Abstract One of the major challenges for effective agricultural activity monitoring systems is defining robust indicators of spatial and temporal variability for the main risk factors associated with crop production. In this context, this study aimed to analyze the potential of the Temperature-Vegetation Dryness Index (TVDI), obtained by terrestrial and orbital sensors from soybean production areas in southern Brazil, in generating spatial and temporal patterns of the main risk factor, surface moisture, to be incorporated in operational agricultural monitoring systems. For this purpose, MODIS Terra and Landsat-8 OLI/TIRS sensor images were used, as well as data from surface positioned sensors to serve as a reference. The study area encompassed one soybean crop area, soybean mapped crop areas near the experimental area, and the municipality of Carazinho-RS. The experimental area was analyzed during the soybean growing season. As the TVDI data estimated by OLI/TIRS and MODIS sensors were coherent and robust, both sensors can be used in conjunction for agricultural risk monitoring. Its main features are continuous monitoring of large production regions by TVDIMODIS and spatial distribution detailing by TVDIOLI/TIRS in critical periods to water deficit.

Resumo Um dos importantes desafios para construção de sistemas eficazes de monitoramento agrícola é a definição de indicadores que representem a variabilidade espacial e temporal dos principais fatores de risco associados à produção. Neste contexto, se analisou a potencialidade de uso do TVDI (Temperature-Vegetation Dryness Index), obtido a partir dos sensores terrestres e orbitais em áreas de produção de soja no sul do Brasil, na geração dos padrões espaciais e temporais do principal fator de risco, a umidade da superfície, visando sua incorporação em sistemas operacionais de monitoramento agrícola. Utilizou-se imagens dos sensores MODIS Terra e OLI/TIRS Landsat 8, e dados de sensores posicionados na superfície que serviram como referência. A área de estudo abrangeu lavoura de soja, onde foi conduzido o experimento, lavouras de soja próximas e o município de Carazinho/RS. O período de análise foi o período de cultivo da soja. Dada a coerência e robustez do TVDI estimado pelos sensores orbitais, verificou-se que é possível o uso conjugado destes sensores em sistema de monitoramento de risco agrícola. A principal característica é o acompanhamento contínuo da umidade em amplas regiões agrícolas usando TVDIMODIS e detalhamento da distribuição espacial da umidade da superfície com TVDIOLI/TIRS nos períodos críticos à deficiência hídrica.

ABSTRACT: The extension of the area occupied by the inter tussock stratum and tussock stratum in natural pastures is essential for the productive performance of grazing animals. Images obtained from unmanned remote sensors can provide useful information, especially because they have a high spatial resolution. Thus, this study evaluated the performance of the supervised adaptive classification applied to aerial images obtained from an onboard drone camera to map the area covered by tussocks in a natural pasture of the Pampa biome. The study was carried out in a natural pasture area managed since 1986 under different forage allowances, considering treatments of 8, 12, and 16 kg of dry matter per 100 kg live weight (% LW). An aerial image from September 2017, obtained with a Canon S100 camera onboard a drone at an altitude of 120 m, with a spatial resolution of 5 cm, was used. The random forest and support vector machine classifiers were tested associated with specific classification rules. False-color images showed considerable visual similarity in the large patterns of the vegetation distribution and the validation performed with independent samples when compared to the classified images. The tested classifiers were able to measure the area covered by the tussock stratum, which could be an indicator of the quality vegetation in a natural grassland of the Pampa biome.

RESUMO: A quantidade de área ocupada por estrato inferior e superior em pastagens naturais tem grande importância sobre o desempenho produtivo dos animais em pastejo. Imagens obtidas de sensores remotos não tripulados podem fornecer informações úteis, especialmente por possuírem alta resolução espacial. O objetivo deste trabalho foi avaliar o desempenho de classificação supervisionada adaptativa aplicada a imagem aérea obtida por câmera a bordo de drone, no mapeamento da área coberta por touceiras em pastagem natural do bioma Pampa. O estudo foi realizado em área de pastagem natural, manejada desde 1986 sob diferentes ofertas de forragem, tendo sido considerados os tratamentos 8, 12 e 16 kg de matéria seca por 100 kg de peso vivo (% PV). Foi utilizada uma imagem aérea, de setembro de 2017, obtida com uma câmera Canon S100, a bordo de um drone a 120 m de altitude, correspondendo a resolução espacial de 5 cm. Foram testados dois classificadores, Random Forest e Support Vector Machine associados a regras específicas de classificação. As imagens de falsa cor, quando comparadas às imagens classificadas, apresentaram considerável semelhança visual nos grandes padrões de distribuição da vegetação, bem como na validação feita com amostras independentes. Os classificadores testados foram capazes de mensurar a área coberta por estrato superior, podendo ser um indicador da qualidade da vegetação, em pastagem natural do bioma Pampa.

