Orientadores: Rubens Augusto Camargo Lamparelli, Jansle Vieira Rocha / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Agricola / Made available in DSpace on 2018-08-12T21:23:17Z (GMT). No. of bitstreams: 1 Ramme_FernandoLuizProchnow_D.pdf: 9393591 bytes, checksum: a6d5183861f5be0ab0ed23ba7a8838da (MD5) Previous issue date: 2008 / Resumo: O objetivo do trabalho foi avaliar a relação entre as fases do crescimento da cana-de-açúcar com as formas de curvas do perfil temporal do Índice de Vegetação por Diferença Normalizada - NDVI, obtidas a partir do sensor remoto orbital MODerate-resolution Imaging Spectroradiometer - MODIS, na região de estudo. A avaliação desta relação é realizada utilizando-se técnicas de sensoriamento remoto para a geração do perfil temporal do NDVI, ao longo do ciclo de desenvolvimento fenológico da cana-soca, nas maturações Precoce, Média e Tardia. Os talhões de cana-soca analisados foram agrupados de acordo com a variedade, solo, data de plantio e corte, e contigüidade. A visualização gráfica das formas de curvas analisadas é realizada através de aplicativo, desenvolvido neste trabalho na linguagem de programação Java, e do sistema gerenciador de banco de dados PostgreSQL. O aplicativo realiza a filtragem de ruídos presentes nas imagens, composição na resolução temporal de 8 dias, através dos dados da banda de controle de qualidade do produto MOD09Q1, realiza a eliminação de valores discrepantes ao longo do perfil temporal do NDVI para a safra analisada, corrige as influências dos períodos de corte e rebrota da cana-soca, e propicia a suavização da forma de curva através do filtro Savitzky-Golay. Três janelas temporais de monitoramento da cultura são apresentadas neste trabalho. Cada janela temporal é determinada em função do tipo de maturação da cultura, do coeficiente de cultura (Kc) ao longo do ciclo fenológico da cana-soca e do comportamento na evolução do perfil temporal do NDVI. Concluiu-se que na região de estudo, diferentes maturações são caracterizadas por diferentes formas de curvas do perfil temporal do NDVI / Abstract: The objective of the work was to evaluate the relationship among the phases of the growth of the sugarcane with the forms of curves of the Normalized Difference Vegetation Index - NDVI temporal profile, obtained from remote sensor orbital MODerate-resolution Imaging Spectroradiometer - MODIS, in the study area. The evaluation of this relationship is accomplished by using of the techniques of remote sensing to generate the NDVI profile, along the phenological development phase of stubble-cane, in the Carly, Medium and Late maturations. The fields of stubble-cane analyzed were contained in agreement with the variety, soil, planting date and cut, and proximity. The graphic visualization of curves shape analyzed is accomplished through application, developed in this work in the Java programming language, and of the PostgreSQL system database manager. The application accomplishes the filtering of present noises in the images, composition in the temporal resolution of 8 days, through the data of the band of quality control of the MOD09Q1 product, accomplishes the elimination of outliers along the NDVI temporal profile for the culture analyzed, corrects the influences of the cut periods and regrowth of the stubble-cane, and propitiates the smoothing in the curve shape through the filter Savitzky-Golay. Three temporal windows of culture monitoring are presented in this work. Each temporal window is determined in function of the type of crop maturation, of the culture coefficient (Kc) along the phenological development phase of stubble-cane and of the behavior in the evolution of the NDVI profile. It concluded that in the study area, different maturations are characterized by different forms of NDVI profile curves / Doutorado / Planejamento e Desenvolvimento Rural Sustentável / Doutor em Engenharia Agrícola

Agriculture and food security are interlinked. New technologies and instruments are making the agricultural system easy to operate and increasing the food production. Remote sensing technology is widely used as a non-destructive method for crop growth monitoring, climate analysis, and forecasting crop yield. The objectives of this study are to (1) monitor crop growth remotely, (2) identify climate impacts on crop yield, and (3) forecasting crop yield. This study proposed methods to improve crop growth monitoring and yield predictions by using remote sensing technology. In this study, we developed crop vegetative growth metrics (VGM) from the MODIS (Moderate Resolution Imaging Spectroradiometer) 250m NDVI (Normalized Difference Vegetation Index) and EVI (Enhanced Vegetation Index) data. We developed 19 NDVI and EVI based VGM metrics for soybean crop from a time series of 2000 to 2018, but the methods are applicable to other crops as well. We found VGMmax, VGM70, VGM85, VGM98T are about 95% crop yield predictable. However, these metrics are independent of climatic events. We modelled the climatic impacts on soybean crop from the time series data from1980-2019 collected from NOAA's National Climatic Data Center (NCDC). Therefore, we estimated the impacts of increase and decrease of temperature (maximum, mean, and minimum) and precipitation (average) pattern on crop yields which will be helpful to monitor climate change impacts on crop production. Lastly, we made crop yield forecasting statistical model across different climatic regions in USA using Google Earth Engine. We used remotely sensed MODIS Terra surface reflectance 8-day global 250m data to calculate VGM metrics (e.g. VGM70, VGM85, VGM98T, VGM120, VGMmean, and VGMmax), MODIS Terra land surface temperature and Emissivity 8-Day data for average day-time and night-time temperature and CHIRPS (Climate Hazards Group Infra-red Precipitation with station data) data for precipitation, from a time series data of 2000-2019. Our predicted models showed a NMPE (Normalized Mean Prediction error) with in a range of -0.002 to 0.007. These models will be helpful to get an overall estimate of crop production and aid in national agricultural strategic planning. Overall, this study will benefit farmers, researchers, and management system of U.S. Department of Agriculture (USDA).