith, e comparar com os resultados estimados pelo Método da Covariância de Vórtices Turbulentos. Os dados de IAF (Índice de Área Foliar) para descrição da vegetação foram obtidos a partir do sensor MODIS e de medições em campo da Rede SulFlux. A área de estudo desta pesquisa localizou-se no município de Cachoeira do Sul-RS, em uma propriedade de cultivo de arroz irrigado. O período de estudo referiu-se a safra que estendeu-se de outubro de 2010 a março de 2011. Os resultados mostraram que o IAF e as estimativas de ET apresentam um comportamento temporal semelhante. A comparação entre os resultados das estimativas de ET, utilizando dados obtidos em campo e estimados através de sensoriamento remoto, foram satisfatórios. No entanto, o resultado que apresentou os maiores valores para a ET foi proveniente do sensor MODIS. Sendo assim, pode-se concluir que a estimativa da ET, a partir de dados de vegetação, obtidos através de técnicas de sensoriamento remoto, constituem-se como uma alternativa para os métodos de ET que utilizam dados de vegetação medidos em campo. / The Evapotranspiration (ET) is a natural phenomenon that directly causes changes in the local and global climate, having a great hydrologic and meteorological importance. This work has as objective to estimate ET through the Penman-Monteith method and compare with the estimated results by the Eddy Covariance Method. The LAI (Leaf Area Index) data for the vegetation description were obtained from the MODIS sensor and from field measurements of the SulFlux network. The field of study of this research had place in the city of Cachoeira do Sul – Rio Grande do Sul state, in a irrigated rice crop property. The study period is referred to the crop that extended from October 2010 to March 2011. The results showed that the LAI and the ET estimates have a similar temporal behavior. The comparison between the results of ET estimates, using data obtained in field and estimated through remote sensing, were satisfactory. However, the result that showed the highest values for ET was from the MODIS sensor. This way, it can be concluded that the ET estimate, through vegetation data, obtained by remote sensing techniques, are an alternative for the ET methods that use vegetation field measured data.

A evapotranspiração (ET) é um fenômeno natural que influencia diretamente mudanças no clima local e global, possuindo grande importância hidrológica e meteorológica. Este trabalho teve como objetivo estimar a ET através do método Penman-Monteith, e comparar com os resultados estimados pelo Método da Covariância de Vórtices Turbulentos. Os dados de IAF (Índice de Área Foliar) para descrição da vegetação foram obtidos a partir do sensor MODIS e de medições em campo da Rede SulFlux. A área de estudo desta pesquisa localizou-se no município de Cachoeira do Sul-RS, em uma propriedade de cultivo de arroz irrigado. O período de estudo referiu-se a safra que estendeu-se de outubro de 2010 a março de 2011. Os resultados mostraram que o IAF e as estimativas de ET apresentam um comportamento temporal semelhante. A comparação entre os resultados das estimativas de ET, utilizando dados obtidos em campo e estimados através de sensoriamento remoto, foram satisfatórios. No entanto, o resultado que apresentou os maiores valores para a ET foi proveniente do sensor MODIS. Sendo assim, pode-se concluir que a estimativa da ET, a partir de dados de vegetação, obtidos através de técnicas de sensoriamento remoto, constituem-se como uma alternativa para os métodos de ET que utilizam dados de vegetação medidos em campo. / The Evapotranspiration (ET) is a natural phenomenon that directly causes changes in the local and global climate, having a great hydrologic and meteorological importance. This work has as objective to estimate ET through the Penman-Monteith method and compare with the estimated results by the Eddy Covariance Method. The LAI (Leaf Area Index) data for the vegetation description were obtained from the MODIS sensor and from field measurements of the SulFlux network. The field of study of this research had place in the city of Cachoeira do Sul – Rio Grande do Sul state, in a irrigated rice crop property. The study period is referred to the crop that extended from October 2010 to March 2011. The results showed that the LAI and the ET estimates have a similar temporal behavior. The comparison between the results of ET estimates, using data obtained in field and estimated through remote sensing, were satisfactory. However, the result that showed the highest values for ET was from the MODIS sensor. This way, it can be concluded that the ET estimate, through vegetation data, obtained by remote sensing techniques, are an alternative for the ET methods that use vegetation field measured data.