Resumo O objetivo deste trabalho foi avaliar o uso dos dados de umidade da superfície gerados pelo índice “temperature-vegetation dryness index” (TVDI), obtido de sensores posicionados na superfície de uma área agrícola de soja (Glycine max). Os limites seco e úmido do índice foram obtidos com uso de imagens Landsat-8 que abrangem a região em torno da lavoura. Para avaliar a qualidade e a coerência do TVDI, foi realizada análise de correlação entre o TVDI, o “normalized difference vegetation index” (NDVI), a temperatura da superfície (TS), e as variáveis usualmente utilizadas para expressar a umidade da superfície. O TVDI mostrou correlação significativa com umidade do solo, armazenamento de água no solo, deficit hídrico, evapotranspiração real e relação evapotranspiração real/evapotranspiração potencial. O deslocamento dos dados radiométricos medidos na superfície (NDVI e TS) dentro do triângulo evaporativo, ajustado com dados orbitais, descreve de forma consistente a variabilidade das condições hídricas no período de estudo, sendo uma ferramenta para apoio à tomada de decisões no manejo de culturas.

Abstract The objective of this work was to evaluate the use of the surface moisture data generated by the temperature-vegetation dryness index (TVDI), obtained from sensors positioned on the surface of a soybean (Glycine max) agricultural field. The dry and wet limits of the index were obtained using Landsat-8 images covering the region around the crop. To assess the quality and consistency of the TVDI, a correlation analysis was carried out between the TVDI, the normalized difference vegetation index (NDVI), surface temperature (TS), and the variables that are usually used to express surface moisture. The TVDI showed a significant correlation with soil moisture, water storage in the soil, water deficit, real evapotranspiration, and the real evapotranspiration/potential evapotranspiration ratio. The displacement of radiometric data measured on the surface (NDVI and TS) within the evaporative triangle, adjusted with orbital data, consistently describes the variability of water conditions during the study period, being a tool to support decision-making in crop management.

RESUMO A Produtividade Primária Líquida (NPP) pode ser utilizada para quantificar o papel relativo do clima e das atividades humanas na dinâmica da vegetação. Dada sua importância, muitos modelos de estimativa de NPP foram desenvolvidos, mas parte dos dados requeridos, ainda são limitados. Diante disso, este trabalho teve como objetivo estimar a NPP potencial e real testando diferentes abordagens quanto a fonte dos dados e métodos de estimativa, assim como, avaliar a apropriação humana da NPP em uma lavoura de soja cultivada no Sul do Brasil. Para isso, foram obtidos dados de NPP medida a campo em cultivo de soja em Carazinho, no Rio Grande do Sul, e comparados às estimativas de NPP potencial e NPP real, utilizando o modelo CASA e dados do ERA-Interim. Posteriormente, com a NPP potencial e real foram avaliadas as mudanças causadas pelo uso da terra em função das atividades agrícolas, através da Apropriação Humana da NPP (HANPP). Verificou-se que não houve diferença significativa associadas às fontes de dados utilizadas, evidenciando que os dados meteorológicos de reanálise do ERA-Interim podem ser utilizados para esse fim. As estimativas da NPP real pelo modelo CASA foram consistentes com elevada associação aos dados medidos a campo. A HANPP por meio de apenas um cultivo anual de soja, representou 29% do potencial de NPP na região. Isso indica que há potencial para elevar a intensificação com cultivos anuais na região.