Estimativas do rendimento da soja são informações importantes que podem auxiliar as instituições na tomada de decisão quanto à políticas de comercialização. São consideradas também, na liberação de recursos para o financiamento da produção e seguro agrícola. Modelos matemáticos, que se baseiam nas relações clima planta, denominados modelos agrometeorológicos, podem estimar o rendimento médio de grãos, através de dados meteorológicos e biofísicos da cultura, como exemplo: o IAF (Índice de área foliar) da soja pode ser associado ao Kc (Coeficiente de cultura) para estimar o estádio fenológico, e esta informação, ser associada à parametrização dos modelos. O objetivo geral deste trabalho foi avaliar as possibilidades de se utilizar imagens do sensor MODIS, para estimar parâmetros biofísicos da soja, aplicáveis à modelagem do rendimento de grãos. A área de estudo abrangeu a porção norte do estado do Rio Grande do Sul. Foram obtidas as imagens do IAF, MOD15A2, e dos índices de vegetação NDVI (Índice de vegetação por diferença normalizada) e NDWI (Índice de umidade por diferença normalizada), do produto MOD13Q1. Foram analisadas duas safras: 2003/04 e 2004/05. O período de safra foi de 15 de outubro até 30 de abril. Foram geradas máscaras de cultivo para as safras, e aplicadas sobre as imagens. Inicialmente, foram comparadas as médias do IAF, MOD15A2, às outras estimativas do IAF obtidas a partir de funções de relação com o NDVI, e analisados os diagramas de dispersão para cinco datas. Então foi estimado o Kc_1 com as médias do IAF, MOD15A2, conforme Martorano (2007), e comparadas ao Kc_2 ajustado conforme Matzenauer, 2002. Foi calculado o balanço hídrico meteorológico da cultura a fim de se obter o Índice de Satisfação das Necessidades de Água das plantas (ISNA), sendo ISNA_1 considerando o Kc_1, e o ISNA_2, o Kc_2. Foram obtidos os índices de correlação entre o ISNA_1 e 2 e os índices NDVI e NDWI, para o ciclo completo da cultura e para períodos de baixo ISNA. Como resultado, o IAF, MOD15A2 apresentou coerência com a evolução do IAF durante o ciclo, porém, em média, apresentou valores inferiores aos observados por Fontana et al. (1992) e Martorano (2007). Já como estimador do Kc, apresentou coeficientes de variação inferiores ao observado no Kc_2. Observou-se também que no IAF, MOD15A2 ocorriam valores superestimados do Kc entre a semeadura e o máximo desenvolvimento, e após, subestimados, o que potencializou períodos de déficit hídrico acentuado durante a floração e enchimento de grãos, em ambas as safras. Já, avaliando os índices de correlação, o ISNA_1, apresentou correlações de maior significância com os índices de vegetação do que o ISNA_2. Com isso podemos supor que, mesmo o IAF, MOD15A2 não tendo apresentado valores compatíveis com os da soja, demonstrou maior significância nas correlações, o que indica que imagens MODIS, podem gerar estimativas adequadas tanto do IAF, quanto do Kc, e também, representar adequadamente as condições hídricas. É recomendável avaliar estimativas do IAF da soja, a partir de suas relações com o NDVI, a fim de se obter resultados compatíveis com a soja. / Estimates of soybean yield are useful information that can assist institutions in decisions related by commercial policies. It is considered also in financing of production and agricultural insurance. Mathematical models, which are based in clime plant relationship known as Agrometeorological Models, can provide an estimate for grain yield through meteorological and biophysical data correlated with the culture, as an example: the LAI (Leaf Area Index) of soybean can be related with the Kc (Culture's coefficient) to estimate the phenological stage, and this information, applied to model's parameters. The main objective of this work was evaluated of possibilities of MODIS's images, in estimation of biophysical parameters, which are applicable in yield's modeling for soybean. The studying area was a portion of northern of State of Rio Grande do Sul. Were obtained images from LAI, MOD15A2, and vegetation indexes, NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) from MOD13Q1 product. Were analyzed two harvests: 2003/04 and 2004/05. The length of time for harvest was 15th October to 30th April. Soybean's areas was detected and masks were applied on the images. Firstly, were compared the means between LAI, MOD15A2 and two different methodologies of LAI's estimation based on relationship with NDVI. The scatter plots were discussed between LAI, MOD15A2 and other methodologies, for five key-dates. The culture's coefficient Kc_1 was estimated through LAI, MOD15A2, according to Martorano (2007), and compare with Kc_2, which were obtained from fitted culture's coefficient by Matzenauer (2002). The water balance were calculated aiming the index for Plant's water satisfaction needs (ISNA), which, ISNA_1 has considered the Kc_1, and ISNA_2, the Kc_2. Correlation indexes were obtained between ISNA_1 and 2, and vegetation indexes NDVI and NDWI, for the whole culture's cycle and specific periods of low ISNA. As results, LAI, MOD15A2, has presented coherence with soybean's cycle progress, however, as a rule, it presents lower values, comparing to Fontana et al. (1992) and Martorano (2007). Even now as a Kc's estimator, Kc_1 has presented lower variation's coefficient compared to Kc_2. Was observed also overrated in Kc_1 between the sowing to maximum development, and after, underestimated, what provokes periods of accented hydric deficiency during flowering and grain forming stages, in both harvests. Now, as to correlation's indexes, we observed more significance correlations between both vegetation's indexes and ISNA_1. This way, these results has indicated that remote sensing images can provide accurate estimates of IAF as much as Kc, and also indicate the hydric conditions of plants. It is recommended to improve the LAI's mean values on the images, exploring the relationship with NDVI, looking for adequate values for the case of soybean.

Estimativas do rendimento da soja são informações importantes que podem auxiliar as instituições na tomada de decisão quanto à políticas de comercialização. São consideradas também, na liberação de recursos para o financiamento da produção e seguro agrícola. Modelos matemáticos, que se baseiam nas relações clima planta, denominados modelos agrometeorológicos, podem estimar o rendimento médio de grãos, através de dados meteorológicos e biofísicos da cultura, como exemplo: o IAF (Índice de área foliar) da soja pode ser associado ao Kc (Coeficiente de cultura) para estimar o estádio fenológico, e esta informação, ser associada à parametrização dos modelos. O objetivo geral deste trabalho foi avaliar as possibilidades de se utilizar imagens do sensor MODIS, para estimar parâmetros biofísicos da soja, aplicáveis à modelagem do rendimento de grãos. A área de estudo abrangeu a porção norte do estado do Rio Grande do Sul. Foram obtidas as imagens do IAF, MOD15A2, e dos índices de vegetação NDVI (Índice de vegetação por diferença normalizada) e NDWI (Índice de umidade por diferença normalizada), do produto MOD13Q1. Foram analisadas duas safras: 2003/04 e 2004/05. O período de safra foi de 15 de outubro até 30 de abril. Foram geradas máscaras de cultivo para as safras, e aplicadas sobre as imagens. Inicialmente, foram comparadas as médias do IAF, MOD15A2, às outras estimativas do IAF obtidas a partir de funções de relação com o NDVI, e analisados os diagramas de dispersão para cinco datas. Então foi estimado o Kc_1 com as médias do IAF, MOD15A2, conforme Martorano (2007), e comparadas ao Kc_2 ajustado conforme Matzenauer, 2002. Foi calculado o balanço hídrico meteorológico da cultura a fim de se obter o Índice de Satisfação das Necessidades de Água das plantas (ISNA), sendo ISNA_1 considerando o Kc_1, e o ISNA_2, o Kc_2. Foram obtidos os índices de correlação entre o ISNA_1 e 2 e os índices NDVI e NDWI, para o ciclo completo da cultura e para períodos de baixo ISNA. Como resultado, o IAF, MOD15A2 apresentou