ABSTRACT Net primary productivity (NPP) can be used to quantify the relative role of climate and human activities in vegetation dynamics. Given its importance, many NPP estimation models have been developed, but some of the required data is still limited. Therefore, this study aimed to estimate the potential and actual NPP by testing different approaches regarding the data source and estimation methods and evaluate the human appropriation of NPP (HANPP) in a soybean field cultivated in southern Brazil. For this, data were obtained from field-measured NPP in soybean cultivation in Carazinho, Rio Grande do Sul, Brazil, and compared to the potential and actual NPP estimations using the CASA model and data from ERA-Interim. Subsequently, land use changes due to agricultural activities were evaluated from the potential and actual NPP through HANPP. No significant difference was observed associated with the used data sources, showing that the ERA-Interim reanalysis weather data can be employed for this purpose. The actual NPP estimations by the CASA model were consistent with a high association with the data measured in the field. HANPP, through only one annual soybean cultivation, represented 29% of the potential NPP in the region. It indicates the potential to increase intensification with annual crops in the region.

Resumo O objetivo do estudo foi analisar a variabilidade no TVDI (Temperature-Vegetation Dryness Index) obtido de sensores orbitais com resoluções distintas, em região agrícola no sul do Brasil. Utilizou-se três imagens OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) do satélite Landsat 8, e 12 imagens MODIS (Moderate Resolution Imaging Spectroradiometer) do satélite Terra. Dados coletados em campo serviram como base para classificação de imagem OLI/TIRS e mapeamento de áreas de arroz, soja, campos naturais, mata ciliar e solo exposto. O TVDI foi obtido por duas parametrizações em períodos distintos, utilizando as dispersões entre Temperatura de Superfície (TS) e NDVI (Normalized Difference Vegetation Index). O TVDI obtido para ambos sensores apresentou padrão similar possibilitando diferenciar os alvos. Na média de todas as datas e classes, o TVDI obtido das imagens MODIS foi superior em 0,128 unidades ao TVDI obtido com o OLI/TIRS. Quando utilizado OLI/TIRS há um melhor detalhamento espacial das condições hídricas, mas com menor repetição ao longo da safra; já utilizando o TVDI-MODIS é possível monitorar as condições hídricas em escala regional, com menor detalhamento espacial, mas com maior repetitividade no tempo. O TVDI estimado pelos sensores OLI/TIRS e MODIS, pode ser utilizado de forma conjunta, trazendo informações complementares.

Abstract The aim of the study was to analyze the variability in the TVDI (Temperature-Vegetation Dryness Index) obtained from orbital sensors with distinct resolution in an agricultural region in southern Brazil. Three images of the Landsat 8 satellite OLI/TIRS (Operational Land Imager/Thermal Infrared Sensor) and 12 of Terra’s MODIS (Moderate Resolution Imaging Spectroradiometer), were used. Data collected in the field served as a basis for OLI/TIRS image classification and mapping of rice, soybean, grasslands, gallery forest and bare soil areas. The TVDI obtained by two parameterizations at different periods was evaluated, using the dispersions between Surface Temperature (TS) and NDVI (Normalized Difference Vegetation Index). The TVDI obtained for both sensors presented a similar pattern allowing differentiation of the targets. The average of all dates and classes, TVDI obtained from MODIS was 0.128 units higher than the obtained with the OLI/TIRS. When used OLI/TIRS there is a better detail in the representation of the moisture, but with less repetition throughout the crop cycle. Using TVDI-MODIS, it is possible to monitor moisture conditions on a regional scale, with less spatial detail, but in a continuous way over time. The TVDI estimated by the OLI/TIRS and MODIS can be used together, providing complementary information.