coerência com a evolução do IAF durante o ciclo, porém, em média, apresentou valores inferiores aos observados por Fontana et al. (1992) e Martorano (2007). Já como estimador do Kc, apresentou coeficientes de variação inferiores ao observado no Kc_2. Observou-se também que no IAF, MOD15A2 ocorriam valores superestimados do Kc entre a semeadura e o máximo desenvolvimento, e após, subestimados, o que potencializou períodos de déficit hídrico acentuado durante a floração e enchimento de grãos, em ambas as safras. Já, avaliando os índices de correlação, o ISNA_1, apresentou correlações de maior significância com os índices de vegetação do que o ISNA_2. Com isso podemos supor que, mesmo o IAF, MOD15A2 não tendo apresentado valores compatíveis com os da soja, demonstrou maior significância nas correlações, o que indica que imagens MODIS, podem gerar estimativas adequadas tanto do IAF, quanto do Kc, e também, representar adequadamente as condições hídricas. É recomendável avaliar estimativas do IAF da soja, a partir de suas relações com o NDVI, a fim de se obter resultados compatíveis com a soja. / Estimates of soybean yield are useful information that can assist institutions in decisions related by commercial policies. It is considered also in financing of production and agricultural insurance. Mathematical models, which are based in clime plant relationship known as Agrometeorological Models, can provide an estimate for grain yield through meteorological and biophysical data correlated with the culture, as an example: the LAI (Leaf Area Index) of soybean can be related with the Kc (Culture's coefficient) to estimate the phenological stage, and this information, applied to model's parameters. The main objective of this work was evaluated of possibilities of MODIS's images, in estimation of biophysical parameters, which are applicable in yield's modeling for soybean. The studying area was a portion of northern of State of Rio Grande do Sul. Were obtained images from LAI, MOD15A2, and vegetation indexes, NDVI (Normalized Difference Vegetation Index) and NDWI (Normalized Difference Water Index) from MOD13Q1 product. Were analyzed two harvests: 2003/04 and 2004/05. The length of time for harvest was 15th October to 30th April. Soybean's areas was detected and masks were applied on the images. Firstly, were compared the means between LAI, MOD15A2 and two different methodologies of LAI's estimation based on relationship with NDVI. The scatter plots were discussed between LAI, MOD15A2 and other methodologies, for five key-dates. The culture's coefficient Kc_1 was estimated through LAI, MOD15A2, according to Martorano (2007), and compare with Kc_2, which were obtained from fitted culture's coefficient by Matzenauer (2002). The water balance were calculated aiming the index for Plant's water satisfaction needs (ISNA), which, ISNA_1 has considered the Kc_1, and ISNA_2, the Kc_2. Correlation indexes were obtained between ISNA_1 and 2, and vegetation indexes NDVI and NDWI, for the whole culture's cycle and specific periods of low ISNA. As results, LAI, MOD15A2, has presented coherence with soybean's cycle progress, however, as a rule, it presents lower values, comparing to Fontana et al. (1992) and Martorano (2007). Even now as a Kc's estimator, Kc_1 has presented lower variation's coefficient compared to Kc_2. Was observed also overrated in Kc_1 between the sowing to maximum development, and after, underestimated, what provokes periods of accented hydric deficiency during flowering and grain forming stages, in both harvests. Now, as to correlation's indexes, we observed more significance correlations between both vegetation's indexes and ISNA_1. This way, these results has indicated that remote sensing images can provide accurate estimates of IAF as much as Kc, and also indicate the hydric conditions of plants. It is recommended to improve the LAI's mean values on the images, exploring the relationship with NDVI, looking for adequate values for the case of soybean.

Leaves in forests assimilate carbon from the atmosphere and woody components store the net production of that assimilation. Separate structure measurements of leaves and woody components advance the monitoring and modeling of forest ecosystem functions. This dissertation provides a method to determine, for the first time, the 3-D spatial arrangement and the amount of leafy and woody materials separately in a forest by classification of lidar returns from a new, innovative, lidar scanner, the Dual-Wavelength Echidna® Lidar (DWEL). The DWEL uses two lasers pulsing simultaneously and coaxially at near-infrared (1064 nm) and shortwave-infrared (1548 nm) wavelengths to locate scattering targets in 3-D space, associated with their reflectance at the two wavelengths. The instrument produces 3-D bispectral "clouds" of scattering points that reveal new details of forest structure and open doors to three-dimensional mapping of biophysical and biochemical properties of forests. The three parts of this dissertation concern calibration of bispectral lidar returns; retrieval of height profiles of leafy and woody materials within a forest canopy; and virtual reconstruction of forest trees from multiple scans to estimate their aboveground woody biomass. The test area was a midlatitude forest stand within the Harvard Forest, Petersham, Massachusetts, scanned at five locations in a 1-ha site in leaf-off and leaf-on conditions in 2014. The model for radiometric calibration assigned accurate values of spectral apparent reflectance, a range-independent and instrument-independent property, to scattering points derived from the scans. The classification of leafy and woody points, using both spectral and spatial context information, achieved an overall accuracy of 79±1% and 75±2% for leaf-off and leaf-on scans, respectively. Between-scan variation in leaf profiles was larger than wood profiles in leaf-off seasons but relatively similar to wood profiles in leaf-on seasons, reflecting the changing spatial heterogeneity within the stand over seasons. A 3-D structure-fitting algorithm estimated wood volume by modeling stems and branches from point clouds of five individual trees with cylinders. The algorithm showed the least variance for leaf-off, woody-points-only data, validating the value of separating leafy and woody points to the direct biomass estimates through the structure modeling of individual trees.

Vegetation controls the exchange of heat, mass and momentum between the land surface and the atmosphere, and is also the primary producer that sustains life on Earth. We combine theoretical analyses, satellite and in-situ observations, and Earth system model simulations in this dissertation to illustrate the key role of vegetation in the climate system and human society. Specifically, this is accomplished via three studies, described below. First, we address the problem of how to retrieve Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FPAR) from a novel satellite Bidirectional Reflectance Factor product derived from the Multi-Angle Implementation of Atmospheric Correction algorithm. The LAI/FPAR retrieval is done via a radiative transfer model using the recently developed theory of spectral invariants. Our analyses show that the LAI/FPAR data sets developed in this study have higher accuracy and better stability relative to the existing products, especially in cloudy conditions and under high aerosol loadings. Second, we analyze the long-term trend in LAI derived from the Moderate Resolution Imaging Spectroradiometer observations and identify its main driver. We find that over a third of the terrestrial vegetation shows statistically significant increasing trends in LAI (i.e., Earth greening) during the 21st century. Both remote sensing and inventory data show that land-use management is the key driver of this greening, arising primarily from large-scale tree planting and intensive agriculture in emerging countries like China and India. This finding highlights the need for a more realistic representation of land-use practices in Earth system models. Third, we use a new method based on the concept of “two-resistances” and the Community Land Model (CLM5) runs with prescribed satellite-derived LAI to quantify the impacts of Earth greening on land surface temperature (LST). We find that over 90% of the Earth greening can lead to a local cooling effect at the annual scale. Further attribution analysis with multiple data sources reveals that aerodynamic resistance is the dominant factor controlling the LST change. The greening produces a decrease in aerodynamic resistance, which favors increased heat dissipation by turbulent fluxes, including the latent heat flux. These studies that span LAI data production, long-term trends and their impacts highlight the importance of vegetation dynamics in the natural and human systems.