ABSTRACT In vineyards, the monitoring of leaf area is used to guide management practices in order to produce grapes with quality and enological potential. Remote sensing has been used to estimate leaf area by means of vegetation indices. This work aimed at determining the temporal evolution of the Normalized Difference Vegetation Index (NDVI) while associating it with leaf area of vineyards in the Serra Gaúcha region, Brazil. To this end, between 10 and 20 plants were evaluated monthly (September to May) in three vegetative seasons. NDVI data were obtained using the remote sensor Greenseeker while the following leaf area related parameters were obtained non-destructively: unit leaf area (ULA), average number of leaves per branch (NLB), plant leaf area (PLA), leaf area index (LAI) and chlorophyll index (CI). Data correlation was assessed by joint temporal analysis and Pearson correlations (p < 0.05). The results indicated that NDVI variation over time is coherent with the leaf area dynamics and expresses biomass variations due to phenology and management actions. The NDVI evolution pattern is described by a quadratic regression equation: the index increases rapidly at first (September to November), followed by a relative stabilization (December to February), and decreases in the final stage (March to May). The correlations were higher in the initial and final stages of the cycle, especially for NLB (r = 0.73 and 0.77), PLA (r = 0.60 and 0.61) and CI (r = 0.48 and 0.84), which are promising parameters for characterizing leaf area in vineyards by remote sensing.

ABSTRACT: This study aimed to characterize the average seasonal pattern of the vegetation in southern grassland in Brazil, and the variability found in the time series of vegetation indices. It also sought to identify similarities in the seasonal pattern of different grassland typologies. Moderate Resolution Imaging Spectroradiometer (MODIS), Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) images from Feb to Dec, 2000 to 2014 were analyzed for ten regions. The grassland typologies studied showed EVI and NDVI profiles consistent with the seasonal dynamics of grassland vegetation under the influence of a subtropical climate, with highest values in the indices during the warm seasons of the year (spring and summer) and lowest in the colder seasons (autumn and winter). Considering the values of EVI and NDVI, grassland typologies were allocated to four groups with similar temporal profiles. Among the groups formed from the EVI index it is possible to identify differences between grassland typologies during the autumn and winter, while the NDVI showed differences only in winter as compared to the other seasons.

ABSTRACT This study evaluated the adequacy of the One-Source Energy Balance (OSEB) and Surface Energy Balance Algorithm for Land (SEBAL) models to estimate evapotranspiration in grain growing areas with humid subtropical climate in Rio Grande do Sul, southern Brazil. The dataset was obtained from a micrometeorological station (Eddy Covariance) and MODIS (Moderate Resolution Imaging Spectroradiometer) products during 84 observation days between 2009 and 2011. The OSEB and SEBAL models were used to estimate the partition of net radiation (Rn) into the components latent heat flux (LE), sensible heat flux (H), and ground heat flux (G) estimated from the MODIS images while the experimental data measured in situ were used to compare the results. Analyses indicated that the Energy Balance (EB) components were estimated from the MODIS images satisfactorily despite the spatial resolution limitations. Furthermore, the SEBAL model estimated the EB components satisfactorily only for summer crops. There are uncertainties associated with determining the hot and cold pixels due to the region humid subtropical climate and the spatial resolution of the sensor used in the other periods. The OSEB model has the lowest errors and the most adequate partitioning of the EB components throughout the year, and therefore, it is the most suitable for the climatic conditions of Rio Grande do Sul.

Resumo: O objetivo deste trabalho foi localizar e quantificar, por meio de geotecnologias, áreas atualmente utilizadas em agricultura anual no bioma Cerrado do Brasil Central, localizadas no interior dos contornos definidos pela região homogênea de adaptação de cultivares de trigo 4 e que apresentem aptidão favorável para o cultivo de trigo (Triticum aestivum) no regime sequeiro. Foram realizados cruzamentos dos seguintes planos de informação: uso e cobertura das terras do bioma Cerrado, modelo digital de elevação e índice de satisfação de necessidades de água para trigo no bioma Cerrado. Também foram considerados níveis de impacto de estresse hídrico (baixo, moderado e elevado), níveis de risco (20, 30 e 40%), capacidades de armazenamento de água disponível (CAD) no solo (35, 55 e 75 mm), ciclos de cultivares (105, 115 e 125 dias) e semeaduras (em fevereiro e março). Há maior área favorável para semeaduras no começo de fevereiro, e o grupo I de cultivares (105 dias) apresentou a maior área favorável. Acima de 800 m de altitude, 2,7 milhões de hectares foram classificados como favoráveis para a melhor combinação de fatores, isto é, semeadura em 5 de fevereiro, CAD de 75 mm, ciclo de 105 dias, nível de risco de 20%, e impactos baixo e moderado.

Abstract: The objective of this work was to locate and quantify, through geotechnologies, areas that are currently used for annual agriculture in the Cerrado biome of Central Brazil, located within the boundaries defined by the homogeneous region of adaptation of wheat cultivars 4 and that present favorable conditions for rainfed wheat (Triticum aestivum) cultivation. The following information layers were crossed: use and coverage of the Cerrado biome, digital elevation model, and water requirement satisfaction index for wheat in the Cerrado biome. In addition, different levels of water stress (low, moderate, and high), risk levels (20, 30, and 40%), available soil water capacities (ASWCs) (35, 55, and 75 mm), cultivar cycles (105, 115, and 125 days), and sowing dates (in February and March) were also considered. A greater favorable area was observed for sowing in early February, and group I of cultivars (105 days) presented the greatest favorable area. Above 800 m altitude, 2.7 million hectares were classified as favorable for the best combination of factors, i.e., sowing on February 5, ASWC of 75 mm, 105-day cycle, 20% risk level, and low and moderate impacts.

ABSTRACT The present study aimed to characterize the dynamics of NDVI and meteorological conditions, relating both to the annual dynamics of biomass accumulation in natural pastures of the Pampa biome as a way of subsidizing growth modeling. Forage accumulation rate data from a long-term experiment, NDVI data from the MODIS images, and meteorological data measured at the surface were used. We verify that the agrometeorological element associated to the accumulation of forage in the natural grasslands is different according to the season, which is typical of the subtropical climate. Winter is the critical season for livestock production due to the lower forage accumulation rate and lower values of NDVI, conditioned by the decrease of solar radiation and air temperature. In the summer, the limiting factor to forage accumulation is the hydric condition. It was also verified that the variability in the growth of grasslands can be associated with the ENSO phenomenon, being the El Niño favorable and the La Niña unfavorable, especially in the spring-summer period. Considering the verified associations, spectral indices combined with agrometeorological elements are recommended to the adjustment of models of forage accumulation in the Pampa biome natural grasslands.

ABSTRACT: Soybean crops occupy most areas in Rio Grande do Sul State and are highly dependent on rainfall since most of them are non-irrigated. Rainfall during the harvest period is often insufficient to meet the water demand, making water indicators an important tool for the crops. This study compared two approaches in the parameterization process of TVDI (Temperature-Vegetation Dryness Index) in a subtropical climate region of Brazil. The process used Moderate Resolution Imaging Spectroradiometer (MODIS) images of the surface temperature (TS) and Normalized Difference Vegetation Index (NDVI), with spatial resolutions of 1,000 m and periods of 8-16 d, respectively. The evaporative triangles for the TS/NDVI scatter plots were built either for each image (scene-specific parameterization) or for all images at once (crop-type parameterization). The rainfall data were obtained from meteorological stations located in the study site and the analysis period comprised two contrasting harvests regarding soybean yield (most important crop in the region). The scene-specific parameterization allowed to analyze water status in the study site by inspecting the wet and dry edge of each image and identifying the areas of stress in each one. TVDI crop parameterization showed that the model was able to determine the time and frequency of water stress events during the crop-seasons. TVDI crop-parameterization, therefore, is more consistent for crop monitoring and forecasting purposes.

Resumo: O objetivo deste trabalho foi a identificação das bandas espectrais, dos índices de vegetação e dos períodos do ciclo da canola em que a correlação entre os dados espectrais e os indicadores biofísicos (matéria seca total da parte aérea e rendimento de grãos) é mais significativa. Os experimentos foram conduzidos nas safras de 2013 e 2014, na Embrapa Trigo, no Estado do Rio Grande do Sul. Utilizou-se o delineamento experimental em blocos ao acaso, com quatro repetições, e os tratamentos foram cinco doses de nitrogênio em cobertura. Foram determinados a matéria seca das plantas, o rendimento de grãos e a fenologia. A resposta espectral da canola foi avaliada por medições de reflectância do dossel, com espectrorradiômetro, e, a partir desses dados, foram calculados os índices de vegetação SR, NDVI, EVI, SAVI e GNDVI. As correlações de Pearson entre as variáveis espectrais e biofísicas da canola mostraram que as melhores bandas para estimativa da matéria seca são as do vermelho (620 a 670 nm) e do infravermelho próximo (841 a 876 nm). O período vegetativo é o mais indicado para obtenção de correlações mais significativas para a canola. Todos os índices de vegetação utilizados são adequados para estimativas da matéria seca e do rendimento de grãos da canola.

Abstract: The objective of this work was to identify the spectral bands, vegetation indices, and periods of the canola crop season in which the correlation between spectral data and biophysical indicators (total shoot dry matter and grain yield) is most significant. The experiment was carried out during the 2013 and 2014 crop seasons at Embrapa Trigo, in the state of Rio Grande do Sul, Brazil. A randomized complete block design was used, with four replicates, and the treatments consisted of five doses of nitrogen topdressing. Plant dry matter, grain yield, and phenology were measured. The canola spectral response was evaluated by measuring the canola canopy reflectance using a spectroradiometer, and, with this data, the SR, NDVI, EVI, SAVI, and GNDVI vegetation indices were determined. Pearson’s correlations between the spectral and biophysical variables of canola showed that the red (620 to 670 nm) and near-infrared (841 to 876 nm) bands were the best to estimate the dry matter. The vegetative period is the most indicated to obtain the most significant correlations for canola. All the used vegetation indices are adequate for estimating the dry matter and grain yield of canola.

RESUMO As técnicas de classificação, como a análise de agrupamento, utilizadas nas áreas biológicas, em chaves de classificação de plantas e animais, também são úteis para definição de regiões homogêneas, seja para dados climáticos ou para identificação ou monitoramento da vegetação. O objetivo desse trabalho foi caracterizar a dinâmica espaço-temporal do Índice de Vegetação por Diferença Normalizada (NDVI) dos grandes grupos vegetais do Rio Grande do Sul, agrupar regiões homogêneas com base na variabilidade temporal do NDVI e analisar o padrão de variabilidade anual do NDVI dos diferentes grupos. Para tanto, foram utilizados dados de NDVI da série Global Inventory Modeling and Mapping Studies (GIMMS), derivadas do Advanced Very High Resolution Radiometer / National Oceanic and Atmospheric Administration (AVHRR/NOAA) do período de janeiro de 1982 a dezembro de 2008. Foram definidos seis grupos, os quais foram comparados ao macrozoneamento ambiental do Estado. Os resultados mostraram que a variabilidade de NDVI, obtida da série GIMMS/AVHRR, permite agrupar regiões homogêneas, de forma a identificar e mapear os principais grupos vegetais que ocorrem no estado do Rio Grande do Sul. Os grupos são caracterizados por um padrão sazonal típico, porém, com variações de amplitude de NDVI entre eles, o que possibilita monitorar as variações fenológicas da vegetação em decorrência da sua dinâmica temporal.

ABSTRACT The classification techniques, such as cluster analysis, used in biological areas, in the plants and animals classification keys, are also useful for defining homogeneous regions, for climate data or for identification or monitoring of vegetation. The objective of the research was to characterize the spatiotemporal dynamic of NDVI (Normalized Vegetation Index Difference) of vegetation groups of Rio Grande do Sul, clustering homogeneous regions based on NDVI temporal variability and to analyze the pattern of NDVI annual variability of the different groups. For this NDVI data from the Global Inventory Modeling and Mapping Studies (GIMMS) series, derived from Advanced Very High Resolution Radiometer / National Oceanic and Atmospheric Administration (AVHRR/NOAA) over the period from January 1982 to December 2008 were used. Six groups were defined, which were compared to environmental macrozoning. The results showed that the variability of NDVI, obtained from GIMMS/AVHRR series, allows clustering homogeneous regions in order to identify and map the main vegetation groups that occur in the Rio Grande do Sul State. Vegetation groups are characterized by a typical seasonal pattern, however, with amplitude variations of NDVI between them, which allow to monitor phenological changes in vegetation due to its temporal dynamics.

RESUMO O índice de vegetação por diferença normalizada (NDVI), obtido por sensoriamento remoto, tem sido amplamente empregado no monitoramento de culturas agrícolas produtoras de grãos, porém poucos são os estudos em fruticultura. O objetivo deste trabalho foi caracterizar a evolução temporal do NDVI obtido por sensor remoto de superfície ao longo do ciclo de videiras em sistemas horizontais de condução do dossel vegetativo. Dados de NDVI foram obtidos com sensor remoto Greenseeker em vinhedos na região da Serra Gaúcha, Rio Grande do Sul, Brasil, de setembro a junho nas safras 2014/2015 e 2015/2016. Os vinhedos das cultivares Chardonnay e Cabernet Sauvignon eram conduzidos em sistema horizontal (latada e lira). Os resultados indicaram que houve variabilidade temporal do NDVI ao longo do ciclo (de 0,33 a 0,85), a qual refletiu as alterações no acúmulo de biomassa e vigor vegetativo decorrentes das principais etapas fenológicas e práticas de manejo. A evolução temporal do NDVI foi semelhante nos sistemas latada e lira, ambos caracterizados pela condução horizontal do dossel vegetativo. Os valores de NDVI para ‘Cabernet Sauvignon’ foram superiores aos de ‘Chardonnay’ ao longo do ciclo, independe da safra avaliada e do sistema de condução, indicando ‘Cabernet Sauvignon’ como a cultivar de maior vigor vegetativo. O NDVI, obtido por sensor remoto de superfície, é uma forma rápida e não destrutiva de monitoramento e caracterização do dossel vegetativo em tempo real, compilando em uma única informação o desenvolvimento da videira, o qual é resultado de diversos fatores, edafo-climáticos e de manejo, dificilmente quantificados conjuntamente.

ABSTRACT The normalized difference vegetation index (NDVI) obtained by remote sensing is widely used to monitor annual crops but few studies have investigated its use in perennial fruit crops. The aim of this study was to determine the temporal NDVI profile during grapevine cycle in vineyards established in horizontal training systems. NDVI data were obtained by the ground-based remote sensing Greenseeker in Chardonnay and Cabernet Sauvignon vineyards located in the Serra Gaúcha region, Rio Grande do Sul, Brazil, from September to June in the 2014/2015 and 2015/2016 vegetative seasons. The grapevine canopies were managed in horizontal training systems (T-trellis and Y-trellis). The results indicated that the temporal NDVI values varied during the grapevine cycle (0.33 to 0.85), reflecting the changing in vigor and biomass accumulation that resulted from the phenological stages and management practices. The temporal NDVI profiles were similar to both horizontal training systems. The NDVI values were higher throughout the cycle for Cabernet Sauvignon compared to Chardonnay indicating Cabernet Sauvignon as the cultivar with greater vegetative vigor. The NDVI obtained by ground-based remote sensing is a fast and non-destructive tool to monitor and characterize the canopy in real time, compiling into a single data several parameters related to vine development, like meteorological conditions and management practices that are difficult to be quantified together.

ABSTRACT The most southern Brazilian state, Rio Grande do Sul, is characterized by high crop yield and is, currently, the third leading soybean producer in the country. Therefore, agriculture is very important to the economy of the region. Because agriculture is highly dependent on variable weather parameters, the present study aimed to test the Temperature-Vegetation Dryness Index (TVDI) as a regional indicator of water status under the climate and soybean crop management conditions that predominate in northwestern Rio Grande do Sul. For this, soybean crop seasons with contrasting yields were selected: 2004 – 2005 (yield 0.5 t∙ha–1) and 2009 – 2010 (2.7 t∙ha–1). TVDI was obtained from the surface temperature (TS) and Normalized Difference Vegetation Index (NDVI) images available from MODIS products, which were correlated to obtain a triangular scatter plot. Rainfall data from surface weather stations were also used. The results showed that the pattern of the TVDI is associated with rainfall variability. However, as the TVDI is based on normalization of the wet and dry edges of each image individually, the comparison of TVDI values across different images is challenging. This deficiency can be mitigated by analysis of the parameters used to derive the index. Therefore, both the TVDI and the “b” and TSmin parameters used to derive it can elucidate the patterns of crop response to water availability.